Total Contributed Projects
Timestamp: 2-22-2022 19:15:12
Email Address: thanmaikurasala1@gmail.com
Name of The Student as Per SSC: Kurasala Thanmai Naga Deeksha
Regd Number: 20A21F0033
Project Title: Farmer's E-Market
Project Abstract (only): ABSTRACT For several years, farmers in India have had little liberty in choosing markets and purchasers for their produce. All states in the country, except three, degree that marketing and selling of farm produce must be directed through state-owned mandis, retail markets where mediators (middlemen) crush farmers to increase margins. According to research, mediators have become dominating buyers of the agricultural market, resulting them to take control over the plight of the farmers and gulping all the profits. The farmers work day and night expecting a good yield. They use a lot of financial resources lending money and buying fertilizers, seeds etc. So, they have the right to enjoy every rupee gained on their corp. In this context, we propose a system which brings farmers close to the retailers cutting the middlemen. The middlemen usually take up to 70% of the profits of farmers leaving them helpless. Our system consists of a mobile or web application which will serve as a platform for farmer the growers and retailers or customers to sell and buy their farm products. This system aims at giving a profitable price to farmers to their farm products cutting the middlemen. This allows the retailers or the customers to buy products from the farmers at a lower than the normal price. This system is used to farmer and user. Farmer uploads their product with details and buyers view these details and book that product with in a time.
Project Documentation: 20A21F0033 - Thanmai Kurasala.pdf
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1I0pJ5Yr-P3czyxUg74YSC4YxX9lkuLtp
Timestamp: 2-22-2022 20:39:59
Email Address: deepumouni26@gmail.com
Name of The Student as Per SSC: Thotakura Mounika
Regd Number: 20A21F0059
Project Title: Tourism management system
Project Abstract (only): Most of the people in this world like to travel from one place to another no matter whether it is a small or large distance. The need for a tourism management system that can manage tourism information with ease is sought after by every tour management company. Tour Management system is a dynamic website for tourism business. This travel and tourism application is designed for travel agencies by which they can manage different tour packages based on the destinations. The main purpose is to help tourism companies to manage tour packages .To provide user friendly environment to overcome the drawbacks of exiting system. This project “TOURISM MANAGEMENT SYSTEM” is used to automate all process of the travel and tourism, which deals with creation, booking and confirmation and user details. The project is designed HTML-PHP as front end and Microsoft SQL Server 2008 as backend which works in any browsers.
Project Documentation: https://drive.google.com/open?id=1n2IdI5UwFkimW82iEpsNCGzffuYSSDsm
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1VOg952rdxRiGfmr0P077Z72AAOXJ6Cx2
Timestamp: 2-22-2022 22:13:53
Email Address: narendramedicharla@gmail.com
Name of The Student as Per SSC: Medicharla narendra sai kumar
Regd Number: 20A21F0039
Project Title: Ifsc code finder
Project Abstract (only): The purpose of ‘IFSC Code Finder Portal’ is to automate existing manual system by the help of computerized equipments and full-fledge computer software, fulfilling their requirements, so that their valuable data/information can be stored for a long period with easy accessing and manipulation of the same. The required software and hardware are easily available and easy to work with. ‘IFSC Code Finder Portal’ can lead to error free, secure, reliable and fast management system. It assist the user to concentrate on their other activities rather concentrate on the record keeping. Thus it will help organization in better utilization of resources. The organization can maintain computerized records without redundant entries. That means that one need not be distracted by information that not relevant, while being able to reach the information. The aim to automate its existing manual system by the help of computerized equipments and full-fledge computer software, fulfilling their requirements, so that their valuable data/information can be stored for a long period with easy accessing and manipulation of the same. Basically the project describes how to manage for good performance and better services for the clients.
Project Documentation: https://drive.google.com/open?id=11MQhLy_VzeBdfQD-OUZzWACOzyz35Vbq
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=17K4yKsIQa8BZz0rBpkPHBJzZXyybFFqZ
Timestamp: 2-23-2022 22:04:27
Email Address: meegadavanaja@gmail.com
Name of The Student as Per SSC: Meegada.Vanaja
Regd Number: 20A21F0040
Project Title: Online College Admission Management System
Project Abstract (only): yes
Project Documentation: https://drive.google.com/open?id=1TC_na91VV6T3vo278rH5oGGGYaYcCYSM
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1yLHrYj81oKqshD7nY8P6HVuLtq1_6VVI
Timestamp: 2-24-2022 15:24:05
Email Address: kanusha4304@gmail.com
Name of The Student as Per SSC: Kommoju Anusha
Regd Number: 20A21F0028
Project Title: Bus reservation system
Project Abstract (only): Online bus reservation system is a web based application that works within a centralized network. This project presents a review on the software program online bus reservation system as should be used in a bus transportation system, a facility which is used to reserve seats, cancellation of reservation and different types of route enquiries used on securing quick reservations
Project Documentation: https://drive.google.com/open?id=1XpkTwlFQfCzkKHd30QhiJ0xhRaQz7Sbs
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1mvKTEbBpSjoxwK9d8Ijku_9VxM8Hc0rf
Timestamp: 2-24-2022 19:10:17
Email Address: goluguribhagyasri@gmail.com
Name of The Student as Per SSC: GoluguriBhagyasri
Regd Number: 20A21F0018
Project Title: Apartment visitors Management System
Project Abstract (only): This paper present the Automated Visitors Management System A visitor management system tracks the usage of a public building or site. By gathering increasing amounts of information, a visitor management system can record the usage of the facilities by specific visitors and provide documentation of visitor’s whereabouts. Because a visitor management system provides a record of building use, these systems are frequently used to complement building security systems and access control systems. As electronic visitor management systems become more common and more powerful, these systems are taking over many of the functions of building security and access control.
Project Documentation: https://drive.google.com/open?id=1BcTzRf6DRgLuK9sKyiXvds0o8nHQBg5O
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1tF-qosrVdXJqULUUC4kmR6ke3zBPl60_
Timestamp: 2-24-2022 19:34:28
Email Address: navyak131@gmail.com
Name of The Student as Per SSC: Kotikalapudi Navya Sri
Regd Number: 20A21F0032
Project Title: Online Examination System
Project Abstract (only): “Online Examination System” is a software solution, which allows a particular company or institute to arrange, conduct and manage examinations via an online environment. This can be done through the Internet, Intranet and or Local Area Network environments. Some of the problems faced by manual examination systems are delays in result processing, filing poses a problem, filtering of records is not easy, The chance of loss of records is high and also record searching is difficult. Maintenance of the system is also very difficult and takes lot of time and effort. I intend to use the systems development life cycle (SDLC) which is a conceptual model used in project management that describes the stages involved in an information system development project.
Project Documentation: https://drive.google.com/open?id=14odltIzBr_lb4FIvIMF_Skea1Ep8fn3u
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1zA835VMKZ_AqeY_5GhxzW3qC0sWt_g7q
Timestamp: 2-24-2022 22:25:01
Email Address: dharanisatya123@gmail.com
Name of The Student as Per SSC: Dirisala.Dharani satya
Regd Number: 20A21F0013
Project Title: Vehicle Parking Management System
Project Abstract (only): This project helps us parking now a days vehicle parking has become major problem in many areas like malls, theatres, public places. It even effects traffic in major areas. Due to increasing population in urban areas, there is an exponential rise in the number of vehicles which is leading to major problems leading to poor traffic management and congestion. Another major problem faced by the vehicle owners is the availability of parking space. The idea of parking system is used to decrease the parking issues.
Project Documentation: https://drive.google.com/open?id=1vpTyMJ2ITloq2klSAbIdbAFNGtpGHL2B
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1MR3vYGBDl-VFQbYPPBA2-ofRSpKfIAlm
Timestamp: 2-24-2022 22:37:06
Email Address: deepikaadabala1997@gmail.com
Name of The Student as Per SSC: MUTYALA.DEEPIKA
Regd Number: 20A21F0042
Project Title: GROCERY ORDERING SYSTEM PROJECT
Project Abstract (only): This is a web based application that provides an online platform for the grocery store/shop's customers or possible customers to order their desired product. The main purpose of the simple project is to let the customers buy their groceries without going to the shop or store. The system will list all products with available stock and customer can save their desired product to their shopping cart and checkout when they are done. The system is easy to use and has a simple user-interface.
Project Documentation: https://drive.google.com/open?id=1DQ7KME5okGGNNvTyzV0xbkuijtlfcHjG
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1wHeyUGCc6yGRdEEBlPnPYmed-rP3u6U5
Timestamp: 2-24-2022 22:53:51
Email Address: anudeepika127@gmail.com
Name of The Student as Per SSC: PATAMSETTI ANUDEEPIKA
Regd Number: 20A21F0046
Project Title: Online Event Management System
Project Abstract (only): ABSTRACT Online event management system is a software project that serves the functionality of an event manager. The system allows only registered users to login and new users are allowed to register on the application.. The project provides most of the basic functionality required for an event. It allows the user to select from a list of event types. Once the user enters an event type eg(Marriage, Dance Show etc), the system then allows the user to select the date and time of event, place , event equipment, parking details and the food details(menu). All this data is logged and stored in the database and the user is given a receipt number for his booking if the time and place of the function hall was acceptable which means that both the organizer and the admin were accepts the user request. This data is then sent to the administrator (website owner) and they may interact with the client as per his requirements and his contact data stored in the database. And the user can also give feedback about the event. It improve your workflow processes and customer engagement simultaneously with connected data, automatic updates, tasks, rule assignments, and much more. Keywords:Functionality, equipment, receipt, administrator, requirements, database, feedback, engagement, automatic, assignment
Project Documentation: https://drive.google.com/open?id=1s6-R27uYlgkbnc8YYa3f3mhfI6LX6tV7
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1F8F4QJZk07mrfpc_DK7IiLvwqyxIksVT
Timestamp: 2-25-2022 8:46:05
Email Address: sivanineelamss@gmail.com
Name of The Student as Per SSC: Neelam Baby sivani
Regd Number: 20A21F0045
Project Title: Hotel management system
Project Abstract (only): This application is specifically developed to help hotel staff. The project hotel management manages and maintains the records of customers and room in the hotel. This rooms have different categories such as delux, semi delux etc... So their charges nd records will be maintained accordingly..
Project Documentation: https://drive.google.com/open?id=17hC5-Z8hrEaTSqAr0-OiSDnz_-2ud1AX
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1f5sYhqMuFVfJ0i07LG8kQvu8azpYQJRn
Timestamp: 2-25-2022 14:00:52
Email Address: subhadraangara@gmail.com
Name of The Student as Per SSC: Angara Subhadra Devi
Regd Number: 20A21F0004
Project Title: Online votting system
Project Abstract (only): ABSTRACT The Project is developed for the threat free and user oriented Online Voting System. The Online Voting system is made for the people of the country residing around the world and wants to vote for their representative. The election can be conducted in two ways the paper ballot election and the automated ballot elections. The proposed online voting system with biometric authentication is an electronic voting system which seeks to make use of the uniqueness of the minutiae of the human fingerprint to further enhance the level of trust and confidentiality of the voters in the system as well as making the actual process as universally accessible as possible which would be achieved through the deployment on the Internet. It is expected to solve the two critical issues facing staff elections conducted within the University of Ibadan community which serves as the project case study. For the voter registration and authentication processes which are performed on the desktop module, the voter is expected to have his or her fingerprints captured and the minutiae extracted that is stored on the database. This is done to prevent the occurrence of multiple registrations or identity. The project was able to achieve a high success rate in the use for conducting elections as it was able to stamp multiple registrations by voters through the combined use of both the unique voter identification number and their unique fingerprints. This effectively solved all questions that may arise on eligibility of voters and accreditation hiccups. Voters can thus proceed to the online module of the project to cast their votes through any internet – connected device using the voter identification number, security answer keyed in during the registration process as well as a token key that was generated automatically for each voter per election on the online module.
Project Documentation: https://drive.google.com/open?id=10KybiFvB5csily8DZsoFYNE1OaoqVgBX
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1RcDavyFQg195pqUGgB6qjL1FXPb_Jq4K
Timestamp: 2-25-2022 19:15:31
Email Address: bhavanarajulapudi005@gmail.com
Name of The Student as Per SSC: Rajulapudi Bhavana
Regd Number: 20A21F0054
Project Title: Blood bank management system
Project Abstract (only): This paper is focused on Blood Bank Management System which is a web application with supporting mobile application aimed to serve as a communication tool between patients (who need blood) and blood donor. To become members of the system, donors need to create their profiles by providing fundamental information like name, blood group, email address, password, and exact location from “Google Map”. In order to find out the exact location of a donor, Google Map is integrated with this application. Blood donors can also be searched from the mobile application, but this is only accessible for registered members. The goal of this paper is to reduce the complexity of the system to find blood donors in an emergency situation
Project Documentation: https://drive.google.com/open?id=1N6B2DFiI_v0RupdUwnf9DVCvdcAITep-
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1psFCNi-TLMmYm0NB0wAVCfQ7TCqRHIqJ
Timestamp: 2-25-2022 19:23:26
Email Address: saibellamkonda3006@gmail.com
Name of The Student as Per SSC: Lakshmi Narasimha Sai Bellamkonda
Regd Number: 20A21F0005
Project Title: VACCINATION MANAGEMENT SYSTEM
Project Abstract (only): Nowadays we cohabit with technology in an inseparable way. With the aim to facilitate and automate application have brought improvement in many different fields and disciplines. A particular field that requires technology’s intervention is vaccination in Albania, which is the most important and crucial component of populations’ health. The e-Vacc is a web application which is used for managing and organizing the vaccination process data. This application will be used by every individual in Albania that receives vaccination, and by the vaccination service providers which are the employees.
Project Documentation: https://drive.google.com/open?id=1tweiSo3Mu7-hDMmDSXlQJTOS8w1dgXgn
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=11RC3vOe-k-UrfeecbsHH6yWHr64A4onN
Timestamp: 2-25-2022 19:47:27
Email Address: ravikumarpeddireddy47@gmail.com
Name of The Student as Per SSC: PEDDIREDDY RAVIKUMAR
Regd Number: 20A21F0047
Project Title: Face Identification System
Project Abstract (only): Criminal record generally contains personal information about particular person along with photograph. To identify any Criminal we need some identification regarding person, which are given by eyewitness. In most cases the quality and resolution of the recorded image segments is poor and hard to identify a face. To overcome this sort of problem we are developing software. Identification can be done in many ways like finger print, eyes, DNA etc. One of the applications is face identification. The face is our primary focus of attention in social inters course playing a major role in conveying identify and emotion. Although the ability to infer intelligence or character from facial appearance is suspect, the human ability to recognize face is remarkable.
Project Documentation: https://drive.google.com/open?id=1wekxT_uhySxcn_f93QjVSawHD9Qw5RqP
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=13WTMRUIdB93wYRkbAyjfechLveRRMc-K
Timestamp: 2-25-2022 20:35:49
Email Address: kesarichandana99@gmail.com
Name of The Student as Per SSC: Kesari.chandana naga sai
Regd Number: 20A21F0025
Project Title: Online job portal
Project Abstract (only): ABSTRACT In this competitive era, the education among the people is so increasing that the jobs for them are now decreasing. The companies even want the people who are best in their fields. At that time, it becomes difficult to find the people who are intelligent enough to be hired. The work for the companies also increases to find the people who can fulfill their requirements. Thinking about these problems, one can think about the process which can handle this process and make the work less complex. This project is about the recruitment process which is done online. The recruitment process here is handled by the system. This project will allow the person to apply for a job in the company for the interested vacancy which would be available at the company. The person will be having the account after registration and will be then called the applied user. If he would be qualified, he would be interacting with the system for the updates. The project is created for fulfilling the requests of the company managers so that the recruitment module can be placed in the company’s website and the users who visit the website can view the vacancies in the company and will be able to apply directly from remote place even. The vacancies will be posted by the administrator on the basis of needs of the manpower in the company. T
Project Documentation: https://drive.google.com/open?id=1kNXq6ZZMb383Sa_hIJqrIXqs_VUQDqYF
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1l07gWpHBqUKqVv784YSWWEHoYfoN5Fes
Timestamp: 2-25-2022 20:41:49
Email Address: veeravallisuryateja@gmail.com
Name of The Student as Per SSC: Veeravalli Rama Surya Teja
Regd Number: 20A21F0064
Project Title: Online Electricity Billing System
Project Abstract (only): This project deals with the design of "Online Electricity Billing System", in which it is possible pay amount electronically. This approach is implemented via virtual bank in which the process of money transfer can be implemented. In the project " Online Electricity Billing System"is an application to automate the process of ordering and calculating the electricity bill with all the charges and penalty for a consumer who has been given connection of electricity. Paper bills are now the primary channel of communication between companies and their customers.However, their potential for personalization is limited, and they are not interactive. An electric bill is a bill for the consumption of electric energy.The user is incapable of paying the bill before month end, it would calculate fine for each subsequent day.
Project Documentation: https://drive.google.com/open?id=1Y5NmmvE_GR0d8Bl5aE1WgAbMtURcc3wo
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1lR8LVFGRLN5Mlii8l-WAlpmKA-NRHgLb
Timestamp: 2-25-2022 21:03:55
Email Address: madusaivanitha10@gmail.com
Name of The Student as Per SSC: Madu Saivanita
Regd Number: 20A21F0035
Project Title: Employee Leave Management System
Project Abstract (only): The purpose of this project to create an online leave management program which is essential to somthing like an organization or college.The employee leave information Mangement system was designed to automate the employee administration and management and strategic planning of leavs for employees.This program manages all of the employee records.
Project Documentation: https://drive.google.com/open?id=1WXhyH-1Byr3Yi3vXqFfJBxbEoeC6hi0M
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1lOhRSUVNCTGnAb9225RvLeygx8NieYfY
Timestamp: 2-25-2022 22:06:12
Email Address: tejapothuraju777@gmail.com
Name of The Student as Per SSC: POTHURAJU SAI VENKATA TEJA
Regd Number: 20A21F0051
Project Title: Online Banking System
Project Abstract (only): This project is aimed at developing an Online Banking for customer. The system is an online application that can be accessed throughout the organization and outside as well with proper login provided.The project has been planned to be having the view of distributed architecture, with centralized storage of the database. The application for the storage of the data has been planned. Using the constructs of MYSQL and all the user interfaces have been designed using the HTML with Javascript. The database connectivity is planned using the “Database” methodology. The main aim of Online Banking System is to manage customers, and their transactions, and it is specifically developed for balance enquiry, Fund transfer to another account in the same bank, and changing the address , getting the last transcations (viewing month wise) and also update their profile through online and also provide the comfort to both bank and customers.The entire project has been developed keeping in view of the distributed client server computing technology, in mind. The specification has been normalized up to 3NF to eliminate all the anomalies that may arise due to the database transaction that are executed by the general users and the organizational administration. The user interfaces are browser specific to give distributed accessibility for the overall system. The internal database has been selected as MYSQL.The basic constructs of table spaces, clusters and indexes have been exploited to provide higher consistency and reliability for the data storage. The MYSQL was a choice as it provides the constructs of high-level reliability and security. The total front end was dominated using the HTML 5. At all proper levels high care was taken to check that the system manages the data consistency with proper business rules or validations. The database connectivity was planned using the latest “ Database connection” technology provided by MYSQL. The authentication and authorization was crosschecked at all the relevant stages.
Project Documentation: https://drive.google.com/open?id=1D5fD66Wij-EixSGGPReOeBNqY6DPEPXs
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1wOO9_Gn0qsqGufpPb-0yF1YETtf1A7mT
Timestamp: 2-25-2022 22:33:45
Email Address: gollavaralakshmi123@gmail.com
Name of The Student as Per SSC: vara lakshmi golla
Regd Number: 20A21F0017
Project Title: CAR RENTAL SYSTEM
Project Abstract (only): This Car Rental System project is designed to aid the car rental company to enable renting of cars through an online system. It helps the users to search for a available cars view profile and book the cars for the time period it has a user friendly interface which helps the user to check for cars and rent them for the period specified. They could also make payment online the rental cars shall be categorized into economy, premium etc. Based on the type of car required by the customer, the user shall be able to make bookings. The use of internet technology has made it easy for the customers to rent a car any time. This Car Rental System makes the bookings easy. Its saves time and labor. The tool shall ask the user for information such as the date and time of journey, type of car etc. Also, it will need an information number Using these details, the tool shall help the customer to book a car for the journey.
Project Documentation: https://drive.google.com/open?id=1XpchXBTBC0ugTTqT2VXRqBz6ki4HZJI_
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1VHq0G7Xtdv_EGiDRsXY6DilBCLyJ4uFm
Timestamp: 2-26-2022 0:38:37
Email Address: bhavani.bsc999@gmail.com
Name of The Student as Per SSC: MAMIDISETTI GANGA BHAVANI
Regd Number: 20A21F0037
Project Title: Online Food Ordering System
Project Abstract (only): ABSTRACT The project titled Online Food Ordering System is designed with visual studio as from and SQL server as back end. There will be different items available in restaurant, many customers will be coming at different time for having food, they will be eating different items available in the restaurant. Restaurant owner has to maintain records of each sales and purchase to keep track of the availability of stock of any materials which is used for cooking. This system will save time and will be easy to use when compared to manual work which will be done in papers. Modern handheld devices such as smart as smart phones and PDAs have become increasingly powerful in recent years. Dramatic breakthroughs in processing power along with the number of extra feature included in these device have opened the doors to a wide range of commercial possibilities. In particular, most cell phones regular include cameras, processor comparable to PCs from only a few years ago, and internet access. However, even with all these added abilities, there are few applications that allow much passing of the environmental information and location based services.
Project Documentation: https://drive.google.com/open?id=1mKINmaoQyCOmu4MP_lFFptambsjTQZcU
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1Yq3UvvCIN4595HqOnljAub15U089JE_7
Timestamp: 2-26-2022 8:38:39
Email Address: girinaudus1234@gmail.com
Name of The Student as Per SSC: Varada giri venkata Sai pavan kumar
Regd Number: 20A21F0062
Project Title: Sanitization management system
Project Abstract (only): “Sanitization Management System” is a web based application which is developed in PHP and MySQL server. Nowadays it is very important to sanitize and disinfect each every thing. The main purpose of this system is to provide sanitization services to clean and disinfect of envoronment. The main purpose of “Sanitization Management System” is to systematically record, store and update recorded data.
Project Documentation: https://drive.google.com/open?id=1EwzvlbmGUatdU_snKSrQPQWWsGJ6RSln
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1VuLBEwKE44CBe1vwtd6ifYiDHpVFppkZ
Timestamp: 2-26-2022 9:54:06
Email Address: saidurgaravi999@gmail.com
Name of The Student as Per SSC: R.Sai Venkata Mani Durga
Regd Number: 20A21F0053
Project Title: Employee payroll application
Project Abstract (only): Employee Payroll Application has been designed to for the purpose of maintaining details of various allowances and deductions that need to be given to the employees of the organization. Also, it generates the salary Sheet of the employees of the organization that assists the accounts department in many ways. Employee Payroll is a web application which is used to maintain the Employee details of the organization or concern in different places; this provides each employee details with unique information. Admin plays the main roll to maintain the activities of the employees, this system is user friendly GUI based application that will automate the day to day activities of the employees who are working in the concern. An application for completely automating the different activities of the various departments or sections of the employees is processed.
Project Documentation: https://drive.google.com/open?id=1pQ9PpcfariMRV7PDg_6H4XtP2uO3VLDp
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1r6_qQXzAFq8LBrQb2G76IcEtPaQPKm1l
Timestamp: 2-26-2022 13:59:05
Email Address: saisurendrakondaparthi143@gmail.com
Name of The Student as Per SSC: KONDAPARTHI SAI SURENDRA BABU
Regd Number: 20A21F0029
Project Title: Movers and packers management system
Project Abstract (only): The packers and movers company uses the brst quality packing materials to pack goods in such away that all goods remain in safesafe condition during transist&moving services assure the safe delivary of goods at destination.
Project Documentation: https://drive.google.com/open?id=1Y7rM1ogM3uNspKwy_6GOimvtlm7V8nFQ
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1kwRHHt_RlP60Opd5QQvlU8LArqTdw_dN
Timestamp: 2-26-2022 15:08:42
Email Address: kavyaaddagalla17@gmail.com
Name of The Student as Per SSC: Addagalla Sri kavya
Regd Number: 20A21F0001
Project Title: PHARMACY MANAGEMENT SYSTEM
Project Abstract (only): One of the most important responsibilities of pharmacy management is to supervise and manage the pharmacy employees in order to ensure healthy working relationships and outcomes. Each of these functions is critical to the pharmacy’s operation and should be explained by the management. However, most pharmacies faced problems such as insufficient service promotions, lack of coherence of pharmacy services in hospitals, poor drug information systems and the inconsistency of the pharmacy information management due to its manual processes. Now these are the problems that must be solved with this Pharmacy Management System Project Proposal.
Project Documentation: https://drive.google.com/open?id=1axBNKHEhE1mn1qozuHreT3Tp6xDg4qZh
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1D3l1zsK2yqLwLm66-jeWFTDIHIQRpZiI
Timestamp: 2-26-2022 15:55:45
Email Address: srideviakana@gmail.com
Name of The Student as Per SSC: AKANA SRIDURGADEVI
Regd Number: 20A21F0003
Project Title: Online crime reporting system
Project Abstract (only): ABSTRACT The main objective of the online crime reporting system is to manage the details of crime, criminal, public, solutions, department. It manages all the information about crime complaint, department, crime. The project is totally built at administrative end and thus only the administrator is guaranteed the access. The purpose of the project is to build and application program to reduce the manual work for managing the crime, criminal, complaint, public. It tracks all the details about the public, solutions, department. The aim is to automate its existing manual system by the help of computerized equipments and full-fledged computer software, fulfilling their requirements, so that their valuable data/information can be stored for a longer period with easy accessing and manipulation of the same. Basically the project describes how to manage for good performance and better services for the clients.
Project Documentation: https://drive.google.com/open?id=1U11kKyodIFLz5-hdSVrzRK2L9yCTWJmH
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1sPohVhJqpTp-oE2bbbQYj5IZwaiycCQe
Timestamp: 2-26-2022 15:57:00
Email Address: revathimaddala123@gmail.com
Name of The Student as Per SSC: REVATHI MADDALA
Regd Number: 20A21F0034
Project Title: DEVELOPMENT OF A MOBILE WORKOUT APPLICATION
Project Abstract (only): The challenge of maintaining proper diet can be facilitated by the use of mobile phones.Mobile phones provide a fair infrastructure, which can be used to provide cost effective, high quality aids to behaviour monitoring and modiication. The nature of mobile phonesallows the user for customization and personalization, retrieval of nutrition informationon demand, as well as the ability to truly monitor the user’s consumption trends. Thisandroid application is a one stop solution for all health related issues and question. It hasvarious functions like diet tracking, nutritional information about food’s, Bmi calculator, information about some basic medicines.
Project Documentation: https://drive.google.com/open?id=18LHWlmqD0YWoCrgbwXsTX3C--cL-naZ0
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1Uxag_A3d_jVEBUgBMBuL7PPV3PwZ5qkz
Timestamp: 2-26-2022 16:06:36
Email Address: ganeshgangula038@gmail.com
Name of The Student as Per SSC: GANGULA GANESH
Regd Number: 20A21F0014
Project Title: OPTICAL CHARACTER RECOGNITION
Project Abstract (only): ABSTRACT Now-a-days there is a huge demand in “storing the information available in the paper documents in to a computer storage disk and then later reusing this information by searching process”. One simple way to store information in these paper documents in to computer system is to first scan the documents. Whenever we scan the documents through the scanner, the documents are stored as images format in the computer system. These images containing text cannot be edited by the user. But to reuse this information it is very difficult to read the individual contents and searching the contents form these documents line-by-line and word?by-word. Thus there is a need of character recognition mechanisms to perform Document Image Analysis (DIA) where the text from these documents in image format is recognized and transformed into machine readable text-data. Language is the only way to communicate with people of different regions. If you are planning to expand your business in different regions, you need to communicate with staff, partners and also with the clients. At that time, language translation is the best way to understand people about your business and services. This project displays a menu where the user tend to choose requirement of his own. It facilitates extraction of text from images, writing text to images, translation of text to speech or text of specified language
Project Documentation: https://drive.google.com/open?id=1J4UUSeSQTwe8oIXdATanCA-oNY_EFPPW
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1sIx8ruH0ID03C6Lj5TNrYTml92jTsUFu
Timestamp: 2-26-2022 16:18:14
Email Address: himamanishamutyala@gmail.com
Name of The Student as Per SSC: Mutyala Hima manisha komalatha
Regd Number: 20A21F0043
Project Title: Directory management system
Project Abstract (only): The main objective of this project (DMS) is to store the data of the desired persons in a database and to automate the complete operation of the directory which meets all the requirements. It is a easy to use interface developed in PHP with MySQL as the backend to store the details. This application stores all the details like name, telephone number, address, email id in a database. This software also allows editing, updating and searching various contact details. It is secure, easy to use and reliable software system. It also provides a good level of security as there is an admin who can only edit and update details.
Project Documentation: https://drive.google.com/open?id=1D2AM9d40EPebdwVj3pNbsv2XPnDtzSTo
Screenshots of project Execution: https://drive.google.com/open?id=161kEnxKnUEzQVexXf9cInkyRgjQrJoZE
Executable Project code: https://drive.google.com/open?id=1d9jIB14vk3F0CQU-FPTVqjOQNtBRg3gQ
Timestamp: 2-26-2022 18:23:25
Email Address: svknbmahalingam@gmail.com
Name of The Student as Per SSC: SAMATHAM VEERA KOTA NAGA BALA MAHALINGAM
Regd Number: 20A21F0055
Project Title: ONLINE ATTENDANCE MANAGEMENT SYSTEM
Project Abstract (only): The project definition name describes many ways, Student attendance management system, Attendance portal, Online attendance System, School attendance system, College attendance system etc.
Project Documentation: https://drive.google.com/open?id=1TXfCRkqTRC-KRWk5LcLDpy_F8qFKkcfX
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1uTFc7NVjJZ4I1XSlqejVHWGXj0c4BA8a
Timestamp: 2-26-2022 18:25:16
Email Address: durgaprasad170890@gmail.com
Name of The Student as Per SSC: KANDREGULA DURGAPRASAD
Regd Number: 20A21F0023
Project Title: HOSTEL MANAGEMENT INFORMATION SYSTEM
Project Abstract (only): This Project "HOSTEL MANAGEMENT INFORMATION SYSTEM" targeted for the College Hostel integrates the transaction management of the Hostel for better control and timely response. This eliminates time delay and paper transactions being marked. The warden is provided with a better control over the transactions like adding the details of new students in the hostel, modifying the details of the students, deleting the students, viewing the students details in the Hostel. This project's main motto is to reduce the effort of Wardens and provide better service to the students. The goal of this project is to develop a system for the computerization of the Hostel. The common transactions of the hostel Includes the maintenance of mess bills, information about students in the hostel, enrolling of new students and their payments and dues etc are stored into the databases and reports are generated according to the user requirements.
Project Documentation: https://drive.google.com/open?id=1tctpu6NUqpqBn9cggDRhd9AQJufY2fdm
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1uL3zmfwlkCo_oPrbt5lDmKLUIppvq_me
Timestamp: 2-26-2022 19:10:16
Email Address: sureshgoka053@gmail.com
Name of The Student as Per SSC: GOKA RAJA SURESH
Regd Number: 20A21F0016
Project Title: INTERVIEW MANAGEMENT SYSTEM
Project Abstract (only): The interview is an important data gathering technique involving verbal communication between the researcher and the subject. Interviews are commonly used in survey designs and in exploratory and descriptive studies.
Project Documentation: https://drive.google.com/open?id=17tbIbm9IS9YkI26ky8mR5V9jgkMFjhbD
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1czyu56VVWXkZKPwJzdIXs1sKrvO0mq9_
Timestamp: 2-26-2022 19:27:29
Email Address: vrajasribsc@gmail.com
Name of The Student as Per SSC: Vasukuri. Rajasri
Regd Number: 20A21F0063
Project Title: Online pizza store
Project Abstract (only): The objective and scope of my Project Pizza Ordering System is to record the details various activities of user. It will simplifies the task and reduce the paper work. During implementation every user will be given appropriate training to suit their specific needs. Specific support will also be provided at key points within the academic calendar. Training will be provided on a timely basis, and you will be trained as the new is Pizza Ordering System rolled out to your area of responsibility
Project Documentation: https://drive.google.com/open?id=1O4gaDiPws5xRpLby4fpB3NBfErfX4urP
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1tAaygT318qFW7vM_9PP_-yNosw9Pt2sM
Timestamp: 2-26-2022 20:02:34
Email Address: degirlmarisetti@gmail.com
Name of The Student as Per SSC: Marisetti Sai Naga Venkata Durga Devi
Regd Number: 20A21F0038
Project Title: Cyber cafe management system
Project Abstract (only): Computer become away of life for today's high society.many aspects modern life that we have to accept as common place Would not be possible if there were no computers Today computers are used extensively in many areas of business The project title was "cyber cafe management system" of software package By using LAN connection in cyber cafe automatic these process very easily These are two modules . Server module . Cilent module
Project Documentation: https://drive.google.com/open?id=1v9grvd4nkwz_och95hSW4kTvWM9bgtQo
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1m4JkmRDlXmelNoGXxNl1-VqPysd97RYh
Timestamp: 2-26-2022 20:49:31
Email Address: jeevanrocky3023@gmail.com
Name of The Student as Per SSC: PERABATHULA JEEVANASAI
Regd Number: 20A21F0048
Project Title: Curfew E-pass Management system
Project Abstract (only): ABSTRACT The goal of the project "CURFEW E-PASS MANAGEMENT SYSTEM" is to develop a system that can be utilized during curfew to efficiently manage people's passes during Covid-19. A computer's internal memory can store data and instructions in an electronic form that may be retrieved at any moment. It makes the task easier to complete and decreases the amount of documentation required. Lockdown is intended to prevent the spread of infection, and it entails not leaving the house unless absolutely essential. However, under unusual circumstances, people may need to go from one town to another, and this e-pass generating mechanism will aid in people's contact-less transportation. The electronic version of the paper gate pass is available. Keywords: E-Pass, Curfew, End-To-End Application, Lockdown.
Project Documentation: https://drive.google.com/open?id=1mcTdDmyOls_Bn-ZyXAX448EtiB8ZeRYk
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=10gFsxte9WRGPpjWiBOygDZ0uvd6WmEDp
Timestamp: 2-26-2022 21:03:11
Email Address: kondetijhansi9@gmail.com
Name of The Student as Per SSC: Kondeti Jhansi
Regd Number: 20A21F0031
Project Title: Complaint management system
Project Abstract (only): It is a software developed for managing complaints
Project Documentation: https://drive.google.com/open?id=1iFTDTj3TonRaq8-5NoW0_zjpb4SlNH-E
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1sRFudM-9JIpPis4UZZxzd3tqAzNu0fgb
Timestamp: 2-26-2022 21:27:30
Email Address: geethachowdula@gmail.com
Name of The Student as Per SSC: Chowdula Geetha Lakshmi
Regd Number: 20A21F0011
Project Title: Beauty Parlour Management System
Project Abstract (only): Life style is fast changing in the modern era, and the women have become more conscious abou their makeup. More women are seen frequenting the beauty parlours for availing themselves of some kind of service to enhance their appearance to look good. The status of women has improved a lot and is improving further. This has resulted in their life style changing. Also their economic independence encourages them to resort to this type of service.
Project Documentation: https://drive.google.com/open?id=160uyZCxg89KbhYX3lpOM9K5-Vz-nGiwI
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1KxHJjsib860qEZVGu54Uog_OmiE5nJWL
Timestamp: 2-26-2022 21:36:12
Email Address: lavanyadevikodavati@gmail.com
Name of The Student as Per SSC: Kodavati Lavanya Devi
Regd Number: 20A21F0027
Project Title: Student result management system
Project Abstract (only): The main objective of “Student Result Management System” is to be developed software which manages student result activities in the college makes an interactive GUI where result admin can manage details of all student results. Although such project as very wide scope, this project contains the most important parts i.e. class_student, class_subject allocation and information relating to students for each subject has to be maintained separately.
Project Documentation: https://drive.google.com/open?id=194rWfXUhqPozFD4L0cwBPp0Y64PFdXUc
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1VUzGak6u8ARSqkQJpugOLIXDa6wqpl3K
Timestamp: 2-26-2022 21:43:45
Email Address: chelsysharon9@gmail.com
Name of The Student as Per SSC: s chelsy sharon
Regd Number: 20A21F0056
Project Title: online shopping management system
Project Abstract (only): The Online Shopping is a web application intended for online retailers. The main objective of this application is to make it interactive and its ease of use. It would make searching, viewing and selection of a product easier. It contains a sophisticated search engine for user to search for products specific to their needs. The search engine provides an easy and convenient way to search for products where a user can Search for a product interactively and the search engine would refine the products available based on the user’s input. The user can then view the complete specification of each product
Project Documentation: https://drive.google.com/open?id=1PrhzFZZT7h-jWXpo-6xAbr0XinYrW6M0
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1zSSIIxdowh-7weCFKBkXxZoiRp6afVxC
Timestamp: 2-26-2022 22:14:11
Email Address: kondavetidevi03@gmail.com
Name of The Student as Per SSC: Kondaveti Naga Satya Devi Sri
Regd Number: 20A21F0030
Project Title: Hospital Management System
Project Abstract (only): OurprojectHospitalManagement system includes registration of patients, storing their details into the system, and also computerized billing in the pharmacy. Our software has the facility to give a unique id for every patient and stores the details of every patient and the staff automatically. It includes a search facility to know the current status of each room. User can search availability of a doctor and the details of a patient using the id. The Hospital Management system can be entered using username and password. It is accessible either by an administrator or receptionist. Only they can add data into the database. The data can be retrieved easily. The interface is very user-friendly. The data are well protected for personal use and makes the data processing very fast.
Project Documentation: https://drive.google.com/open?id=1g8I46gYXIVnkRQxca4c9pvB_RXAgvEg-
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1ffakunyRToEyFzodpPXRullBE7Yb--RJ
Timestamp: 2-26-2022 22:23:47
Email Address: lathah339@gmail.com
Name of The Student as Per SSC: G.HEMALATHA
Regd Number: 20A21F0061
Project Title: TEACHER RECORD MANAGEMENT
Project Abstract (only): Teacher ManagementDr. Radhika KapurAbstractProgressions can be brought about in the overall System of education, when the concept of teacher management is identified in an appropriate Manner. Teachers are the ones, who are impart knowledge and information to the students in such a Manner and ensure that their growth and development takes place in an operative manner and they are able to achievetheir academic goals and objectives.
Project Documentation: https://drive.google.com/open?id=18Gs15k16oGyBf0bjc7gYLHKCPRPsT-xf
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1bIhcK45Q44mEhem_0mBeEMLM--ceDyRC
Timestamp: 2-26-2022 22:31:55
Email Address: geethikachinni163@gmail.com
Name of The Student as Per SSC: Purilla Geethika Tulasi Durga
Regd Number: 20A21F0052
Project Title: Dairy Farm shop management system
Project Abstract (only): This website Milk Dairy Management System is built such a way that it should suits for all type of Milk Distributors in future. So every effort is taken to implement this project in this Milk Distributing Office, on successful implementation in this Dairy Distributors Office, we can target other Milk Distributors in the city. This project is developed using PHP and MySQL. This website helps in controlling all the dairies in and around the area to prevent from extra dairy production. And the admin can easily control over all the dairies. The main objective of this website is to automate the complete operations of the Dairy Distributors office and bring ease between each dairy managers and the system admin. They need to maintain hundreds of thousands of records. Also searching should be very faster so they can find required details instantly
Project Documentation: https://drive.google.com/open?id=1xD1s_LJVXdjYX34WbMONcTcqrfFXuWM_
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1vm3uY61ZaoGYjk_eXlGfbPVWtXW7dF_B
Timestamp: 2-26-2022 23:05:44
Email Address: navyamente@gmail.com
Name of The Student as Per SSC: Mente.Navya
Regd Number: 20A21F0041
Project Title: Online course registration system
Project Abstract (only): Abstract The online course registration system is the central part of the educational administration system, which consists of registration guidance, registration controlling, undergraduate course registration, graduate course registration, retaking and retesting, dropping the course in the middle phase and information exchange, etc. By registering the course voluntarily, the new system improved registration mechanism, implemented course registration of common course for undergraduate and graduate students, and also supported the teaching activities across spring, summer and fall semesters. This article introduces the design and implementation of the new online course registration system, including registration mechanism, technical architecture, and system design, etc. There are a number of online registration systems that have been made in different context and to suit the need arising. Researched on the online course registration for the faculty of engineering and in their research they generated a solution named Online Registration. The project was divided into three namely, Online Registration which is the start-up project of their system, and depending on data Access and web security. data Access would actuate the data transaction between the client and the server and Web Security would configure the database and handle the authentication and authorization. The research described their experience in designing, developing and deploying an online course registrati0n. The system has not only reduced the burden of all parties involved in the course registration process, but also improved the process by reducing errors. Though their system managed to reduce the errors significantly it lacked the continued interaction with the student in the updates of the student’s registration status updates. It also lacked accountability as some stakeholders in the registration process do not have access to the system.
Project Documentation: https://drive.google.com/open?id=12fe5_Hqozv2kLDbwvPljRae7yXoNV2WF
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1AHzVSXS-FrrSgwm6ucKNv5IqYu3J1vjn
Timestamp: 2-27-2022 9:18:57
Email Address: namyathadasari@gmail.com
Name of The Student as Per SSC: Dasari Namyatha
Regd Number: 20A21F0012
Project Title: Client management system
Project Abstract (only): Client management system is an established concept. Which is used to manage client life cycle through various technologies and process oriented tools. This study identifies how web application can be utilized for improved client management and to understand the potential of web application in the business environment.
Project Documentation: https://drive.google.com/open?id=1I5jc7rzXfETsAUqjOq9GhcLdWZ8VZFBs
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=15OFKVGR1dg7vFQyj74p4JVX8XRVyfCsE
Timestamp: 2-27-2022 9:26:48
Email Address: satyaprakash.tholeti@gmail.com
Name of The Student as Per SSC: THOLETI LAHARI SATYA PRAKASH
Regd Number: 20A21F0058
Project Title: Courier Management System
Project Abstract (only): It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Project Documentation: https://drive.google.com/open?id=1Dc9bOglOabPLCZyeSXaTZGCL1zi4qi1W
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1RZTOrbd4nRTffv0ZNl4ZpOqF5u2E1Ga_
Timestamp: 2-27-2022 9:43:13
Email Address: yamuna.thumma07@gmail.com
Name of The Student as Per SSC: THUMMA BALA YAMUNA
Regd Number: 20A21F0060
Project Title: Employee Record Management System
Project Abstract (only): Employee Management System is a distributed application, developed to maintain the details of employees working in any organization. It maintains the information about the personal details of their employees, also the details about the payroll system which enable to generate the payslip. The application is actually a suite of applications developed using php. It is simple to understand and can be used by anyone who is not even familiar with simple employees system. It is user friendly and just asks the user to follow step by step operations by giving him few options. It is fast and can perform many operations of a company
Project Documentation: https://drive.google.com/open?id=1RaPYCJ5ViGSBJ6JlxWUT3Xn-78k0ZDla
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1uDEa8BWXJz5uM9-VLGCdjYePOO4BR7fn
Timestamp: 2-27-2022 11:29:27
Email Address: nlsrjyothi@gmail.com
Name of The Student as Per SSC: NAGIREDDY LAKSHMI SAI RAMA JYOTHI
Regd Number: 20A21F0044
Project Title: Online Art Gallery
Project Abstract (only): Online Art gallery project in php exhibits the work s of many artist. Each artist is given a specified space on the online webpage. Any user can register and login to the website and can buy those art works.
Project Documentation: https://drive.google.com/open?id=1BZA7MWtnNfQqa_AYxrhxn_FDmjbEL7jO
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1MRSCXc-c7h8EnaiAffbGaB05Wmoc9KmT
Timestamp: 2-27-2022 11:52:39
Email Address: bhavanabobbili123@gmail.com
Name of The Student as Per SSC: BOBBILI BHAVANA
Regd Number: 20A21F0006
Project Title: Covid-19 testing management system
Project Abstract (only): Nowadays covid-19 testing management system is one of the most essential tools that are mostly used in testing lab; it is mostly used to manage covid-10 medical lab related activities.
Project Documentation: https://drive.google.com/open?id=1IEgE9TfXQHLDpuR_sclFvd3OXnNzGlaS
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1U4fjshMGdAm8FWOBREI35pk9w6rnDneH
Timestamp: 2-27-2022 12:11:38
Email Address: tejakasani146@gmail.com
Name of The Student as Per SSC: kasani Teja Gowd
Regd Number: 20A21F0024
Project Title: car washing management system
Project Abstract (only): Car Washing Management System is fully automated with different stages of foaming, washing, drying and brushing. In Car Washing System, we performed all the operations needed to clean the car successfully by using highly expert and experience worker
Project Documentation: https://drive.google.com/open?id=1pGcNTjB3N9hh7xlrvmgh5rFAtSEnJ_vo
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1aQZDreyT-rpMw1-5aTwAkkWggbOAqKTF
Timestamp: 2-27-2022 13:14:09
Email Address: chamakuridurgajyothi@gmail.com
Name of The Student as Per SSC: CHAMAKURI DURGA JYOTHI
Regd Number: 20A21F0008
Project Title: RAILWAY RESERVATION SYSTEM
Project Abstract (only): This project deals with reserving tickets for the trains which reserve date.So in order to make is convenient to travel we can book tickets priority.This makes the travel smooth and convenient.This also helps in checking the details afterwards.
Project Documentation: https://drive.google.com/open?id=1SwfqVDkJ-SnU7r3SKz-gxqHZGW2owOXQ
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1zjEBBYm0aS-Fy3DYZ8QwwwLqPCFZUU_i
Timestamp: 2-27-2022 13:44:06
Email Address: jnaneswaridevi2000@gmail.com
Name of The Student as Per SSC: PERABATHULA JNANESWARI DEVI
Regd Number: 20A21F0049
Project Title: Library management system
Project Abstract (only): A college library management is a project that manages and stores books information electronically according to students needs. The system helps both students and library manager to keep a constant track of all the books available in the library. It allows both the admin and the student to search for the desired book. It becomes necessary for colleges to keep a continuous check on the books issued and returned and even calculate fine. This task if carried out manually will be tedious and includes chances of mistakes. These errors are avoided by allowing the system to keep track of information such as issue date, last date to return the book and even fine information and thus there is no need to keep manual track of this information which thereby avoids chances of mistakes. Thus this system reduces manual work to a great extent allows smooth flow of library activities by removing chances of errors in the details.
Project Documentation: https://drive.google.com/open?id=1s7e5sPX1QIONQf5BWkUnKfcKBJXGBczD
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1t4tFINKX62z_y-XjrtEl-LL9j2gCTy97
Timestamp: 2-27-2022 13:45:15
Email Address: kesarijhansi@gmail.com
Name of The Student as Per SSC: KESARI JHANSI
Regd Number: 20A21F0026
Project Title: News portal
Project Abstract (only): Today the world totally relays upon the electronic media to its every day venture. People have no time to be updated through newspaper or watching or listening the news on television or radios. People today need to be updated on daily basis in this competitive world. Most of the people get the information about the world around through the internet which is fast, accessible, and reliable. The WWW (World Wide Web) is huge, widely distributed, global information service centre for Information services news, advertisements, consumer information, financial management, education, government, e-commerce etc, hyper-link information, acces and usage information. “24 Hours News Portal” is
Project Documentation: https://drive.google.com/open?id=1mUcA_AF3QZaIgKAa3hoOvOPnePQhtJP6
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=10lFiJ6cUvq0jTliEpk7tggSPfs8sbWuL
Timestamp: 2-27-2022 15:33:06
Email Address: vickysai252@gmail.com
Name of The Student as Per SSC: majji.purna sairam
Regd Number: 20A21F0036
Project Title: zoo management system
Project Abstract (only): This project manages people, animals details and provides ticket to the person who comes to visits in zoo with his/her family. With this project admin is able to see how many people is visiting in zoo and also see how many ticket is generating in particular period.
Project Documentation: https://drive.google.com/open?id=1GMAArHwhXJmv1hEEaVApbSlGxZa4hx2B
Screenshots of project Execution:
Executable Project code: https://drive.google.com/open?id=1GEEefEC8neeOoM5OpPxWTuxtv0BOMzBU
Timestamp: 2-27-2022 17:51:49
Email Address: rojach1512@gmail.com
Name of The Student as Per SSC: CHAMAKURI ROJESWARI
Regd Number: 20A21F0009
Project Title: Student Management System
Project Abstract (only): Student management systems serve academic institutions in a variety of ways, the most important of which is centralized data administration and accessibility. Teachers will be able to input, maintain, and access student data more simply. Parents and guardians have a better understanding of how their children perform in class. Teacher and other school or college professionals must enhance their student management skills over time. It is defined as a collection of skills that every teacher develops through time in order to properly manage their students. Getting into the student’s heart is one of the most important aspects of management.
Project Documentation: https://drive.google.com/open?id=1XGdZJYcAjN2eHG25ueKftzKgq4jt8UGn
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Executable Project code: https://drive.google.com/open?id=1gEKDhXbIxj0tNRuANM6rLOSwnQga0SqG
Timestamp: 2-27-2022 19:17:41
Email Address: durgaprasdpippalla2000@gmail.com
Name of The Student as Per SSC: Pippalla Durga Prasad
Regd Number: 20A21F0050
Project Title: Men Salon Management System
Project Abstract (only): A saloon management system is a website that manages the appointment scheduling functionality. This system connects users and Salon in an online platform where user can browse salon and their services. Users can also write and read reviews of the salon and its management. Salon management system helps the industry to fill this void in such a way that is on-demand, easy to use, and effective. The final product will be a functioning web application that can handle use cases like appointment scheduling, writing, and reading review for specific salon.
Project Documentation: https://drive.google.com/open?id=1adQSicHNlSykI2nyXI9VkB2iYLWwWrX9
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Executable Project code: https://drive.google.com/open?id=1ipLHkA_9NiBxyZRw_WTDEv8p3yHUAmLT
Timestamp: 2-27-2022 20:06:08
Email Address: saiganeshjarugu1999@gmail.com
Name of The Student as Per SSC: J sai ganesh
Regd Number: 20A21F0020
Project Title: DJBOOKING MANAGEMENT SYSYTEM
Project Abstract (only): A Disk Jockey (DJ) plays musical recordings in parties or events. Roles of these professionals vary depending on the purpose or the setting of the music. They may work for radio stations where they play musical selections from playlists. The Online DJ Booking System makes a DJ more profitable as they can be reached in the web. This system is programmed using php and mysql is the database used.
Project Documentation: https://drive.google.com/open?id=1umwwYjyXGPxGxYAOsX6B2gLWzq5ipeZZ
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Executable Project code: https://drive.google.com/open?id=1kuuNBLNyFxyHi5r1zgxP3_ATqHfUCKZI
Timestamp: 2-27-2022 20:24:33
Email Address: maheshkallakuri66@gmail.com
Name of The Student as Per SSC: KALLAKURI MAHESH
Regd Number: 20A21F0021
Project Title: Online Birth Certificate System
Project Abstract (only): Online Birth Certificate System maintains a good record of date of birth of people. This system helps admin to view data of date of birth of people who reside in country. The main objective of “Online Birth Certificate System” project is to providing easier registration of date of birth and gets certificate of birth online which save lots of time
Project Documentation: https://drive.google.com/open?id=1B76IResowY1XHYwZwEDkLwIucm4daxJD
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Executable Project code: https://drive.google.com/open?id=1JvYEoRzv6JMSNib9ZTJ3uPM_An7jmoMn
Timestamp: 03-02-2022 19:10
Email Address: 840balaji@gmail.com
Name of The Student as Per SSC: bodasingu venkata balaji
Regd Number: 20A21F0007
Project Title: lacal service search engine management system
Project Abstract (only): Local Services Search Engine Management System (LSSEMS) is a web based application which helps user to find serviceman in a local area such as maid, tuition teacher, plumber etc. LSSEMS contain data of serviceman (maid, tuition teacher, plumber etc.). The main purpose of LSSEMS is to systematically record, store and update the serviceman records.
Project Documentation: https://drive.google.com/open?id=1A9vK5EAF47TKMJezYpGY2sc5a3aUEEeC
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Executable Project code: https://drive.google.com/open?id=1lUoKDhZsgrZkaqlhwLfl0uWDMDIkCSBy
Timestamp: 03-03-2022 18:19
Email Address: maheshsunkara609@gmail.com
Name of The Student as Per SSC: Sunkara mahesh babu
Regd Number: 20A21F0057
Project Title: Exam hall seating arrangement management
Project Abstract (only): The main agenda of the paper is to lessen the mammoth task of manually allocating seats during an examination. The tool provides an effective measure to dynamically allocate students in a classroom. This research can be further extended to seating planning in conferences, weddings, movie theaters etc. It is an organized system which enables us to automatically allocate students to their desired location. For people handling institutions, the work load is very high and the need for faster work is a need of an hour. There is a general complaint that government offices have surplus work load but the speed of efficiency is very low. It is a type of software which can decrease our work time. Those Institutions which uses this software can save a sample amount of time during the examination time. Some of the few benefits of this model are that it is very fast, reliable and robust. In today’s world, it is the tool for event management which is extremely useful of various occasions. This much needed feature of user friendliness is present in this model and can be used for all types of user whether Agile, Naïve or expert.
Project Documentation: https://drive.google.com/open?id=1oUuhEd02I5MXzcGGFmcGaHImvBiS5Vuc
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Executable Project code: https://drive.google.com/open?id=1xRUWlOXfp9Z3boTecJCdBkQ_usOya18d
Timestamp: 02-08-2023 15:37
Email Address: devivatturi51@gmail.com
Name of The Student as Per SSC: VATTURI DEVI
Regd Number: 21A21F0062
Project Title: Analysis of performance on machine learning algorithms in detection of flowers
Project Abstract (only): The detection of various types of flowers based on their characteristics, is very useful in many fields of agriculture and medical research. Random Forest, Decision Tree and CNN algorithms are applied in this project to the identification of flowers on the basis of their characteristics. This algorithms are applied in a data set of flowers and their precision is calculated. Algorithms are implemented on a data collection in the Python programming language. It is found that the performance of CNN algorithm is best in detection of flowers.
Project Documentation: https://drive.google.com/open?id=11aprSjdzqUwCyKTT5CJo__cF2WvOEwGu
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Timestamp: 01-08-2023 13:04
Email Address: pavanimamidipalli49@gmail.com
Name of The Student as Per SSC: Mamidipalli Pavani
Regd Number: 21A21F0035
Project Title: Comparative Study To Identify the Heart Disease Using Machine Learning Algorithms
Project Abstract (only): Nowadays, heart disease is a common and frequently present disease in the human body and it’s also hunted lots of humans from this world. Doctor and clinical research said that heart disease is not a suddenly happen disease it’s the cause of continuing irregular lifestyle and different body’s activity for a long period after then it’s appeared in sudden with symptoms. After appearing those symptoms people seek for a treat in hospital for taken different test and therapy but these are a little expensive. So, I developed a machine learning model to predict weather a patient has heart disease or not.
Project Documentation: https://drive.google.com/open?id=1EqTpCG6GYH1ZmTiMP5EVndZw59_9-AKI
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Timestamp: 01-08-2023 12:33
Email Address: srinivasungarala283@gmail.com
Name of The Student as Per SSC: UNGARALA VISWESWARASRINIVAS
Regd Number: 21A21F0066
Project Title: Analysis of A Mixed Neural Network Based on CNN and RNN for Computational Model of Sensory Cortex
Project Abstract (only): Under the limitation of modern science, Anatomy is not able to discover the extremely complicated organ - brain’s working activities. Along with the development of machine learning and its subset - neural network, named by nearly implementing neurons connection, scientists found an efficient way to represent brain as a visible approach. Therefore, a new discipline has been created under biology, named as computational neuroscience. Most scientists focus on finding computational models that are closed to fit the specific or general areas of the cortex. Convolutional neural network (CNN) and Recurrent neural network (RNN) are two of them.
Project Documentation: https://drive.google.com/open?id=1k4mqi4uJW1RBK2S0PS8_ZS9IwunmbsKd
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Timestamp: 01-08-2023 15:09
Email Address: kotikalapudipavani123@gmail.com
Name of The Student as Per SSC: kotikalapudi Pavani
Regd Number: 21A21F0029
Project Title: A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques
Project Abstract (only): During the pandemic, there has been a strong upward momentum in the supply of online jobs posted on various trading portals. Therefore, a fictitious assignment to a job posting can be a huge problem for everyone. Thus, these fake jobs can be detected and properly distinguished from the pool of real and fake job hobby advertisements using advanced deep mastering algorithms and automated field type. This model proposed using several statistical mining strategies and algorithms along with KNN, preference tree, naive Bayes classifier, random forest classifier, SVM and deep neural society to wait for the submitted project to decide whether it is real or Fake. They experimented with EMSCAD with 18, 000 employee samples. We've used 3 dense layers for this Deep Neural network. An expert classifier suggests up to 98% accuracy for predicting a fraudulent e-book assignment.
Project Documentation: https://drive.google.com/open?id=1UJ4617gvPoWn3Q3NJbt9inClHsutnOis
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Timestamp: 01-08-2023 14:20
Email Address: anushaakula82@gmail.com
Name of The Student as Per SSC: Akula Anusha
Regd Number: 21A21F0001
Project Title: Image Captioning using Convolutional Neural Networks and Recurrent Neural Network
Project Abstract (only): Image Caption is a concept of gathering the right description of the given image on the internet use Computer Vision and natural language processing. We modeled automatic image caption using convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to provide a series of text that best describes the image.
Project Documentation: https://drive.google.com/open?id=1c8pbHyV8vpH6fEuD7tWYVQdI3jf1KW_h
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Timestamp: 02-08-2023 13:58
Email Address: borrapanchakshari@gmail.com
Name of The Student as Per SSC: Borra Panchakshari
Regd Number: 21A21F0006
Project Title: Hybrid Classification Algorithm For Predicting Student Performance
Project Abstract (only): Data mining is the development of instinctive discovery of valuable information from a huge repository. In educational record mining, prediction of a student's overall educational performance is a well-known study. The purpose of study boards is to show class algorithms as a hybrid type. We used feature network algorithms based on Radial, Layered Perceptron, C4Five, and Class Random Forest Areas. At first, grace accuracy was calculated by yourself using nice algorithms. The combination of the radial basis function, multi-layer realization, C4.5, and the odd- numberedness of the Random Forest algorithm gave an accuracy of 72, 9167%, 75, 4167%, 75%, and 73%. . One hundred twenty-five percent, and vice versa. To increase the accuracy of the type rule set, the ray-based feature grid is carefully layered. This set of instructions provides a type accuracy of 75.625%. We then combine the C4.Five ruleset with the Random Forest ruleset, to give an accuracy of type 76, 4583%. In this test, we found that a mixed class rule set provides more accuracy than a single class rule set.
Project Documentation: https://drive.google.com/open?id=1LezQ-MQeKXxlmWVfglg0p_GgzRGRfg3N
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Timestamp: 01-08-2023 15:39
Email Address: hareesh sivakoti
Name of The Student as Per SSC: Sivakoti hareesh
Regd Number: 21A21F0059
Project Title: Prediction Probability of Getting an Admission into a University using Machine Learning
Project Abstract (only): This study analyzes activities to create a model that can help college students choose topranked institutions based on their scores and profiles. We can test applicants in all disciplines, including Master of Science (International), Master of Technology (India) and Master of Business Administration (India and worldwide). We plan to build a device study model to provide results that can help students choose the right college. The dataset contains records about the university and academic profiles and a field indicating successful admission. Key performance indicators are used to evaluate predictions made with Ensemble Machine Learning using algorithm diffusion. Thissuggestedsystem uses linear regression (LR), random forest (RF), CatBoostClassifier algorithms but CatBoostClassifieralgorithm is giving highest accuracy
Project Documentation: https://drive.google.com/open?id=1L40PpnIjJSNnbjcBsH9USnbX-z4pqoXE
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Timestamp: 01-08-2023 12:27
Email Address: vasantha lakshmi kunapareddy
Name of The Student as Per SSC: KUNAPAREDDY VASANTHA LAKSHMI
Regd Number: 21A21F0030
Project Title: Comparative Analysis of Machine Learning Algorithm to Forecast Indian Stock Market.
Project Abstract (only): In this system, we used five different machine learning algorithms. Those are Decision Tree, Random Forest, Gradient Boosted Trees, Support Vector Machine and Linear Regression.These algorithms were applied on the BSE index data from 1st January 2015 to 31st March 2020. Amongst all the models applied, Linear Regression is chosen to be the efficient one, as it has least relative error and standard deviation. Further Linear Regression is used to forecast the BSE SENSEX Closing prices.The Indian Stock Market is expected to experience significant growth over the next few years.
Project Documentation: https://drive.google.com/open?id=1g1xmsKw4IpX03J2OfVq-VPCLYHXq6vkF
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Timestamp: 01-08-2023 12:01
Email Address: adityadivvi77@gmail.com
Name of The Student as Per SSC: Divvi Satya Sai Aditya Prasad
Regd Number: 21A21F0013
Project Title: Driver Drowsiness Monitoring Using Convolutional Neural Networks
Project Abstract (only): We aim to implement an intelligent processing system that can reduce road accidents drastically. The major challenges faced by these methods are robustness to handle variation in human face and lightning conditions This approach enables us to identify driver's face characteristics like eye closure percentage, eye-mouth aspect ratios, blink rate, yawning, head movement, etc. This system will helps the driver to come out from the drowsy state by alarm sound.
Project Documentation: https://drive.google.com/open?id=14RLICRvKRC0iz3RvaocfSbLPgKcgmpZL
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Timestamp: 01-08-2023 12:19
Email Address: malluvalasaladevi@gmail.com
Name of The Student as Per SSC: M. Devi
Regd Number: 21A21F0034
Project Title: Prediction Of Attendance At Medical Appointments Based On Machine Learning
Project Abstract (only): A recurring problem in the area of public health is the high rate of patients who do not attend scheduled medical examinations and consultations. Absenteeism from previously scheduled appointments compromises the effectiveness of medical care and generates a series of problems in the public health system, such as long waiting lines for care, lack of resources to meet demand and high financial loss, by paying a professional who will be idle due to the absence of patients. This predictive approach may help in optimizing resource allocation, reducing no-show rates, and enhancing overall healthcare efficiency and patient satisfaction. Thus, this study aims to explore different models of machine learning in order to help predict whether the patient will attend the scheduled appointment or not
Project Documentation: https://drive.google.com/open?id=1xw9SZahMQCBtADqcBFdaeolPobK7MIKw
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Timestamp: 01-08-2023 12:26
Email Address: nanimoka198@gmail.com
Name of The Student as Per SSC: MOKA NANI
Regd Number: 21A21F0039
Project Title: Smart Online Voting System
Project Abstract (only): Our country, India is the largest democratic country in the world. So it is essential to make sure that the governing body is elected through a fair election. India has only offline voting system which is not effective and upto the mark as it requires large man force and it also requires more time to process and publish the results. Therefore, to be made effective, the system needs a change, which overcomes these disadvantages. The new method does not force the person's physical appearance to vote, which makes the things easier. This project focusses on a system where the user can vote remotely from anywhere using his/her computer or mobile phone and doesn’t require the voter to go to the polling station through two step authentication of face recognition and OTP system.
Project Documentation: https://drive.google.com/open?id=1TjdCu83cqHA7-sETe5hp2o-V7aeQajIF
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Timestamp: 01-08-2023 14:15
Email Address: Kavya Kancharla
Name of The Student as Per SSC: Kancharla.Kavya sri
Regd Number: 21A21F0024
Project Title: Rainfall prediction using linear regression model
Project Abstract (only): The rainfall rate is one of the essential phenomena in the weather system, which has a direct influence on the agriculture and biological sectors. This project aims to develop a linear regression model in order to predict the rate of precipitation (PRCP), i.e., rainfall rate, for Khartoum state. It is based on some weather parameters, such as temperature, wind speed, and dew point.
Project Documentation: https://drive.google.com/open?id=1PR4HdutRsiUcxJNVWJrbrHff44ZdhXoi
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Timestamp: 01-08-2023 12:03
Email Address: kodavarthikumari3@gmail.com
Name of The Student as Per SSC: Kodavarthi Kumari
Regd Number: 21A21F0025
Project Title: CLASSIFICATION AND PREDICTION OF SEVERITY OF INFLAMMATORY BOWEL DISEASE USING MACHINE LEARNING
Project Abstract (only): The status of Inflammatory Bowel Disease from vitamin D in children and adolescents. IBD is a phrase used to refer to gastrointestinal system swelling that is recurrent. It is observed that low vitamin D levels are linked with higher risk, particularly colon cancer in people with IBD.
Project Documentation: https://drive.google.com/open?id=1XWprxjaoyj6dhj8WJh0WEh4XXayymU-z
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Timestamp: 01-08-2023 17:03
Email Address: Balasatyaappaji99 Kurella
Name of The Student as Per SSC: Bala satya appaji kurella
Regd Number: 21A21F0032
Project Title: Intelligent crop recommendation system
Project Abstract (only): Crop recommendation systems for agriculture are based on various input parameters. It proposes a hybrid model recommending plants for South Indian states by considering multiple attributes, including soil type, rainfall, groundwater level, temperature, fertilizers, pesticides and weather.
Project Documentation: https://drive.google.com/open?id=1zS7lY6nv2DiFPL7lhngW7uyHgNOvM2ex
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Timestamp: 01-08-2023 11:58
Email Address: leelaneeharika@gmail.com
Name of The Student as Per SSC: Mutyala Leela Neeharika
Regd Number: 21A21F0040
Project Title: Student Grade prediction System Using Machine Learning
Project Abstract (only): This project predicts student grade on the basis of various parameters which plays an important role in an education system. Some of the parameters are taken into consideration like learner factor, learner engagement, learning strategies use, teacher experience and motivational beliefs etc. With the help of examination and evaluation of these parameters we can measure student learning outcome. Classification Algorithms like Decision Tree, Random Forest and Support Vector Machine can help us to classify student’s performance.
Project Documentation: https://drive.google.com/open?id=137snrOGuIaiU23xMbARbZj1RCnIY19qE
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Timestamp: 01-08-2023 15:11
Email Address: ramyanadimpalli29@gmail.com
Name of The Student as Per SSC: Nadimpalli Ramya surya lakshmi kumari
Regd Number: 21A21F0041
Project Title: Analysis of Doppler Collision Prediction using Supervised Machine Learning
Project Abstract (only): Navigation with the Indian Constellation is now progressing through the Indian Space Research Organization (ISRO). The satellite constellation consists of four geosynchronous satellites and three geostationary satellites. There are many elements that affect the navigation device. Doppler collimation is one such addition that ends up being detected by geostationary satellites. It occurs between a mix of IRNSS 1C-1G, 1C-1F, 1F-1G geostationary satellites. When the relative Doppler frequency of the satellites is much less than the bandwidth of the tracking code loop, the Doppler collision length (DC) is determined. The positional accuracy of the NavIC is low with the most direct modern accuracy.Due to the CC, the most influential geostationary satellite television of the computer pair is 1C-1G. For DC mitigation, DC prediction using system recognition algorithms can be very useful to improve positional accuracy. The parameters taken into account for the prediction are the relative Doppler, the duration of the computer, the satellite TV, the speed of the computer, the satellite TV, the repetition duration and the relative Doppler. For the prediction, 3 supervised system control algorithms are used, together with linear regression, Random Forest repressor and K-Nearest Neighbors (KNN). Among these three algorithms, the random regression predicted the Doppler collision in the forest area as it should.
Project Documentation: https://drive.google.com/open?id=1j-2RL3ljdla7rX9vLEhR0FIVb_mAlOGu
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Timestamp: 08-08-2023 17:13
Email Address: Durgaprasad Moka
Name of The Student as Per SSC: MOKA DURGA PRASAD
Regd Number: 21A21F0038
Project Title: The machine learning approach to predict the result of league of legends
Project Abstract (only): Abstract—Nowadays, the MOBA game is the game type with the most audiences and players around the world. Recently, the League of Legends has become an official sport as an e-sport among 37 events in the 2022 Asia Games held in Hangzhou. As the development in the e-sport, analytical skills are also involved in this field. The topic of this research is to use the machine learning approach to analyze the data of the League of Legends and make a prediction about the result of the game. In this research, the method of machine learning is applied to the dataset which records the first 10 minutes in diamond-ranked games. Several popular machine learning (AdaBoost, GradientBoost, RandomForest, ExtraTree, SVM, Naïve Bayes, KNN, LogisticRegression, and DecisionTree) are applied to test the performance by cross-validation. Then several algorithms that outperform others are selected to make a voting classifier to predict the game result. The accuracy of the voting classifier is
Project Documentation: https://drive.google.com/open?id=1rcjKTPL9vf2XAFUGFIMvYFanSGIS77pX
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Timestamp: 01-08-2023 12:58
Email Address: dhanasrichinimilli@gmail.com
Name of The Student as Per SSC: CHINIMILLI DHANA SRI
Regd Number: 21A21F0010
Project Title: STRESS DETECTION IN IT PROFESSIONALS BY IMAGE PROCESSING AND MACHINE LEARNING
Project Abstract (only): The main motive of our project is to detect stress in the IT professionals using vivid Machine learning and Image processing techniques. Our system is an upgraded version of the old stress detection systems which excluded the live detection and the personal counseling but this system comprises of live detection and periodic analysis of employees and detecting physical as well as mental stress levels in his/her by providing them with proper remedies for managing stress by providing survey form periodically. Our system mainly focuses on managing stress and making the working environment healthy and spontaneous for the employees and to get the best out of them during working hours.
Project Documentation: https://drive.google.com/open?id=1W0rZIsfnUqixoCk3PILY5ISYL33QyUI4
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Timestamp: 01-08-2023 12:52
Email Address: Sai Kiran Korakanchu
Name of The Student as Per SSC: Korakanchu Y N V Sai Kiran
Regd Number: 21A21F0028
Project Title: An Efficient Approach for Interpretation of Indian Sign Language Using Machine Learning
Project Abstract (only): Communicating with everyone through hand gestures is, of course, called sign language. It is the ideal language of communication among the deaf-mutes in this society. The Deaf-Mute Network recognizes positive everyday barriers to communicating with the people they meet. The World Health Organization's most up-to-date test report shows that a very large percentage of the world (about 360 million parents) have hearing problems, i.e., 5.3% of the world's population. This makes us want to invent an automated system that converts hand gestures into meaningful phrases and sentences. A convolutional neural network (CNN) is used on 24 American Sign Language hand gestures to simulate the comfort of verbal communication. Text mining algorithms is proposed and vice versa in this document.
Project Documentation: https://drive.google.com/open?id=11Pd-vfsPRLDQBh6UyYH3eXWiGl0lDxeB
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Timestamp: 01-08-2023 12:43
Email Address: Donga Pavani
Name of The Student as Per SSC: Pavani donga
Regd Number: 21A21F0015
Project Title: Prediction thyroid disease(hypothyroid) using feature selection and classification techniques
Project Abstract (only): Thyroid disease is one of the most common diseases among the female mass in Bangladesh. Hypothyroid is a common variation of thyroid disease. It is clearly visible that hypothyroid disease is mostly seen in female patients. Most people are not aware of that disease as a result of which, it is rapidly turning into a critical disease. There are two types of thyroid diseases namely 1. Hyperthyroid and 2.Hypothyroid. Here in this project, we have attempted to predict hypothyroid in the primary stage. Feature selection techniques used by us are Recursive Feature Selection(RFE)along with classification algorithms named Support Vector Machine(SVM), Decision Tree(DT), Random Forest(RF), Logistic Regression(LR) and Naive Bayes(NB). By observing the results, we could extrapolate that the RFE feature selection technique helps us to provide constant 99.35% accuracy.
Project Documentation: https://drive.google.com/open?id=1TwJ5PHUlWGC1cInXq3VeDLo-BW0GpzgN
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Timestamp: 01-08-2023 11:59
Email Address: bhargavi saroja naidu
Name of The Student as Per SSC: Bhargavi sarojanaidu Sirigineedi
Regd Number: 21A21F0005
Project Title: SUPERVISED AND UNSUPERVISED MACHINE LEARNING BASED ON DIABETES REVIEW
Project Abstract (only): A sedentary lifestyle, bad diet, stress from work causes diabetes, which can also cause many fatal health problems like heart attack, stroke, kidney failure, nerve damage, etc. Automated scanning (ML) strategies are highly effective for early detection and prediction of diabetes. The aim of this report is to provide a comprehensive review of diabetes analysis using supervised and unsupervised system knowledge algorithms. This survey consists of articles about the 2018-2020 Diabetes Assessment. The algorithms are entirely based on decision trees, AdaBoost, XGBoost, etc., and predict diabetes with extraordinary accuracy. Unsupervised knowledge techniques along with PCA and K-Mean are also useful for feature selection and external detection from large datasets. This analysis shows that K-Mean and SVM also diagnosed and assessed diabetes mellitus with excellent accuracy as a combination of supervised and unsupervised device monitoring techniques.
Project Documentation: https://drive.google.com/open?id=1_dzVVdE_AWL2wREf-XZ7667UaDM9jmbt
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Timestamp: 01-08-2023 14:20
Email Address: Rajanikala Dwarampudi
Name of The Student as Per SSC: DWARAMPUDI RAJANIKALA
Regd Number: 21A21F0016
Project Title: Performance analysis of machine learning classifier for predicting chronic kidney disease
Project Abstract (only): Chronic Kidney Disease (CKD) is a type of chronic disease which means it happens slowly over a period of time and persists for a long time thereafter. It is deadly at its end stage and will only be cured by kidney replacement or regular dialysis which is an artificial filtering mechanism. It is important to identify CKD at an early stage so that necessary treatments can be provided to prevent or cure the disease. The main focus of this project is on the classification techniques, that is, tree-based decision trees, random forest, and logistic regression have been analyzed. Different measure has been used for comparison between algorithms for the dataset collected the from standard UCI repository.
Project Documentation: https://drive.google.com/open?id=1azy2Xwco8ZO2--vnVIbrRFNu-xaWQqWY
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Timestamp: 01-08-2023 14:13
Email Address: chelluboinaruchitha2468@gmail.com
Name of The Student as Per SSC: Chelluboina Ruchitha
Regd Number: 21A21F0009
Project Title: Deep Learning for Human Action Recognition
Project Abstract (only): The project is to develop a model for human actions such as running, jogging, walking, clapping, handwaving and boxing etc. A series of videos is given for the layout, where an individual executes an event in each video. The action performed on that particular video will be the label of a video. This relationship must be learned by the model, and the label of an input (video) which he never saw can then be predicted. Technically, despite descriptions of these acts, the model would need to learn to distinguish between various human behaviors. There may be many content identification programs which can work on following jobs like Active object tracking for identifying an item such as a vehicle or a human from a CCTV picture and learning the patterns in the movement of humans when we are able to create a pattern that will guide us (humans) to perform a variety of activities.
Project Documentation: https://drive.google.com/open?id=1T5gRp7rSm1K8i2xy_LzK9vdBVhyWhbpW
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Timestamp: 01-08-2023 15:37
Email Address: Satya Chowdula
Name of The Student as Per SSC: CHOWDULA SATYA DURGA
Regd Number: 21A21F0011
Project Title: machine learning algorithm for stroke disease classification
Project Abstract (only): Stroke is the leading cause of life loss in many countries .This project aims to classify stroke patients into ischemic and hemorrhagic subgroups using optimized CT scan images and eight different machine learning algorithms. We pre-processed data with advanced image optimization techniques to improve image quality and minimize noise. Evaluation metrics such as accuracy, precision, recall, and F1-score were employed to assess algorithm performance. Results indicate success rates for stroke classification. The combination of optimized imaging and machine learning algorithms offers promising insights for enhancing stroke diagnosis and treatment outcomes, potentially reducing stroke-related mortality.
Project Documentation: https://drive.google.com/open?id=1OpMDJVRGsnFOFKA5eI_JOqW2nEJtAQ9g
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Timestamp: 05-08-2023 14:39
Email Address: Venkata Ganesh
Name of The Student as Per SSC: Vendra Venkata Ganesh
Regd Number: 21A21F0064
Project Title: MACHINE LEARNING FOR WEB VULNERABILITY DETECTION THE CASE OF CROSS SITE REQUEST FORGERY
Project Abstract (only): To address security issues caused by network vulnerabilities, tools, and area-based Internet utility vulnerability detection technique has been proposed to effectively prevent programmatic website script attacks. Reduce the spread of network and community security incidents. An indepth look at the latest exploit detection technology, exploit detection test with machine development techniques, exploit detection version requirements, and website scripts. The security vulnerability detection model is designed for .NET applications and is fully based on popular and advanced network vulnerability detection tools, an authentication code detection
Project Documentation: https://drive.google.com/open?id=10ULib07E_vgIQahHIcS0MMPrkxfIifHi
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Timestamp: 01-08-2023 18:57
Email Address: sumaapparie@gmail.com
Name of The Student as Per SSC: APPARI UDAYA SATYA SUMA
Regd Number: 21A21F0003
Project Title: SENTIMENTAL ANALYSIS ON FOOD REVIEW USING MACHINE LEARNINGN APPROACH
Project Abstract (only): Interpersonal interaction correspondence has acquired an everyday public way of addressing the network. Informal correspondence refers to the use of live packets and destinations on the Internet. Twitter is one of the main web-based media in use today. People share their inclinations with the post in many activities in our daily life. What if valuation becomes a huge addiction. In any case, Twitter's strong example of representation of ideas is compromised by a few problems: the particularly unbalanced beauty of calculating different patterns, the problems of explanatory extravagance of emotional labels, and the use of different everyday semantic paradigms. These problems are extreme given the fact that different forms of evaluating online presence are based on subtle insights spread from Twitter. There also appears to be a need for a form of book selection recommended for Twitter cognition research. Twitter data usage estimates are widely attributed to this record. Words and phrases express people's views on things, groups, governments and activities through the method of electronic management structures. Depolarizing the coolest, terrible, or non-partisan content in digital life is called the NLP Skepticism Assessment Project. The remarkable development of association requests from industrial companies and governments, influences specialists to take their exams within the so-called exam. This exploration uses three top-rated ML SVMs, logistic regression, random forests, and a Naive Bayes classifier to increase the overall product score. The scans are completed using datasets from Twitter Yelp. This statistic is on the Internet on the net. Discussion, verbal exchange, fashion objections, and placements are all part of evaluating valuable resources where commentary or published articles are closest to your general inclinations or causes of the problem.
Project Documentation: https://drive.google.com/open?id=1Ple0XhRr55HgWqPVw4KUrrkH3DSa93D9
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Timestamp: 01-08-2023 12:08
Email Address: Sree Vasavi
Name of The Student as Per SSC: PALLAPOTHU SRI SUNDARA TEJO VASAVI
Regd Number: 21A21F0046
Project Title: Implementation of Grey Scale Normalization in Machine Learning & Artificial Intelligence for Bioinformatics using Convolutional Neural Networks
Project Abstract (only): The aim of this project is to perform a grayscale normalization of a selected image and thereafter to reduce the effect of illumination differences. Normalization is considered so that CNN works in a faster manner. The Image Data Generator group in Keras provides a matching set of techniques for scaling pixel standards in the image dataset subsequent to modeling. The Keras functional API provides an additional flexible approach for significant models.
Project Documentation: https://drive.google.com/open?id=1wNcNH6fuC6P2u9AfbgYAFwA8ZG9aNf2m
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Timestamp: 01-08-2023 18:57
Email Address: ramakrishnag2708@gmail.com
Name of The Student as Per SSC: Gelli Ramakrishna
Regd Number: 21A21F0017
Project Title: Image Recognition using Artificial Intelligence
Project Abstract (only): The leading intention of the project is to provide a new approach for image recognition using Python and its library in which we extensively use python libraries like numpy, Bing image downloader, matplotlib, sklearn and several others as well for the use of machine learning and its properties like support vector machine (SVM). An image recognition technique utilizing aa info of image characteristics is introduced.
Project Documentation: https://drive.google.com/open?id=1dLVIyYTP_EAWk4t64OZuDRpaU1T19He2
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Timestamp: 01-08-2023 13:04
Email Address: swethagopu187@gmail.com
Name of The Student as Per SSC: GOPU SWETHA
Regd Number: 21A21F0018
Project Title: Pneumonia Detection Using Deep Learning Based on Convolutional Neural Network
Project Abstract (only): Pneumonia is an infection of the lower part of the respiratory tract in which the airways and lungs are affected. The risk of pneumonia is critical for many, particularly in developing countries where billions of people facing the air pollution problems. The World Health Organization estimates that more than 4 million premature deaths occur yearly from household air pollution-related illnesses, including pneumonia. The X-ray is very important for detecting the pneumonia. This project is very use full for doctors. This is not a time consuming process and no need of high Quality machines and specialists. Here in my project i used deep learning algorithms to process chest X-ray images in order to support the decision making process in determining the correct diagnosis. This algorithm predicts the whether the pneumonia is present or not.
Project Documentation: https://drive.google.com/open?id=181-DOjz1VCP86LiMaGwXlA7vYermhnkP
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Timestamp: 01-08-2023 15:26
Email Address: anilmoka.72@gmail.com
Name of The Student as Per SSC: Moka Anil Kumar
Regd Number: 21A21F0037
Project Title: A Comparative Analysis of Deep Neural Networks for Brain Tumor Detection
Project Abstract (only): the technological advancement in the field of medical science for the detection, classification and identification of several diseases is making the diagnosis process easier and efficient at the same time, provides a helping hand for medical practitioners in saving life. Health experts are making use of these most advanced technological practices for reaching at conclusions in the area of health care. Brain tumor detection is one of the key major challenges in medical field. Early detection of tumor plays the most important role in fixing the most efficient treatment techniques for increasing the survival rate of patients. Manual detection of tumors for diagnosing cancer from data generated from clinical instruments is a time consuming task and the efficiency depends upon the radiologist. So through this paper, we are proposing methods for automating the detection process which can help the radiologist reaching at a faster conclusion in an efficient manner.
Project Documentation: https://drive.google.com/open?id=1zX147Nmo615qXP194HXh-k2nU7iAmfw0
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Timestamp: 01-08-2023 14:30
Email Address: Harika jammisetti
Name of The Student as Per SSC: JAMMISETTI HARIKA
Regd Number: 21A21F0020
Project Title: Design and Implementation of E-commerce Recommendation System Model Based on Cloud Computing
Project Abstract (only): With the continued expansion in employer size, a single offline revenue model cannot meet the demand. To keep up with the current trends of the Internet age, companies want to expand their e-commerce platform. Many e-commerce websites introduce the record mining era. Based on customer’s purchase records and past browsing statistics, they can discover products they like and recommend them to customers to improve the level of dealers and the competitiveness of the internet business market.
Project Documentation: https://drive.google.com/open?id=1ddJQqZv7Ld4_1G4JJYSedTJmmy2-AJjp
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Timestamp: 01-08-2023 19:00
Email Address: Swathi Kadali
Name of The Student as Per SSC: KADALI SWATHI
Regd Number: 21A21F0023
Project Title: Bone Deformity Identification Using Machine Learning
Project Abstract (only): The success of machine learning algorithms in medical imaging has increased the need for artificially trained models to make them work in the medical field more quickly and efficiently. This paper gives a technique to identify bone fracture using machine learning algorithms, by which workload for orthopedics can be reduced. The significant use of machine learning in this era of big medical data would help gather information from the available x-ray images rather than spending hours in the radiology departments. This paper presents imaging technologies used to identify bone fracture in the human body and give quick results once the x-ray has been taken.
Project Documentation: https://drive.google.com/open?id=1t6c6nqSjZbWuyKZvJ1L0AVqTWFBE_P7d
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Timestamp: 01-08-2023 15:55
Email Address: vinayraavi55@gmail.com
Name of The Student as Per SSC: RAAVI VINAY SAI SURENDRA
Regd Number: 21A21F0053
Project Title: Deep Learning based Face Mask Detection using Yolov5
Project Abstract (only): The COVID-19 pandemic has created a major disaster that affects most people's health in most towns in the region, taking a toll on human health. The comparative model is developed with unique differences: 20, 50, 100, 300, and 500. Our main goal is to decide whether a person in a photo/video broadcast is wearing a face mask with the help of deep learning and deep understanding."Deep learning based face mask detection using yolov5 algorithm for real-time streaming and image detection" is a comprehensive project designed to deploy a robust and efficient face mask detection system.The model is trained on a diverse dataset to ensure high accuracy and adaptability to various environmental conditions.
Project Documentation: https://drive.google.com/open?id=1i25mEAZWIq6rsp-xxnwI1vijIojkTMsR
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Timestamp: 01-08-2023 12:02
Email Address: mouli parimi
Name of The Student as Per SSC: PARIMI NITISH MOULI
Regd Number: 21A21F0047
Project Title: Evaluation based Approaches for Liver Disease prediction using machine learning algorithms.
Project Abstract (only): The life of humans living without liver disease is one of the fundamental care of every human. Therefore, for better care detection of liver disease at a primary phase is necessary. For medical experts, predicting the early stage of liver disease is a very difficult task. So, our current work aims to solve the liver disease problem by using machine learning methods.
Project Documentation: https://drive.google.com/open?id=1i4AaQ9nDI0zry81uNibum1qeDNF0HWK5
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Timestamp: 01-08-2023 14:24
Email Address: velivelabhavani2000@gmail.com
Name of The Student as Per SSC: VELIVELA SRI LAKSHMI BHAVANI
Regd Number: 21A21F0063
Project Title: Role of Machine Learning in Human Stress: A Review
Project Abstract (only): Today, it is normal for humans to experience mild depression in various situations. A stress capacity is sufficient for a function. However, too much stress affects a person's mental well- being, and if it is neglected for too long, it is a definite risk of suicide. Long-term stress is associated with health problems. With an increasing number of people facing technological stress, it is crucial to address it as soon as possible and help humans understand and control it before it causes further harm. Traditional techniques for assessing stress levels include interviewing the individual and observing facial expressions. This study represents an in-depth investigation of human stress detection using support vector machine (SVM) algorithm. Accurately detecting stress can prevent psychological and physical problems.
Project Documentation: https://drive.google.com/open?id=1b8ivTbak7VL0LII_m-syxKIkkS0yxb8i
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Timestamp: 01-08-2023 12:03
Email Address: tanneedisirisha@gmail.com
Name of The Student as Per SSC: Tanneedi Sirisha
Regd Number: 21A21F0061
Project Title: Machine Learning Approach to Study the Impact of Obesity on Autonomic Nervous System using Heart Rate Variability Features
Project Abstract (only): Currently, obesity has become one of the major international threats. Obesity refers to an abnormal or unnatural amount of fat in our body. People live bad lives, eat too much junk food, sleep too late and sit too long. Young people are especially vulnerable to their unconscious behaviors. It is a systemic problem known as a fully complex disease. It promotes the spread of complex diseases, stroke, coronary heart disease, liver cancer and many types of cancer. The objective of this paper is to develop an approach based primarily on device handling to estimate the probability of weight problems using machine handling algorithms. The aim of the article is to recognize the outcome of obesity on ANS utilizing by HRV parameters. Originally, 16 older adults and 16 heavy subjects among the ages of 20 and 50 participated in the trial. At the same time, a SMOTE approach was utilized to extend the control sample period. Overweight subjects ranged from 16 to 48
Project Documentation: https://drive.google.com/open?id=19q9KZh-78WhFXFQMbZZmT52_pA4lwPbe
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Timestamp: 01-08-2023 12:07
Email Address: Madhu Kiran
Name of The Student as Per SSC: GURUJU MADHU KIRAN
Regd Number: 21A21F0019
Project Title: Image-Based Plant Disease Detection Using Deep Learning
Project Abstract (only): In developing countries like India, agriculture is excess, but food security remains a major problem. Most flowers are lost due to garage installations, transportation and plant diseases. More than 15% of crops in India are lost due to disease. So, fixing it becomes the first issue. There is a need for an automated machine that can detect these diseases and help farmers take appropriate measures to delay crop failure. Farmers have followed the traditional method of visual detection of plant diseases, and now it is not possible for all farmers to detect even these diseases. With the advancement of artificial intelligence, he wants to add centres of imagination and clairvoyance to agriculture. Deep expertise libraries and a human- and developer-friendly environment to work with – those types of features make deep learning a good way to tackle this problem. This document uses deep learning as it offers instant paint for fast results, especially on images. This method involves taking leaves from infected plants and labelling them according to the disease pattern. Images of swollen leaves are processed simultaneously on a pixel basis, and operations are performed to improve image registration. As a next step, feature extraction is performed, followed by image segmentation, and in the remaining crop disease categories, it is mainly based on samples taken from diseased leaves.
Project Documentation: https://drive.google.com/open?id=1KCe7ZYKsbUVUeieGB7_U1Te0rvmM9b-N
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Timestamp: 01-08-2023 12:07
Email Address: achyuthkumarsadhanala@gmail.com
Name of The Student as Per SSC: Sadhanala Achyuth Kumar
Regd Number: 21A21F0054
Project Title: Car Traffic Sign Recognizer Using Convolutional Nueral Network
Project Abstract (only): Traffic sign Recognizer (TSR) and visibility are important to aid engine pressure and protect drivers, pedestrians, and vehicles. To find ways to push, drivers sometimes forget road signs and signals, and alerts on the road or extreme weather conditions (e.g., fog, rain, and many others) make things difficult for drivers and pedestrians. Our software will help the device to detect and understand the signs and symptoms of visitors without losing the focal point of the driving force while driving .The TSR framework makes it easy for drivers to use road signs and symbols to provide sufficient data about the road. The auto industry has become a big business, and some groups are trying to collect autonomous vehicles that site visitors sign because popularity is important to remember. One version is built using a cnn, and this version will recognize the signs and symptoms of website visitors to understand their signs and symptoms. This version can also be done on common engines to report visitor signs and symptoms and the driving force of symptoms through content recognition.
Project Documentation: https://drive.google.com/open?id=1J1bCgAQ7MQGw57Re1qeGNn5Hcg8mq72T
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Timestamp: 01-08-2023 14:38
Email Address: pavaniampili238@gmail.com
Name of The Student as Per SSC: AMPILI PAVANI
Regd Number: 21A21F0002
Project Title: Improving Lives of Indebted Farmers Using Machine Learning: Predicting Agricultural Produce Prices Using Random Forest and Knn
Project Abstract (only): Farmer suicides have become an urgent social problem which governments around the world are trying hard to solve.Most farmers are driven to suicide due to an inability to sell their produce at desired profit levels, which is caused by the widespread uncertainty/fluctuation in produce prices resulting from varying market conditions. To prevent farmer suicides, this paper takes the first step towards resolving the issue of produce price uncertainty by presenting PECAD, a deep learning algorithm for accurate prediction of future produce prices based on past pricing and volume pattern.
Project Documentation: https://drive.google.com/open?id=1rdFGYVK25OtrkEDiKidgFvidKe0EgB0p
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Timestamp: 02-08-2023 21:35
Email Address: Nani Yadav Manne
Name of The Student as Per SSC: Manne Musalayya
Regd Number: 21A21F0036
Project Title: Classification of Digital Dental X-ray Image Using Machine Learning
Project Abstract (only): During the diagnostic process, the dentist must interpret salient patterns of dental problems, such as the number of teeth and associated diseases. Segmentation of dental X-ray images is useful to help clinicians monitor dental conditions and detect dental disease. This paper aims to discuss the in-depth knowledge and lightweight techniques of dental X-ray image segmentation
Project Documentation: https://drive.google.com/open?id=1Nt5DVH4A2i6JcgYxAvS5vhE9YzT4fO3r
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Timestamp: 01-08-2023 12:28
Email Address: Pavan Kalyan Punya
Name of The Student as Per SSC: Pesala Punya Pavan Kalyan
Regd Number: 21A21F0048
Project Title: Real time Employee Emotion Detection(RtEED) System using Machine Learning
Project Abstract (only): Now a days, employee health and well-being is the most important consideration in the work place. Because it will affect the productivity of an individual employee and team contribution. the automatic facial expression analysis using machine learning has become an interesting and active research area from past few decades. Real time Employee Emotion Detection System (RtEED) has been proposed to automatically detect employee emotions in real time using machine learning. RtEED system helps the employer can check well-being of employees and identified emotion will be intimated to respective employee through messages.
Project Documentation: https://drive.google.com/open?id=1YMgjc8gpyzLHzy3gIfaHdSqqMBhhRCER
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Timestamp: 01-08-2023 14:04
Email Address: yerrasyamvinay@gmail.com
Name of The Student as Per SSC: YERRA SYAMVINAY
Regd Number: 21A21F0065
Project Title: Weapon Detection Using Artificial Intelligence and Deep Learning for security Applications
Project Abstract (only): Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SS D and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually. Results are tabulated, both algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy.
Project Documentation: https://drive.google.com/open?id=1vDlTA2zPSPTw6_t3Pi5-xb6X8bnEubYo
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Timestamp: 01-08-2023 12:26
Email Address: Bvdn Prasad
Name of The Student as Per SSC: Bellamkonda Vigneswara Durga Nagendra Prasad
Regd Number: 21A21F0004
Project Title: An application of a deep learning algorithm for automatic detection of unexpected accidents under bad CCTV monitoring conditions.
Project Abstract (only): In this project, Object Detection and Tracking System (ODTS) in combination with a well-known deep learning network, Faster Regional Convolution Neural Network (Faster R-CNN), for Object Detection and Conventional Object Tracking algorithm will be introduced and applied for automatic detection and monitoring of unexpected events on CCTVs in tunnels, which are likely to (1) Wrong-Way Driving (WWD), (2) Stop, (3) Person out of vehicle in tunnel (4) Fire. This system makes it possible to track a moving object in time, which is not usual to be achieved in conventional object detection frameworks.
Project Documentation: https://drive.google.com/open?id=1j1LMthbcZn-4WYlRUX-mwBP25N557kfr
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Timestamp: 01-08-2023 22:33
Email Address: devisrid1357@gmail.com
Name of The Student as Per SSC: Donga Devisri
Regd Number: 21A21F0014
Project Title: A Video Streaming Vehicle Detection Algorithm Based on YOLOv4
Project Abstract (only): To achieve safe driving of vehicles, it is necessary to perceive information about the vehicle's surroundings, and computer vision is one of the key technologies to solve this problem. The YOLO series and SSD, RetinaNet algorithm are representative of one-stage target detection algorithms, which have high accuracy and high speed. YOLOv4 is the latest algorithm of YOLO series, which has improved the speed and accuracy of vehicle target detection than before, but there is still a distance from the real real-time in vehicle detection. This paper proposes an improved YOLOv4-based video stream vehicle target detection algorithm to solve the problem in the detection speed which is not fast enough. This paper first introduces the YOLOv4 algorithm theoretically, then proposes an algorithmic process to speed up the detection speed, and finally conducts practical road experiments. From the experimental results, the algorithm of this paper can improve the detection speed of the algorithm without losing accuracy, which can provide a basis for decision making for safe vehicle driving.
Project Documentation: https://drive.google.com/open?id=1BZtu4ZAOLGT1Q1q7AJJk12vuN-9jYaeB
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Timestamp: 01-08-2023 18:51
Email Address: bhuvaneswarichinimilli@gmail.com
Name of The Student as Per SSC: Chinimilli Bhuvaneswari
Regd Number: 21A21F0007
Project Title: Enhanced Dermatoscopic Skin Lesion Classification Using Machine Learning Technique
Project Abstract (only): Malefic Cancer will create pleasant effect on people. Much of the research on this topic has been done to judge benign and malignant images and to classify unique types of skin lesions. Therefore, within the approach of skin and porous lesions, beauty, segmentation, feature extraction, and type of dermoscopic images will play an important role. In this record, we are focusing on the class description. We are using the MNIST HAM 10000 dataset. This document contains gadgets. In the first step, we argued that, during mode, a balanced data set would provide greater accuracy than an unbalanced set of data. Therefore, to balance the data set, we use a sampling method called Synthetic Minority Oversampling Technique (SMOTE), which significantly improves the accuracy of most device knowledge models. We then evaluated the accuracy of several device learning algorithms that we ran. As a result, we conclude that the support vector system concept provides higher accuracy than other systems that derive knowledge from algorithms based on polynomial kernels, such as selection tree, Gini, and entropy index. Use of A set of regression laws. We chose precision, do not forget, and F1-rank as valid evaluation metrics. On our device, at best, we got 96.825% accuracy with Support Vector Machine using Polynomial Kernel. Since XGBoost is a gradient. When iterating through a set of rules, the final result provided using the algorithm may vary. To validate the accuracy obtained from the XGBoost ruleset, we used the OK-fold cross-validation method on the XGBoost approach. On our device, we used ok = 10 within the exact fold-omit validation algorithm and obtained an accuracy of 95.984% and concluded that the SVM with polynomial kernel gave higher accuracy than all other algorithms.
Project Documentation: https://drive.google.com/open?id=1tTqN_A3zQvvwsQa-PA0eFNLIqM4K6MHz
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Timestamp: 01-08-2023 14:26
Email Address: yasaswi Devalla
Name of The Student as Per SSC: Devalla Yasaswi
Regd Number: 21A21F0012
Project Title: e-Health Monitoring System with Diet and Fitness Recommendation using Machine Learning
Project Abstract (only): Nowadays, more individuals are being diagnosed with diseases that are becoming chronic due to not following the proper diet, not doing proper exercise regularly, or not giving proper attention to the diseases because of busy schedules. Hence, we propose a system that aims at improving the health of the patients suffering from various diseases by recommending them healthier diet. Our System can be essentially useful for the doctors to recommend diet based on the reports and personal health details.For the Diet and Exercise Recommendation module, the algorithm that is used is a Decision tree for classification. To be precise, C4.5 is used to give recommendations of diet. A C4.5 Decision tree will help recommend and determine if a particular food item should be given to a particular individual or not with respect to our customized datasets.
Project Documentation: https://drive.google.com/open?id=1x8r1TXFr30xiqC29sVIxvpjbL2mrnjsn
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Timestamp: 01-08-2023 12:04
Email Address: yaminipothuri2000@gmail.com
Name of The Student as Per SSC: Pothuri Yamini Rupa Sai Keerthana
Regd Number: 21A21F0052
Project Title: Image Classification of Rice Leaf Diseases Using Random Forest Algorithm
Project Abstract (only): Agricultural experts have done important work on rice plant Parallel Random Forest (CPRF) algorithm to evaluate the applicable information within the last period. Confronting problems of protection. However, the problem of plant diseases has not been investigated in depth until now. In this text, we have explored a sample of rice diseases using the Continuously large size and asymmetry within the statistical distribution of agricultural facts. The proposed technique reduces the scale and potential impact of skewed logs with the help of multiple random forest configurations. For the empirical evaluation, we used the Spark platform to investigate the vegetation records of several provinces in China for twenty years. Results for CPRF transcripts for plant diseases affecting rice yield are presented, as well as results using random forests, CRF, and Spark-MLRF. The accuracy of CPRF is 96.253%, which is better than other algorithms. These results indicate that CPRF and mass data assessment are useful in solving vegetation degradation problems.
Project Documentation: https://drive.google.com/open?id=16nOXTD285-6ApP4wQn94LhBXWESyEqos
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Timestamp: 01-08-2023 12:23
Email Address: somanadhmaka2001@gmail.com
Name of The Student as Per SSC: Maka Somanadh
Regd Number: 21A21F0033
Project Title: Artificial Intelligence and COVID-19 Deep Learning Approaches for Diagnosis and Treatment
Project Abstract (only): The novel coronavirus disease 2019 (COVID-19) has been an extreme health problem that affects the respiratory system and has spread rapidly from humans to other parts of the world. The limited diagnostic strategies are applied to detect patients with Covid-19, which are not effective in overcoming this issue. In this text, deep neighborhood awareness techniques are used to detect the inflammatory regions of the corona virus in the lung. A deep convolutional neural network for large-scale segmentation of the infected community, identifying the specific affected region and evaluating it in a fully deterministic convolutional network in COVID-19 and non-COVID-19 cases. CNN was used to end up labeling it collectively. The proposed model is primarily tested based on detection, segmentation, and sophistication using a validated, qualified dataset of humans infected with COVID-19.
Project Documentation: https://drive.google.com/open?id=1pxtTh_KlMbuRk2LSmnDnaHE-0Klzz2nM
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Timestamp: 01-08-2023 12:55
Email Address: shekbasha472@gmail.com
Name of The Student as Per SSC: SHEK VAHHED SURJAN BASHA
Regd Number: 21A21F0056
Project Title: Integrated Churn Prediction and Customer Segmentation Framework for Telco Business
Project Abstract (only): Customer churn is one of the biggest issues within the telecommunications industry. In the telecom industry, attracting new customers to your network costs more than retaining existing customers. Therefore, it is very important to develop customer churn and techniques to prevent customers from switching to their current issuer. Various sophisticated customer forecasting models and techniques developed by specialized researchers are largely successful. Data mining strategies have proven useful in predicting which customers will switch between their telecommunications businesses. With this forecast, the telecommunication company can implement various customer retention plans. This article reviews several statistical mining strategies for churn prediction.
Project Documentation: https://drive.google.com/open?id=1SycLzqaRjlMcrtMa7SI7-Y1_TQBM3P1u
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Timestamp: 01-08-2023 14:19
Email Address: jayavarapugovardhini@gmail.com
Name of The Student as Per SSC: JAYAVARAPU GOVARDHINI
Regd Number: 21A21F0021
Project Title: Wheat yield prediction using Artificial Intelligence models and its comparative analysis for better prediction
Project Abstract (only): Generally, wheat grows well in moderate temperatures. By predicting wheat yield accurately, farmers can make decisions about their farming practices. They can plan their planting, irrigation, fertilization, and pest control strategies more effectively. Without accurate yield predictions, resources like water and fertilizers could be used inefficiently, leading to waste and increased costs. To overcome this problem I developed this Project. When we are knowing the production of a crop, it may help the farmers can know the yield of wheat.
Project Documentation: https://drive.google.com/open?id=1tvKQGPHj_ksmRetTSGOAhA94FbbKTNs7
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Timestamp: 01-08-2023 11:52
Email Address: Vamshi Ponnapalli28
Name of The Student as Per SSC: Ponnapalli Vamsi Ganesh
Regd Number: 21A21F0051
Project Title: Prediction of Parkinson disease and severity of the disease using Machine Learning and Deep Learning
Project Abstract (only): Parkinson’s disease is a neurodegenerative disease which worsens over time. People have trouble vocally, writing, strolling, or completing other simple tasks when dopaminegenerating neurons in parts of the brain become impaired or expire. These symptoms worsen over time, increasing the severity of the condition in patients. We have suggested a methodology in this article for the prediction of Parkinson’s disease severity using deep neural networks on UCI’s Parkinson’s Telemonitoring Vocal Data Set of patients. We have created a neural network to predict the severity of the disease and a machine learning model to detect the disorder. Classification of Parkinson’s Disease is done by Neural network, Random Forest Classifier.
Project Documentation: https://drive.google.com/open?id=1_sO2pOL07DYv3ifFHQichmV-k9zEA0wc
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Timestamp: 01-08-2023 12:02
Email Address: Mani Siddireddy
Name of The Student as Per SSC: Siddireddi Manikanta
Regd Number: 21A21F0057
Project Title: Automated Food Image Classification Using Deep Learning
Project Abstract (only): Food image classification is an emerging research field due to its increasing benefits in the health and medical sectors. For sure, in the future automated food recognition tools will help in developing diet monitoring systems, calories estimation and so on. In this paper, automated methods of food classification using deep learning approaches are presented. SqueezeNet and VGG-16 Convolutional Neural Networks are used for food image classification.
Project Documentation: https://drive.google.com/open?id=1BIVzuxDgSLKTDmX3rIauRBebDa7JizLu
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Timestamp: 01-08-2023 11:58
Email Address: Sai Prasanna Netala
Name of The Student as Per SSC: NETALA SAI PRASANNA
Regd Number: 21A21F0043
Project Title: Application of Convolutional Neural Networks to the Classification of Agricultural Technology Articles
Project Abstract (only): The main aim of this project to develop a classification system for agricultural articles using CNN algorithm. It is used to solve the time -consuming and labuor intensive problem of classifying agricultural articles . The rapid growth of agricultural information on the internet has made it challenging for researchers , farmers to efficiently locate relevant literature. This system is used for solving this issues and classifies the agricultural articles into different categories like planting , processing , cultivation , Reproduction .
Project Documentation: https://drive.google.com/open?id=141_5Nr7PQbJSv9ceArPJg-i4SQt7eLjg
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Timestamp: 01-08-2023 11:55
Email Address: varmaponnala73@gmail.com
Name of The Student as Per SSC: PONNALA VARMA
Regd Number: 21A21F0050
Project Title: A Comprehensive Study of Machine Learning algorithms for Predicting Car Purchase Based on Customer Demands
Project Abstract (only): The automobile industry is one of the prominent industries for the national economy. Day by day car is getting popular for the private transport system. The customer needs review when he wants to buy the right vehicle, especially the car. Because it is a very costly vehicle. There are many conditions and factors matter before buying a new car like spare parts, cylinder volume, headlight and especially price. So deciding everything, it is important for the customer to make the right choice of purchase which can satisfy all the criteria. Our goal is to help the customer to make the right decision whether he will buy a car or not. Therefore we wanted to build a technique for decision making in-car buy system. That’s why we propose some wellknown algorithms to get better accuracy for a car purchase in our project. We applied those algorithms in our dataset which contains 50 data. Among them, Support Vector Machine(SVM) gives the best result with 86.7% accuracy of prediction.
Project Documentation: https://drive.google.com/open?id=1BnLZSXU72Fxt4hQe2hCDofjuZcwxeupM
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Timestamp: 01-08-2023 12:21
Email Address: muralinanda96@gmail.com
Name of The Student as Per SSC: Nanda Murali Krishna
Regd Number: 21A21F0042
Project Title: Movie Recommender System Using Sentiment Analysis
Project Abstract (only): Recommendation systems are the most important intelligent systems that plays in giving the information to the users. Previously approaches in recommendation systems (RS) include Content-based-filtering and collaborative filtering. Thus, these approaches had certain limitations as like the necessity of the user history as they visit. So as to make back the effect of such dependencies, this research paper provides a hybrid RS are those which mixes both Collaborative filtering, Content based filtering with sentiment analysis of movies. In this research paper, we developed a recommender system based on the sentiment of the user to suggest the movie to the user based on their view history
Project Documentation: https://drive.google.com/open?id=1jJGF7lAJ2-5n8pGzz7SKgJF0_kokQaWb
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Timestamp: 01-08-2023 14:12
Email Address: ss4173537@gmail.com
Name of The Student as Per SSC: kopparthi Divya
Regd Number: 21A21F0027
Project Title: Black Firday sales mechine learnig pediction framework
Project Abstract (only): Understanding the buying behavior of a pair of consumers (specific variables) using their demographic characteristics (IS characteristics, most of which are self-explanatory) to approximate an exceptional product. This collection of facts includes values, redundant information and device experience. Encapsulation of the retail industry within a location – This idea helps scale expectations that have great business value for store owners because it allows stock Controlling, financial planning, advertising and marketing, and advertising and marketing, advertising and marketing will help. We are developing a model consisting of processing, modeling, schooling, control and evaluation so that managers can automate and complicate part of it. Our proposed algorithm is transformed into a random forest area with the Accuracy of 83.6% and minimum RMSE (root squared errors) of 2829 on Black Friday tire sales data.
Project Documentation: https://drive.google.com/open?id=1yfB-zI-gEWrdM2b6LlRmJY3toJ_bMNdD
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Timestamp: 01-08-2023 11:59
Email Address: siram saikrishna
Name of The Student as Per SSC: Saikrishna Siram
Regd Number: 21A21F0058
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Project Abstract (only): Hepatitis is a life-threatening condition caused by inflammation of the liver. It can result from infection of the liver by hepatitis viruses (hepatitis B, hepatitis C, and viral hepatitis). Manual tests can greatly overload evaluation, which has a great impact on men's and women's fitness software. Automated hepatitis prediction, using machine learning strategies such as Naive Bayes , Support Vector Machine (SVM), K-NN, perceptron, and Random Forest, promises to serve as an effective diagnostic tool. Based on this comment, a decision tree is built to predict hepatitis, and another algorithm, K-NN and perception, is executed to predict the same disease. The three learning algorithms are compared with each of them with respect to the volume in which their hepatocytes are located. Comparisons are made using the RAND index and the confusion matrix. By postponing observations that do not contain missing values beyond the reality location feature selection system, we have helped improve the accuracy of our prediction models.
Project Documentation: https://drive.google.com/open?id=1a0YIZyTJ7VrdjjwSb6VYvX12fu2bs6bS
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Timestamp: 4-12-2024 14:49:42
Email Address: chandiniagisetti@gmail.com
Name of The Student as Per SSC: AGISETTI CHANDINI
Regd Number: 22A21F0067
Project Title: Automatic english essay scoring algorithm using machine learning
Project Abstract (only): With the development of natural language processing(NLP) technology and machine learning, the research task of automatic English scoring(AES) is becoming clearer and clearer, and the research difficulties arise due to the mutual constraints of research methods and annotation data. How to build a perfect and reliable scoring system has become a great challenge under today's research. In this paper, we designed an English AES system, and verified the effectiveness of RF on English scoring model by analyzing the prediction effect of RF on non-text features and text features, and then compared the Pearson correlation coefficients(PCC) of RF(RF), GBDT, and XGBoost, and the study showed that the performance of RF algorithm is higher than the other two composition scoring methods.
Project Documentation: https://drive.google.com/open?id=1KhTofUCEAzFCmJvhSgUWt3_SsVB1yOAM
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Timestamp: 4-12-2024 14:49:55
Email Address: saiswarupa520@gmail.com
Name of The Student as Per SSC: YEDLAPALLI SAI SWARUPA
Regd Number: 22A21F00D2
Project Title: Automated Resume Screening Using Natural Language Processing
Project Abstract (only): The most qualified applicant for a position must be found through careful consideration of job applications, which is done during the Automated Resume Screening Using Natural Language processing. Automated resume screening is now a practical alternative to the manual screening procedure because to developments in natural language processing .To increase the precision and effectiveness of the screening process, these approaches employ a variety of method for screening automated resumes . The results of this study can help human resource managers and recruiters automate the hiring process and efficiently and impartially identify viable applicants.
Project Documentation: https://drive.google.com/open?id=15jmUD9BgkI_wuS7t5qG-FoLn3ii9QWsI
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Timestamp: 4-12-2024 15:18:28
Email Address: ravurisunitha1@gmail.com
Name of The Student as Per SSC: RAVURI SUNITHA
Regd Number: 22A21F00B2
Project Title: Sentimental Polarity Detection using Machine Learning and Deep Learning
Project Abstract (only): As e-commerce has grown in recent years, so online shopping has increased with the number of product reviews posted online. The consumer’s recommendations or complaints influence significantly customers and their decision to purchase. Sentiment polarity analysis is the interpretation and classification of text-based data. The main goal of our work is to categorize each customer’s review into a class that represents its quality (positive or negative). Our sentiment polarity detection consists of the following steps: preprocessing, feature extraction, training, classification and generalization. First, the reviews were transformed into vector representation using different techniques of Tf-Idf and Tokenizer. Then, we trained with a machine learning model of SVM Linear, RBF, Sigmoid kernel and a deep learning model LSTM. After that, we evaluated the models using accuracy, f1-score, precision, recall. Our LSTM model predicts an accuracy of 86% for Amazon-based customer reviews and an accuracy of 85% for Yelp customer reviews
Project Documentation: https://drive.google.com/open?id=14v9l1XLoR7vvVk3XMvfubz2HhcwKCY7K
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Timestamp: 4-12-2024 15:34:57
Email Address: inti.vasavi@gmail.com
Name of The Student as Per SSC: INTI VASAVI
Regd Number: 22A21F0069
Project Title: The price prediction of sneakers based on machine learning
Project Abstract (only): This project aims to predict the prices of sneakers using machine learning techniques. The sneaker market is known for its volatile nature, with prices fluctuating rapidly due to various factors such as brand popularity, limited edition releases, and celebrity endorsements. To tackle this problem, we gathered data from Stock X, a leading sneaker marketplace, and utilized four linear regression models for prediction. Through our analysis, we found that the Ordinary Least Squares (OLS) regression model exhibited the highest accuracy in predicting sneaker prices. Our study demonstrates the potential of machine learning in forecasting sneaker prices, providing valuable insights for both consumers and sellers in this dynamic market.
Project Documentation: https://drive.google.com/open?id=1fuUKQq8w8oNNj14yvzrwLL70ZrP-q0DF
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Timestamp: 4-12-2024 16:01:13
Email Address: jaddujhansi914@gmail.com
Name of The Student as Per SSC: JADDU JHANSI DURGA BHAVANI
Regd Number: 22A21F0075
Project Title: Predicting chronic kidney disease machine learning algorithms
Project Abstract (only): In the modern era, everyone tries to be aware of their health, but because of their workload and hectic schedules, they only pay attention to it when certain symptoms appear. However, because CKD (Chronic Kidney disease) is a disease with no symptoms or, in some cases, no symptoms at all, it is difficult to predict, detect, and prevent such a disease, which could result in long-term health damage. However, machine learning (ML) offers hope in this situation because it excels at prediction and analysis. In this paper, we proposed nine ML approaches, such as K-nearest neighbors (KNN), support vector machines (SVM), logistic regression (LR), Naïve Bayes, Extra tree classifiers, AdaBoost, Xgboost, and LightGBM. These predictive models are built using a dataset on chronic kidney disease with 14 attributes and 400 records to choose the best classifier for predicting chronic kidney illness. The dataset was gathered via Kaggle.com. Additionally, this study has compared how well these model’s function. With the LightGBM model, we could predict kidney illness more accurately than ever before, with a 96.00% accuracy level.
Project Documentation: https://drive.google.com/open?id=1bCG2khMtrvbBFW3q6Gs5aWXVcNsfHu8T
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Timestamp: 4-12-2024 18:46:23
Email Address: abhichintha4@gmail.com
Name of The Student as Per SSC: Chintha Abhinaya
Regd Number: 22A21F0018
Project Title: HELMET DETECTION AND NUMBER PLATE RECOGNITION USING DEEP LEARNING
Project Abstract (only): In current situation, we come across various problems in traffic regulations in India which can be solved with different ideas. Riding motorcycle without wearing helmet is a traffic violation which has resulted in increase in number of accidents and deaths in India. Existing system monitors the traffic violations primarily through CCTV recordings, where the traffic police have to look into the frame where the traffic violation is happening, zoom into the license plate in case rider is not wearing helmet. But this requires lot of manpower and time as the traffic violations frequently and the number of people using motorcycles is increasing day-by-day. What if there is a system, which would automatically look for traffic violation of not wearing helmet while riding motorcycle and if so, would automatically extract the vehicles' license plate number. Recent research have successfully done this work based on CNN, R-CNN. But these works are limited with respect to efficiency, accuracy or the speed with which object detection and classification is done. In this research work, a Non-Helmet Rider detection system is built which attempts to satisfy the automation of detecting the traffic violation of not wearing helmet and extracting the vehicles' license plate number. The main principle involved is Object Detection using Deep Learning. Then the license plate registration number is extracted using OCR . All these techniques are subjected to predefined conditions and constraints, especially the license plate number extraction part. Since, this work takes video as its input, the speed of execution is crucial.
Project Documentation: https://drive.google.com/open?id=1tpcqzUJc7X2oWl1YKlKNhLuExlX-Tzka
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Timestamp: 4-12-2024 20:22:09
Email Address: aadikommula@gmail.com
Name of The Student as Per SSC: KOMMULA LAKSHMAN ADI VENKAT ESWAR
Regd Number: 22A21F0051
Project Title: Diabetes Disease Prediction Using Machine Learning Algorithms
Project Abstract (only): This paper deals with the prediction of Diabetes Disease by performing an analysis of five supervised machine learning algorithms, i.e. K-Nearest Neighbors, Na¨?ve Baye, Decision Tree Classifier, Random Forest and Support Vector Machine. Further, by incorporating all the present risk factors of the dataset, we have observed a stable accuracy after classifying and performing cross-validation. We managed to achieve a stable and highest accuracy of 76% with KNN classifier and remaining all other classifiers also give a stable accuracy of above 70%. We analyzed why specific Machine Learning classifiers do not yield stable and good accuracy by visualizing the training and testing accuracy and examining model overfitting and model underfitting. The main goal of this paper is to find the most optimal results in terms of accuracy and computational time for Diabetes disease prediction.
Project Documentation: https://drive.google.com/open?id=1Oa9orCjm2xygpdwLxX1tj6G87TDNcI59
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Timestamp: 4-12-2024 21:32:54
Email Address: vasanthamannempalli@gmail.com
Name of The Student as Per SSC: MANNEMPALLI VASANTHA LAKSHMI
Regd Number: 22A21F0055
Project Title: Prediction Of Air Pollution By Using Machine Learning
Project Abstract (only): controlling and defensive the higher air greatness has gotten one in everything about first imperative occasions in different creating and metropolitan districts at the present. The greatness of air is adversely contacting collectible to the different styles of tainting influenced through the transportation, power, powers consumptions, and so forth. In our country population is a big problem as day by day population is increasing, so the rapid increasing in population and economic upswing is leading environment problems in city like air pollution, water pollution etc. In some of air pollution and air pollution is direct impact on human body. As we know that major pollutants are arising from Nitrogen Oxide, Carbon Monoxide & Particulate matter (PM), SO2 etc. Carbon Monoxide is arising due to the deficient Oxidization of propellant like as petroleum, gas, etc. nitrogen oxide (NO) is arising due to the ignition of thermal fuel; Sulphur Dioxide(So2) is major spread in air, So2 is a gas which is present more pollutants in air, it’s affect more in human body. the predominance of air is overstated by multidimensional impacts containing spot, time and vague boundaries. The goal of this improvement is to take a gander at the AI basically based ways for air quality expectation. In this paper we will predict of air pollution by using machine learning algorithm.
Project Documentation: https://drive.google.com/open?id=1xouCz0akQtcFmFfeMvBN09eV6P_P1OHc
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Timestamp: 4-12-2024 22:17:16
Email Address: kishorepatnala2001@gmail.com
Name of The Student as Per SSC: PATNALA YUVA NAGA DURGAVASANTHA KISHORE
Regd Number: 22A21F00A4
Project Title: Dog Breed Identification Using Deep Learning
Project Abstract (only): The current paper presents a fine-grained image recognition problem, one of multi-class classification, namely determining the breed of a dog in a given image. The presented system employs innovative methods in deep learning, including convolutional neural networks. Two different network are trained and evaluated on the Stanford Dogs dataset. The usage/evaluation of convolutional neural networks is presented through a software system. It contains a central server and a mobile client, which includes components and libraries for evaluating on a neural network in both online and offline environments.
Project Documentation: https://drive.google.com/open?id=1e4HlJPqenWKiQarFgHmyondfrvGbB6b6
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Timestamp: 4-13-2024 15:47:49
Email Address: vallamkondavanipriya@gmail.com
Name of The Student as Per SSC: VALLAMKONDA VANI PRIYA
Regd Number: 22A21F00C0
Project Title: Indian currency classification using deep learning techniques
Project Abstract (only): Progression and evolution of technology has super- seeded mechanical human workload in almost every domain with the operation of machines. The currency paper recognition is applicable in various domains of automatic selling goods systems and in banking systems. In the modern transition world for the automatic current recurring systems, the precise identi?cation of paper currency notes is indeed an essential need. Machines often ?nd it dif?cult in identifying and recognising the currencies in the market when the currency notes have turned bleary and damaged. It is hard for visually disabled people without any technological support or assistance to predict and analyse genuine currency notes. The accuracy of currency notes analysis identi?cation have been re?ned and boosted throughout with the assistance of these models.
Project Documentation: https://drive.google.com/open?id=1l2NHJL8tlw-CWjHIhb6D0lfh0noUA2OD
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Timestamp: 4-13-2024 15:55:04
Email Address: yathamgowthami5@gmail.com
Name of The Student as Per SSC: YATHAM GOWTHAMI
Regd Number: 22A21F00C9
Project Title: Enhancing Password Security Via Supervised Meachine Learning
Project Abstract (only): Text-based passwords are currently the most widely used form of authentication and are expected to remain so. However, since these passwords are typically made up of meaningful strings, machine learning and deep learning algorithms can help developers assess their strengths and predict their vulnerability to brute-force attacks. Advanced techniques such as Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GAN) can generate lists of similar and expected text passwords by learning the patterns of how users create and select their passwords. This paper explores using machine learning algorithms to categorize passwords into three levels: strong, moderate, and weak. We also evaluate whether machine learning or deep learning techniques can learn the patterns used in hashing techniques. Furthermore, we have developed a password generation model using Gated Recurrent Unit (GRU) to create new passwords based on learned patterns. With this approach, we aim to improve password security and enhance the user experience of creating and managing passwords.
Project Documentation: https://drive.google.com/open?id=1kY8H3C_bFfL100ZtAg-R4Xn0cHG_Km74
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Timestamp: 4-13-2024 18:00:01
Email Address: pradeepnelluri88@gmail.com
Name of The Student as Per SSC: NELLURI LAKSHMI PRADEEP
Regd Number: 22A21F0099
Project Title: Depression Detection Using Machine Learning Techniques on Twitter Data
Project Abstract (only): Depression has become a serious problem in this current generation and the number of people affected by depression is increasing day by day. However, some of them manage to acknowledge that they are facing depression while some of them do not know it. On the other hand, the vast progress of social media is becoming their “diary” to share their state of mind. Several kinds of research had been conducted to detect depression through the user post on social media using machine learning algorithms
Project Documentation: https://drive.google.com/open?id=1Yxe4mpXrlk08J1SxTJwaTWeQQTORD_Hh
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Timestamp: 4-13-2024 18:36:21
Email Address: ummidiswathi9@gmail.com
Name of The Student as Per SSC: UMMIDI SWATHI VIJAYA SRI
Regd Number: 22A21F00B9
Project Title: AI Enabled Healthcare Management system
Project Abstract (only): The integration of artificial intelligence (AI) into healthcare systems has significantly transformed patient care and management. This abstract presents an overview of an AI-enabled healthcare management system designed to optimize healthcare delivery, enhance patient outcomes, and streamline administrative processes. The AI-enabled healthcare management system leverages advanced machine learning algorithms and natural language processing techniques to analyze vast amounts of medical data efficiently. By harnessing the power of AI, healthcare providers can make data-driven decisions, personalize treatment plans, and improve diagnostic accuracy.
Project Documentation: https://drive.google.com/open?id=1csvrPkqxluywQ0-DeUFtkqgtQOPbLfG3
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Timestamp: 4-13-2024 18:44:25
Email Address: aparnapampana1801@gmail.com
Name of The Student as Per SSC: PAMPANA APARNA
Regd Number: 22A21F0058
Project Title: SOCIAL MEDIA POPULARUTY PREDICTION BASED ON MULTI MODELLING SELF ATTENTION MECHANISM
Project Abstract (only): Popularity prediction using social media is an important task because of its wide range of real-world applications such as advertisements, recommendation systems, and trend analysis. However, this task is challenging because social media is affected by multiple factors that cannot be easily modeled (e.g.quality of content, relevance to viewers, real-life events). Usually, other methods adopt the greedy approach to include as many modalities and factors as possible into their model but treat these features equally.To solve this phenomenon, our proposed method leverages the self-attention mechanism to effectively and automatically fuse different features to achieve better performance for the popularity prediction of a post, where the features used in our model can be mainly categorized into two modalities, semantic (text) and numeric features.
Project Documentation: https://drive.google.com/open?id=1MNmvp7LkEq45-0aZ2DWE9ZubI2XhCdlH
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Timestamp: 4-13-2024 18:51:22
Email Address: ramarish12345@gmail.com
Name of The Student as Per SSC: Jyotsna chellinki
Regd Number: 22A21F0016
Project Title: Campus placement prediction and analysis using machine learning algorithms
Project Abstract (only): Placement of students is one of the most important objective of an educational institution. Reputation and yearly admissions of an institution invariably depend on the placements it provides it students with. That is why all the institutions, arduously, strive to strengthen their placement department so as to improve their institution on a whole. Any assistance in this particular area will have a positive impact on an institution’s ability to place its students. This will always be helpful to both the students, as well as the institution. In this study, the objective is to analyse previous year's student's data and use it to predict the placement chance of the current students. This model is proposed with an algorithm to predict the same. Data pertaining to the study were collected form the same institution for which the placement prediction is done and also suitable data pre-processing methods were applied. This proposed model is also compared with other traditional classification algorithms such as Decision tree and Random forest with respect to accuracy, precision and recall. From the results obtained it is found that the proposed algorithm performs significantly better in comparison with the other algorithms mentioned.
Project Documentation: https://drive.google.com/open?id=1S452-eaWwJWdjVOilzk4JUyX3xzYdm0o
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Timestamp: 4-13-2024 19:05:17
Email Address: theeparthijayakrishna633@gmail.com
Name of The Student as Per SSC: TEEPARTHI JAYAKRISHNA
Regd Number: 22A21F0064
Project Title: PREDICTING MISLEADING INFORMATION IN SOCIAL MEDIA FOR BETTER DEMOCRACY
Project Abstract (only): In this paper, we present a resource allocation mechanism for the problem of incentivizing filtering among a finite number of strategic social media platforms. We consider the presence of a strategic government and private knowledge of how misinformation affects the users of the social media platforms. Our proposed mechanism incentivizes social media platforms to filter misleading information efficiently, and thus indirectly prevents the spread of fake news. In particular, we design an economically inspired mechanism that strongly implements all generalized Nash equilibria for efficient filtering of misleading information in the induced game. We show that our mechanism is individually rational, budget balanced, while it has at least one equilibrium. Finally, we show that for quasi-concave utilities and constraints, our mechanism admits a generalized Nash equilibrium and implements a Pareto efficient solution.
Project Documentation: https://drive.google.com/open?id=1ryayd04koGVF2UT5TPuRmetvtWPcOUNV
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Timestamp: 4-13-2024 19:51:20
Email Address: k.pavankumar1432n@gmail.com
Name of The Student as Per SSC: KOLLEPARA PAVAN KUMAR
Regd Number: 22A21F0050
Project Title: A Road accident prediction model using datamining techniques.
Project Abstract (only): Due to the exponentially increasing number of vehicles on the road, the number of accidents occurring on a daily basis is also increasing at an alarming rate. With the high number of traffic incidents and deaths these days, the ability to forecast the number of traffic accidents over a given time is important for the transportation department to make scientific decisions. In this scenario, it will be good to analyze the occurrence of accidents so that this can be further used to help us in coming up with techniques to reduce them. Even though uncertainty is a characteristic trait of majority of the accidents, over a period of time, there is a level of regularity that is perceived on observing the accidents occurring in a particular area. This regularity can be made use of in making well informed predictions on accident occurrences in an area and developing accident prediction models. In this paper, we have studied the inter relationships between road accidents, condition of a road and the role of environmental factors in the occurrence of an accident. We have made use of data mining techniques in developing an accident prediction model using Apriori algorithm and Support Vector Machines. I am using data set available in the internet(Kaggle) have been made use for this study. The results from this study can be advantageously used by several stakeholders including and not limited to the government public work departments, contractors and other automobile industries in better designing roads and vehicles based on the estimates obtained.
Project Documentation: https://drive.google.com/open?id=1NoFfdx0uaAUOh3GSQP43JAvrTzo02mP3
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Timestamp: 4-13-2024 20:27:38
Email Address: dineshmygapula.mca@gmail.com
Name of The Student as Per SSC: MYGAPULA SRI NAGA SAI DINESH
Regd Number: 22A21FOO56
Project Title: DRIVER DROWSINESS MONITORING USING CONVOLUTIONAL NEURAL NETWORKS
Project Abstract (only): The advancement in computer vision has assisted drivers in the form of automatic self-driving cars etc. The misadventure are caused by driver's fatigue and drowsiness about 20%. It poses a serious problem for which several approaches were proposed. However, they are not suitable for real-time processing. The major challenges faced by these methods are robustness to handle variation in human face and lightning conditions. We aim to implement an intelligent processing system that can reduce road accidents drastically. This approach enables us to identify driver's face characteristics like eye closure percentage, eye-mouth aspect ratios, blink rate, yawning, head movement, etc. In this system, the driver is continuously monitored by using a webcam. The driver's face and the eye are detected using haar cascade classifiers. Eye images are extracted and fed to Custom designed Convolutional Neural Network for classifying whether both left and right eye are closed. Based on the classification, the eye closure score is calculated. If the driver is found to be drowsy, an alarm will be triggered.
Project Documentation: https://drive.google.com/open?id=1zIVZ_uSq4TjzbKu4684eAts_Bhp6Orow
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Timestamp: 4-13-2024 20:27:58
Email Address: nanijavvadi341@gmail.com
Name of The Student as Per SSC: JAVVADI SATISH CHANDRA
Regd Number: 22A21F0040
Project Title: RECOMMENDATION OF INDIAN CUISINES RECIPES BASED ON INGREDIENTS
Project Abstract (only): There are lots of varieties of Indian cuisine available with same ingredients. In India, Traditional cuisines consist of wide varieties due to locally available spices, herbs, vegetables, and fruits. In this paper, we purposed a method that recommends recipes of Indian cuisine on the basis of available ingredients and liked cuisine. For this work, we did web scraping to make a collection of recipes' varieties and after that apply the content-based approach of machine learning to recommend the recipes. This system gives the recommendation of Indian Cuisines based on ingredients.
Project Documentation: https://drive.google.com/open?id=1xSRarLPPGoTj11f0Mhb4RA83T8y_jsXq
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Timestamp: 4-13-2024 22:04:47
Email Address: pillasujana@gmail.com
Name of The Student as Per SSC: Pilla Sujana
Regd Number: 22A21F0060
Project Title: Predicting flight delays with error calculation using machine learned classifiers
Project Abstract (only): Flight delay is a major problem in the aviation sector. During the last two decades, the growth of the aviation sector has caused air traffic congestion, which has caused flight delays. Flight delays result not only in the loss of fortune also negatively impact the environment. Flight delays also cause significant losses for airlines operating commercial flights. Therefore, they do everything possible in the prevention or avoidance of delays and cancellations of flights by taking some measures. In this paper, using machine learning models such as Logistic Regression, Decision Tree Regression, Bayesian Ridge, Random Forest Regression and Gradient Boosting Regression we predict whether the arrival of a particular flight will be delayed or not.
Project Documentation: https://drive.google.com/open?id=17ir_bYUL6Kwj1NoxXsUtwnrFW1vDw-XJ
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Timestamp: 4-13-2024 22:16:57
Email Address: saibavisetti123@gmail.com
Name of The Student as Per SSC: BHAVISETTI VENKATA SAI NAGA LAKSHMI
Regd Number: 22A21F0071
Project Title: Machine Learning Algorithms based Student Performance Prediction based on Previous Records
Project Abstract (only): Predicting a student's grade has become increasingly important in order to determine whether or not the student will be placed. In order to develop after this and obtain better placements, it is also being attempted to ascertain the student performance. Based on their recent and prior performances, the prediction will be made. The major goal is to improve the student unsatisfactory performances so that they can improve their academic performance. Therefore, using these factors, the predictor will determine whether or not the student will be hired by the company. The biggest problem in this situation is deciding which algorithm to utilize because there are numerous classification algorithm strategies that can be applied. Based on the dataset, different algorithms produce different degrees of accuracy. It uses a classification method for machine learning. It is a theory that uses probability to identify a fix for the existing issue.
Project Documentation: https://drive.google.com/open?id=1bVTq4bAXYx9N_I1fLGhKdskGMHaXxQtH
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Timestamp: 4-13-2024 22:21:18
Email Address: vamsimanchiganti184@gmail.com
Name of The Student as Per SSC: MANCHIGANTI VAMSI SRINIVAS
Regd Number: 22A21F0091
Project Title: Resume screening based on skillset using ML techniques
Project Abstract (only): In today’s competitive world, it is a very complicated process to hire candidates with manual verification of resumes. This work is an experimental method for ranking of hiring resumes because manually ranking is quite a complicated job for the hiring team, as it takes more time to verify each of the candidates resumes. If the resumes are high in number then man power will also increase for the same task. To rectify these problems a new solution has been proposed. In order to make this whole resume screening process more effective, an application for processing the resumes screening using machine learning is proposed. This work uses methods such as optimizing the candidates’ performance in the preferred skill mentioned in the resume and also ranking method to display the selected candidates based on their overall performance according to the skill requirement of the company’s required job position. The whole idea is implemented using python language and the results are sure to make the recruitment process efficient.
Project Documentation: https://drive.google.com/open?id=1O842UrtaPpdhEHRtEtWcEd7sa2gRWrLm
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Timestamp: 4-14-2024 8:14:44
Email Address: swarupagaragapati2@gmail.com
Name of The Student as Per SSC: GARAGAPATI SWARUPA
Regd Number: 22A21F0073
Project Title: MACHINE LEARNING BASED PATIENT CLASSIFICATION IN EMERGENCY DEPARTMENT
Project Abstract (only): This work contains the classification of patients in an Emergency Department in a hospital according to their critical conditions. Machine learning can be applied based on the patient’s condition to quickly determine if the patient requires urgent medical intervention from the clinicians or not. Basic vital signs like Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Respiratory Rate (RR), Oxygen saturation (SPO2), Random Blood Sugar (RBS), Temperature, Pulse Rate (PR) are used as the input for the patients’ risk level identification. High-risk or non-risk categories are considered as the output for patient classification. Basic machine learning techniques such as LR, Gaussian NB, SVM, KNN and DT are used for the classification. Precision, recall, and F1-score are considered for the evaluation. The decision tree gives best F1-score of 77.67 for the risk level classification of the imbalanced dataset.
Project Documentation: https://drive.google.com/open?id=1pSzawkGlVA71cAaoUhgiUid4HiBd3nqk
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Timestamp: 4-14-2024 9:38:04
Email Address: bhavaniguruju@gmail.com
Name of The Student as Per SSC: Guruju Sri Lakshmi Bhavani
Regd Number: 22A21F0031
Project Title: Child Mortality Prediction using Machine Learning Techniques
Project Abstract (only): Children's Mortality alludes to mortality of children younger than 5. The kid death rate, in addition under-five death rate, alludes to the probability of biting the mud among birth and exactly 5 years recent. The mortality of kids in addition happens in embryo. The purpose is to analysis AI based mostly strategies for grouping of mortality vertebrate upbeat characterization brings concerning best truth. The examination of dataset by directed AI procedure (SMLT) to catch a couple of data's like, variable characteristic proof, uni-variate investigation, bi-variate and multi-variate examination, missing value medicines and dissect the data approval, data cleaning/getting prepared and knowledge illustration are done on the entire given dataset. Our examination provides a whole manual for responsiveness investigation of model boundaries on execution within the characterization of vertebrate upbeat. To propose AN AI based mostly and moreover, to seem at and examine the presentation of various AI calculations for the given dataset.
Project Documentation: https://drive.google.com/open?id=1SeLfnCoWG5x1GLmlNBXA6i_6Efc8iIjJ
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Timestamp: 4-14-2024 11:09:58
Email Address: dwarampudichandana@gmail.com
Name of The Student as Per SSC: DWARAMPUDI SATYAGOWRI CHANDANA
Regd Number: 22A21F0022
Project Title: SECURE DATA SHARING USING CP-ABE-KEM IN CLOUD STORAGE ACROSS PLATFORMS
Project Abstract (only): With more and more data moving to the cloud, privacy of user data have raised great concerns. Client-side encryption/decryption seems to be an attractive solution to protect data security, however, the existing solutions encountered three major challenges: low security due to encryption with low-entropy PIN, inconvenient data sharing with traditional encryption algorithms, and poor usability with dedicated software/plugins that require certain types of terminals. This work designs and implements Web Cloud, a practical browser-side encryption solution, leveraging modern Web technologies. It solves all the above three problems while achieves several additional remarkable features: robust and immediate user revocation, fast data processing with offline encryption and outsourced decryption. Notably, our solution works on any device equipped with a Web user agent, including Web browsers, mobile and PC applications. We implement Web Cloud based on own Cloud for basic file management utility, and utilize Web Assembly and Web Cryptography API for complex cryptographic operations integration. Finally, comprehensive experiments are conducted with many well-known browsers, Android and PC applications, which indicates that Web Cloud is cross-platform and efficient. As an interesting by-product, the design of Web Cloud naturally embodies a dedicated and practical ciphertext-policy attribute-based key encapsulation mechanism (CP-AB-KEM) scheme, which can be useful in other applications.
Project Documentation: https://drive.google.com/open?id=1IF7MMLCS0QUPucfwCbLe5v9Wg4ELMcOG
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Timestamp: 4-14-2024 11:10:14
Email Address: msdevi2711@gmail.com
Name of The Student as Per SSC: MEDIDHI SUBHADRA DEVI
Regd Number: 22A21F0093
Project Title: An Enhanced Segmentation and Deep Learning Architecture for Early Diabetic Retinopathy Detection
Project Abstract (only): Diabetic retinopathy is a serious complication needing prompt diagnosis and medication to avert vision loss. Lesions caused by the condition are difficult to track because they are hidden behind the eye’s structure in small and subtle forms. To extract relevant features, we created a robust pipeline using multiple preprocessing techniques, image segmentation architecture (DR-UNet) with atrous spatial pyramid pooling, and an attention-aware deep learning convolutional network with different modules based on ResidualNet. Empirical results show that our framework has segmentation accuracies of 87.10% (intersection over union) and 84.50% (dice similarity coefficient). Moreover, classification performance of 99.20% provided better results than existing schemes, as reinforced by the smooth convergence of training/validation loss and accuracy. This study has the potential to supplement traditional diagnosis to identify better the ailment in its early and advanced stages.
Project Documentation: https://drive.google.com/open?id=1Yr4DsurrFAEMhxJwiuG6eZMu_-Cdeo-U
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Timestamp: 4-14-2024 11:41:03
Email Address: saikatta276@gmail.com
Name of The Student as Per SSC: KATTA SAI KRISHNA
Regd Number: 22A21F0048
Project Title: Comparative Analysis of Liver diseases by using Machine Learning Techniques
Project Abstract (only): In a human body function of the liver is important. Many persons are suffering from liver disease, but they don't know it. The identification of liver diseases in the early stage helps a patient get better treatment. If it is not diagnosed earlier, it may lead to various health issues. To solve these issues, physicians need to examine whether the patient has been affected by liver disease or not, based on the multiple parameters. In this paper, we classify the patients who have liver disease or not by using different machine learning algorithms by comparing the performance factors and predicting the better result. The liver dataset is retrieved from the Kaggle dataset.
Project Documentation: https://drive.google.com/open?id=1Pg04qQ6qPHWtiAzrS4HSeXGpRkmygAWV
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Timestamp: 4-14-2024 12:11:53
Email Address: lankaganeshsai08@gmail.com
Name of The Student as Per SSC: LANKA GANESH SAI
Regd Number: 22A21F0053
Project Title: A Machine Learning Approach for Rainfall Estimation Integrating Heterogeneous Data Sources
Project Abstract (only): Providing an accurate rainfall estimate at individual points is a challenging problem in order to mitigate risks derived from severe rainfall events, such as floods and landslides. Dense networks of sensors, named rain gauges (RGs), are typically used to obtain direct measurements of precipitation intensity in these points. These measurements are usually interpolated by using spatial interpolation methods for estimating the precipitation field over the entire area of interest. However, these methods are computationally expensive, and to improve the estimation of the variable of interest in unknown points, it is necessary to integrate further information. To overcome these issues, this work proposes a machine learning-based methodology that exploits a classifier based on ensemble methods for rainfall estimation and is able to integrate information from different remote sensing measurements. The proposed approach supplies an accurate estimate of the rainfall where RGs are not available, permits the integration of heterogeneous data sources exploiting both the high quantitative precision of RGs and the spatial pattern recognition ensured by radars and satellites, and is computationally less expensive than the interpolation methods. Experimental results, conducted on real data concerning an Italian region, Calabria, show a significant improvement in comparison with Kriging with external drift (KED), a well-recognized method in the field of rainfall estimation, both in terms of the probability of detection (0.58 versus 0.48) and mean-square error (0.11 versus 0.15).
Project Documentation: https://drive.google.com/open?id=1BMqVjdImdQ8KIkkjxsS33YHn3V9EJLmR
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Timestamp: 4-14-2024 12:24:49
Email Address: nagarajusola4402@gmail.com
Name of The Student as Per SSC: Sola Nagaraju
Regd Number: 22A21F00B3
Project Title: A peculiar image encryption technique for mobile application
Project Abstract (only): The upgradation in the field of mobile applications is predominantly increasing. Nowadays mobile applications are used in various platforms on one-handled devices in addition, attackers can use similar technology to anonymize their mali- cious behaviors and hide their identification of behaviors. Thus, security is important. In this project, we are focusing on the precautionary encryption and decryption algorithms like PNSR metric and Elliptic curve Digital signature algorithm which help us to provide secured transmission of a personal imagebetween the mobile stations. Based on these algorithms a defense application will be developed. There are 4 different levels oftechnology that will be applied in this project which help to improve security transmission. The first level is selecting a secret image. The secret image will support file types like jpg, png. In thesecond level of security, we encode the image that we get from thefirst level using an encryption algorithm. Here the image quality is measured by using PSNR metric, the third level is finding the LSB, along with 3m (Mean, Mean, Mode) of the image to hide the message inside the cover image. Then the obtained stegnographic image is compressed using GZIP is the final security level. An Elliptic curve, a Digital signature algorithm is used to enhancea security process. Therefore, this method is suggested to send a secret message through applications of special importance across the mobile application.
Project Documentation: https://drive.google.com/open?id=1eaymFO2bw8t88baNGAruLNM-yT-mr2nF
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Timestamp: 4-14-2024 13:14:30
Email Address: vineethaatkuri@gmail.com
Name of The Student as Per SSC: Atkuri Vineetha
Regd Number: 22A21F0038
Project Title: Hate speech classification on social media using a framework
Project Abstract (only): It is indeed a challenge for the existing machine learning approaches to segregate the hateful content from the one that is merely offensive. One prevalent reason for low accuracy of hate detection with the current methodologies is that these techniques treat hate classification as a multiclass problem. In this article, we present the hate identification on the social media as a multilabel problem. To this end, we propose a CNN-based service framework called “HateClassify” for labeling the social media contents as the hate speech, offensive, or nonoffensive. Results demonstrate that the multiclass classification accuracy for the CNN-based approaches particularly sequential CNN (SCNN) is competitive and even higher than certain state-of-the-art classifiers. Moreover, in the multilabel classification problem, sufficiently high performance is exhibited by the SCNN among other CNN-based techniques. The results have shown that using multilabel classification instead of multiclass classification, hate speech detection is increased up to 20%..
Project Documentation: https://drive.google.com/open?id=1mUpNa7cJyhWRxxu0Cb0TpqOh4f6n5oBm
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Timestamp: 4-14-2024 13:16:16
Email Address: leeladandu1999@gmail.com
Name of The Student as Per SSC: DANDU.LEELA
Regd Number: 22A21F0019
Project Title: REAL TIME OBJECT DETECTION USING YOLO ALGORITHM
Project Abstract (only): The Objective is to detect of objects using You Only Look Once (YOLO) approach. This method has several advantages as compared to other object detection algorithms. In other algorithms like Convolutional Neural Network, Fast Convolutional Neural Network the algorithm will not look at the image completely but in YOLO the algorithm looks the image completely by predicting the bounding boxes using convolutional network and the class probabilities for these boxes and detects the image faster as compared to other algorithms.
Project Documentation: https://drive.google.com/open?id=1ABSM5qeqQWQpS6JzeeVZXYGaws8yM0p4
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Timestamp: 4-14-2024 13:40:28
Email Address: muralikrishnavamisetti143@gmail.com
Name of The Student as Per SSC: Vamisetti Naga Murali Krishna
Regd Number: 22A21F00C1
Project Title: Prediction of Blood Lactate Levels in Children after Cardiac Surgery using Machine Learning Algorithms
Project Abstract (only): The general LHC, while measuring the basic biochemical parameters, will help to establish precisely the source of the pathology; an extended LHC that includes the LDH parameter can more precisely specify the source of pathology, its type, and cause. Lactate dehydrogenase is an oxidoreductase enzyme that catalyzes the lactic acid formation reaction during glycolysis. Like most catalysts, lactate dehydrogenase does not accumulate in cells but is evenly excreted from the body as it is formed. Laboratory blood tests are informative primary diagnostic methods. Based on their results, possible disturbances in the functioning of organs and body systems are evaluated. The aim of LDH in biochemical blood tests is to determine hematological, cardiac, muscular and ontological pathologies. A high enzyme concentration is found in the parenchyma of the liver and kidneys. It is also in the tissues of the muscular apparatus and the heart. Each region of localization has its isoenzyme. A small amount of lactate dehydrogenase is found in red blood cells. In this paper, a smart prediction of Blood Lactate Levels in Children after Cardiac Surgery has discussed using Machine Learning Algorithms. In most cases, an unsatisfactory result of a biochemical blood test for LDH is an enzyme concentration increase. It is because, with a destructive violation of the integrity of the cellular structure of an organ, a significant part of lactate dehydrogenate enters the bloodstream. A shallow enzyme level or its complete absence is observed in the degenerative stage of liver cancer and cirrhosis.
Project Documentation: https://drive.google.com/open?id=1dWls4st6fpMXHOjWH_S57vlZS5w35ZJg
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Timestamp: 4-14-2024 13:54:58
Email Address: sahithipeethaniii@gmail.com
Name of The Student as Per SSC: PEETHANI GEETHA PALLAVI
Regd Number: 22A21F00A5
Project Title: Construction Cost Index Prediction Based On Meachine Learning
Project Abstract (only): Construction projects have a great impact on the cost of construction in the process of carrying out construction, which is directly related to the quality of the construction of the entire project. Therefore, it is necessary to continuously improve the understanding of the construction industry. Machine learning is the use of the powerful computing power of computers, through the continuous update and recognition of data, so as to obtain a large amount of effective information. This paper presented a predictive analysis of construction project costs with the help of machine learning, with the aim of accurately calculating project costs and predicting construction costs. The paper used experimental design, case studies and data comparison to study the construction cost index. The experimental test results showed that the stability value reached a minimum of 87% and the stability of the system performed well in the three cases.
Project Documentation: https://drive.google.com/open?id=1ZKFSs8QU2LyPmh9yiys4nxybqDD727YH
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Timestamp: 4-14-2024 14:12:28
Email Address: happideekshi95@gmail.com
Name of The Student as Per SSC: Tummuri Durga Deekshitha
Regd Number: 22A21F00B8
Project Title: Prediction of People's Abnormal Behaviors Based on Machine Learning Algorithms
Project Abstract (only): Some improper behaviors in specific situations may put people in danger, such as smoking in a gas station, therefore they need to be detected. This paper tries to find out the best Machine Learning algorithm to address that kind of prediction problems. Datasets related to behavior detection are collected, whose categories consists of smoking, calling and normal behaviors. Experiments based on several famous algorithms are conducted, including Linear Support Vector Machine (LSVM), Kernel Support Vector Machine (KSVM), Decision Tree Classifier (DT), Random Forest Classifier (RF), K-nearest Neighbors (KNN) and K-Means Clustering. Additionally, Confusion Matrix and Mean Squared Error (MSE) are used to judge the performance of each algorithm. Finally, Principal Component Analysis (PCA) visualizes the outcome of the best algorithm. The results show that Random Forest Classifier (RF) achieves the best performance and is capable of predicting people’s abnormal behaviors with an accuracy of 82%.
Project Documentation: https://drive.google.com/open?id=1cIxrdtzSGRTuO-e3Y5RIlceOOlZ2pfwl
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Timestamp: 4-14-2024 14:35:32
Email Address: shivayarramsetti1225@gmail.com
Name of The Student as Per SSC: YARRAMSETTI NAGA SRI VENKATESWARA RAO
Regd Number: 22A21F00C8
Project Title: RESEARCH ON THE APPLICATION OF THE ARTIFICIAL INTELLIGENCEIN IN MEDICAL IMAGING DIAGNOSIS
Project Abstract (only): This paper first expounds the research status for artificial intelligence technology in medical imaging diagnosis, and illustrates the importance of computer-aided diagnosis with examples; Secondly, the current bottlenecks in the development of computer-aided diagnosis technology are analyzed in detail from the aspects of technology, industry and application; Finally, based on the previous analysis, the paper puts forward some suggestions on how to better use artificial intelligence technology in medical imaging diagnosis with reference to the current actual situations.
Project Documentation: https://drive.google.com/open?id=19Ro-Gf8rocXYxMdppS-GSei3yLfrcyYk
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Timestamp: 4-14-2024 14:46:30
Email Address: mukeshyedida1@gmail.com
Name of The Student as Per SSC: YEDIDA LAKHSMI MUKESH
Regd Number: 22A21F00D0
Project Title: Machine Learning Based House Price Prediction Using Modified Extreme Boosting
Project Abstract (only): Machine learning is increasingly vital for predicting various outcomes, such as house prices. While the House Price Index (HPI) is commonly used, it alone isn't sufficient due to factors like location and population. Conventional machine learning techniques have been used to predict house prices, but they often overlook advanced models. We propose Modified Extreme Gradient Boosting for its adaptability and model selection process. This involves steps like feature engineering, hyperparameter optimization, and model interpretation. Our project aims to develop models using machine learning to forecast home price changes, focusing on factors like location and square footage. Keywords include Home price, Location, Square footage, and Modified extreme gradient boosting.
Project Documentation: https://drive.google.com/open?id=1LkrSG2qedFmKqRhZeHr8EtZtRxn1Bqp9
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Timestamp: 4-14-2024 15:07:51
Email Address: ushasreevidivada@gmail.com
Name of The Student as Per SSC: VIDIVADA USHA SREE
Regd Number: 22A21F00C5
Project Title: BLOCK CHAIN TO SECURE CLOUD COMPUTING SERVICES USING JAVA
Project Abstract (only): The fast progress of technology has led to a rise in recent decades in privacy and cyberattack issues. This effort focuses on job in keeping and anonymity-enhancing safe cloud computing services using a blockchain named. It is developed with two features—anonymous files and searches for illegally submitted content. On, cloud users may identify all users inside the application layer and access data using payment systems. Analysis is done on how well three different implementations pure ledger, composite block chain with a cache and a convention database—perform when it comes to obtaining data. The results show that the work with the caches beats the pure network and the conventional by 50% and 53.19 percent, respectfully.
Project Documentation: https://drive.google.com/open?id=1JUBILgH2owe9ENYtixkNG5nuHq6B4axO
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Timestamp: 4-14-2024 15:21:53
Email Address: sravs123png@gmail.com
Name of The Student as Per SSC: Guttula Sravanthi
Regd Number: 22A21F0034
Project Title: B2B sales success prediction with machine learning
Project Abstract (only): The objectives of this project are two-fold: One is to use statistical modeling techniques to help a Fortune 500 paper and packaging company classify what drives sales success and second one is to develop a model that can predict sales success with a reasonable degree of accuracy. The goal is to help the company increase revenue and profits by improving sales close rates, shortening sales cycles, and reducing the cost of sales. The research team used various models to predict the likelihood of winning sales opportunities, selecting the most powerful one to serve as the basis for a client tool. They analyzed data from Salesforce.com, identifying key factors influencing sales success. The team experimented with several techniques including binomial logit and various decision tree methods, including boosting with gradient boost and random forest. The best model achieved 80% accuracy in predicting win probability, with precision and recall of 86% and 77%, respectively, surpassing current sales forecast accuracy.
Project Documentation: https://drive.google.com/open?id=1DBxA8ij1sEUYgWAgiqysglPO_pYQjLd8
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Timestamp: 4-14-2024 15:39:41
Email Address: bhavani060420@gmail.com
Name of The Student as Per SSC: Komanapalli Bhavani
Regd Number: 22A21F0080
Project Title: Carrer role prediction system using machine learning
Project Abstract (only): Most of the students across the Country are constantly in delirium about their career path after their senior secondary schooling. Mostly at the age of 18 students do not have the maturity that they should require in order to choose a right career path. Almost all the students have a series of questions and a complex and confounding thought process of which field to carry on after 12th. Also, most people have doubts about whether they have adequate skills or not. So, in this paper basically we discuss career prediction which uses basic web development consisting of an exhaustive questionnaire and machine learning approaches like Decision trees, KNN algorithm, Classification to predict a career or field which the student can pursue as per his interest.
Project Documentation: https://drive.google.com/open?id=1-R5hLJ5qDhD9s3CLj3alxwFxVehYOcmy
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Timestamp: 4-14-2024 15:53:41
Email Address: geethikarani123@gmail.com
Name of The Student as Per SSC: VATALA GEETHIKA RANI
Regd Number: 22A21F00C2
Project Title: Multiple Disease Prediction Based On User Symptoms Using Machine Learning Algorithm
Project Abstract (only): People are currently suffering from a variety of diseases. Many people are unsure if the symptoms they are experiencing are indicative of a certain disease, and hence they are unable to take the required safeguards. People will not be able to visit a doctor every time they experience a symptom. It may sometimes become a serious ailment if not treated. A model is suggested that uses a variety of symptoms as input to predict the illness. For disease prediction, the suggested method utilizes Decision trees, Naive Bayes, and Random Forest classifiers. The ultimate result will be the mode of all these machine learning models. We use supervised classification algorithms like Decision Tree, Support Vector Machine, K- Nearest Neighbors, Logistic Regression, Naive Bayes and Random Forest. This project is useful for medical care. Prediction and has the potential to improve disease diagnosis and management in the medical domain.
Project Documentation: https://drive.google.com/open?id=1W7odiwJ2mS_rTctW-o2TQC9_vqBGhkIC
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Timestamp: 4-14-2024 16:12:50
Email Address: satyendra111thumpudi@gmail.com
Name of The Student as Per SSC: THUMPUDI SATYENDRA
Regd Number: 22A21F0065
Project Title: Efficient Email phishing detection using Machine learning
Project Abstract (only): Emails are frequently utilized as a way of personal and professional communication. Banking information, credit reports, login data, and other sensitive personal information are commonly transmitted over email. This makes them valuable to cybercriminals, who can exploit the knowledge for their gain. Phishing is a technique used by con artists to steal sensitive information from people by impersonating well-known sources. The sender of a phished email can persuade you to disclose personal information under pretenses. The detection of a phished email is treated as a classification problem in this research, and this paper shows how machine learning methods are used to categorize emails as phished or not. SVM classifier attains a maximum accuracy of 0.998 percent in email classification.
Project Documentation: https://drive.google.com/open?id=1Yq14b3ERT5BYCTjGZiV1hv6t_X76AJL6
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Timestamp: 4-14-2024 16:14:15
Email Address: kalyanikhandavalli6925@gmail.com
Name of The Student as Per SSC: KHANDAVALLI KALYANI
Regd Number: 22A21F0079
Project Title: Air Pollution Detection Using Machine Learning Algorithms
Project Abstract (only): To analyze the air quality of any country, a machine learning technique is being developed and an air quality indicator is proposed for a particular area. Air Quality Index is considered to be a basic measure which can indicate the levels of SO2, NO2.etc. over a particular amount of time. We technologically put forward a model to determine the air quality index in view of historical data of preceding years and computing the same for the forthcoming year considering it as a gradient decent attached boosted multivariable regression problem. We enhance the proposed model's effectiveness by relating cost estimation on behalf of the problem to be a predictive one. Thus this proposed system resolve successfully and work well to envisage the air quality indicator of any entire country or state or any bounded region furnished with enough historical data about contaminants in air. In the proposed model, subsequently machine learning technique is assimilated, upright enactment with performance is accomplished further than the standard regression model. In this, GBR, XGB, RF and regression model algorithms are used to detect air pollution.
Project Documentation: https://drive.google.com/open?id=1TT3ekRgXhuyu7VKq4cJtmKDbBsMeOJUs
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Timestamp: 4-14-2024 16:29:49
Email Address: durgathamudu143@gmail.com
Name of The Student as Per SSC: BATTINA DURGA PRASAD
Regd Number: 22A21F0070
Project Title: Exploring Machine-learning Techniques for Early Detection of depression from social media posts
Project Abstract (only): Different social media platforms are trendy among all age groups of people. They post their daily activities regarding the things which have happened to them. People also express their feelings which can be of any kind, such as depressive, sarcastic, irony, and many more. Identifying depression from those social media posts is very difficult work. This work has collected a dataset containing depressive and non-depressive tweets from Twitter and investigated different conventional machine-learning classifiers. Among all classifiers, the Support Vector Machine (SVM) performs better than the remaining and obtained an F1-score of 0.89.
Project Documentation: https://drive.google.com/open?id=1ro5jHcsMOkfLY1U7El_MmyJxI7jRcpmz
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Timestamp: 4-14-2024 16:37:04
Email Address: abhargavsaikrishna@gmail.com
Name of The Student as Per SSC: AKKARABOTHU BHARGAV SAI KRISHNA
Regd Number: 22A21F0004
Project Title: A Novel Approach for Credit Card Fraud Detection using Decision Tree and Random Forest Algorithms
Project Abstract (only): In the world of finance, as the technology grown, new systems of business making came into picture. Credit card system is one among them. But because of lot of loop holes in this system, lot of problems are aroused in this system in the method of credit card scams. Due to this the industry and customers who are using credit cards are facing a huge loss. There is a deficiency of investigation lessons on examining practical credit card figures in arrears to privacy issues. In the manuscript an attempt has been made for finding the frauds in the credit card business by using the algorithms which adopted machine learning techniques. In this regard, two algorithms are used viz Fraud Detection in credit card using Decision Tree and Fraud Detection using Random Forest. The efficiency of the model can be decided by using some public data as sample. Then, an actual world credit card facts group from a financial institution is examined. Along with this, some clatter is supplemented to the data samples to auxiliary check the sturdiness of the systems. The significance of the methods used in the paper is the first method constructs a tree against the activities performed by the user and using this tree scams will be suspected. In the second method a user activity based forest will have constructed and using this forest an attempt will be made in identifying the suspect. The investigational outcomes absolutely show that the mainstream elective technique attains decent precision degrees in sensing scam circumstances in credit cards.
Project Documentation: https://drive.google.com/open?id=1mxnrh7yepB_EVqs9_cO_XhMHxm5WOEbu
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Timestamp: 4-14-2024 17:11:00
Email Address: pmeharteja010309@gmail.com
Name of The Student as Per SSC: PARISI MEHAR TEJA
Regd Number: 22A21F00A1
Project Title: IMAGE DATA AUDMENTATION USING DEEP LEARNING
Project Abstract (only): Deep learning has achieved remarkable results in many computer vision tasks. deep neural networks typically rely on large amounts of training data to avoid overfitting. However, labeled data for real world applications may be limited. By improving the quantity and diversity of training data, data augmentation has become an inevitable part of deep learning model training with image data
Project Documentation: https://drive.google.com/open?id=1Q6N-7pNEMtaEaZkP0sFhMXIl5nd0WIxZ
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Timestamp: 4-14-2024 17:32:39
Email Address: pavankumargandham9@gmail.com
Name of The Student as Per SSC: GANDAM PAVAN KUMAR
Regd Number: 22A21FOO72
Project Title: An Enhanced Stress Based Hair fall Detection and Prevention Using KNN and Machine Learning Techniques
Project Abstract (only): Numerous factors might affect a person's stress level, which results in hair loss. Due to variables such as increased employee dominance, job pressure, and work overload, among others employees in IT sectors are more prone to experience stress. Depression, anxiety, somatization, and attention deficit disorder are just a few of the mental health issues that stress can lead to, and even mortality. As a result, it's critical to recognize human stress early so that the proper treatments may be given and tension can be reduced. Numerous studies have been conducted on stress prediction. An extension of the skin, hair is an essential component of a person's facial beauty. The outcomes of some learning algorithms, like KNN, are superior. Other intelligent methods such as ML algorithms can be used to diagnose the diseases.
Project Documentation: https://drive.google.com/open?id=1t7edb6wc_Zq6-H6F4VhhanLPQrI_fKPD
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Timestamp: 4-14-2024 18:12:54
Email Address: kavuruyamini078@gmail.com
Name of The Student as Per SSC: KOWRU YAMINI
Regd Number: 22A21F0049
Project Title: Students Performance Prediction in Online Courses Using Machine Learning Algorithms
Project Abstract (only): Advances in Information and Communications Technology (ICT) have increased the growth of Massive open online courses (MOOCs) applied in distance learning environments. Various tools have been utilized to deliver interactive content including pictures, figures, and videos that can motivate the learners to build new cognitive skills. High ranking universities have adopted MOOCs as an efficient dashboard platform where learners from around the world can participate in such courses. The students learning progress is evaluated by using set computer marked assessments. In particular, the computer gives immediate feedback to the student once he or she completes the online assessments. The researchers claim that student success rate in an online course can be related to their performance at the previous session in addition to the level of engagement. Insufficient attention has been paid by literature to evaluate whether student performance and engagement in the prior assessments could affect student achievement in the next assessments. In this paper, two predictive models have been designed namely students’ assessments grades and final students’ performance. The models can be used to detect the factors that influence students’ learning achievement in MOOCs. The result shows that both models gain feasible and accurate results. The lowest RSME gain by RF acquire a value of 8.131 for students assessments grades model while GBM yields the highest accuracy in final students’ performance, an average value of 0.086 was achieved.
Project Documentation: https://drive.google.com/open?id=1AyA1TQtu58hWKnqS-E3NjLRoOuABmfUR
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Timestamp: 4-14-2024 18:19:11
Email Address: srinivasanbhuvana7@gmail.com
Name of The Student as Per SSC: SRINIVASAN BHUVANESWARI
Regd Number: 22A21F00B4
Project Title: Design of Intelligent Medical Integrity Authentication and Secure Information for Public Cloud in Hospital Administration
Project Abstract (only): Due to political and financial considerations, large hospitals are also less likely to share their patient information with outside healthcare providers. To get around the barriers that prevent an efficient process of exchanging medical data. The integrated computerized clinical information system is part of the Hospital Information System (HIS), which aims to improve hospital operations and clinical management. Furthermore, the patient has access to an accurate electronic medical record that has been stored. For research and statistical applications, such records can be utilized in a data warehouse. The architecture of a centralized information system, on which HIS was established intended for the rapid transmission of both operational and administrative information. It would be difficult and It requires a lot of money and resources to set up an independent information management system for a small village hospital. The hospital information system in use presently, information is only shared within the same hospital. The theory of cloud computing serves as the proposal's basis. The "cloud" makes it possible for greater analysis, sharing, and exchange of medical data from images. Doctors may be able to get the data they need due to cloud-based medical image storage, patient will be able to get treatment across hospital departments automating the management of hospital information and computational resources. Hence, this system develops of intelligent medical integrity authentication and it is more effective for hospital administration to use secure information on public clouds, low-cost and time saving.
Project Documentation: https://drive.google.com/open?id=14QTdk21RZPClS5dDN2HMPCeaAhj-eFIa
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Timestamp: 4-14-2024 18:22:55
Email Address: maheshjakkamsetti68@gmail.com
Name of The Student as Per SSC: JAKKAMSETTI MAHESH
Regd Number: 22A21Foo39
Project Title: FINDING PSYCHOLOGICAL INSTABILITY USING MACHINE LEARNING
Project Abstract (only): As we know that people around the globe work hard to keep up with this racing world. However, due to this each individual is dealing with different health issues, one of the most known issue is depression or stress which may eventually lead to death or other brutal activities. These abnormalities can be termed as the Bipolar disorder which can be treated by undergoing some treatment suggested by specialists. For this research, data has been collected from working people which comprises of all kinds of questions for despondent detection and the dataset has been run through some machine learning algorithms. Random Forest algorithm gives the highest accuracy as 87.02% compared to the other algorithms.
Project Documentation: https://drive.google.com/open?id=1DRobtU-uUKUbLY3z6TbTqOWn11Srzw1y
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Timestamp: 4-14-2024 18:30:46
Email Address: hrudayaramu51@gmail.com
Name of The Student as Per SSC: GUNDUMOGULA HRUDAYA RAMU
Regd Number: 22A21F0029
Project Title: SUICIDAL IDEATION DETECTION: USING MACHINE LEARNING METHODS AND APPLICATIONS
Project Abstract (only): Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people’s life. Current suicidal ideation detection methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This paper is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of suicidal ideation detection are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and datasets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.
Project Documentation: https://drive.google.com/open?id=1rrQhbqCBXda7QbtR1GvzU_6s_4Lmuo8_
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Timestamp: 4-14-2024 18:40:09
Email Address: mahimkumar8@gmail.com
Name of The Student as Per SSC: ELIPAY MAHIM KUMAR
Regd Number: 22A21F0023
Project Title: Malware Detection: A Framework for Reverse Engineered Android Applications through Machine Learning Algorithms
Project Abstract (only): Today, Android is one of the most used operating systems in smartphone technology. This is the main reason, Android has become the favorite target for hackers and attackers. Malicious codes are being embedded in Android applications in such a sophisticated manner that detecting and identifying an application as a malware has become the toughest job for security providers. In terms of ingenuity and cognition, Android malware has progressed to the point where they're more impervious to conventional detection techniques. Approaches based on machine learning have emerged as a much more effective way to tackle the intricacy and originality of developing Android threats. They function by first identifying current patterns of malware activity and then using this information to distinguish between identified threats and unidentified threats with unknown behavior. This research paper uses Reverse Engineered Android applications’ features and Machine Learning algorithms to find vulnerabilities present in Smartphone applications. Our contribution is twofold. Firstly, we propose a model that incorporates more innovative static feature sets with the largest current datasets of malware samples than conventional methods. Secondly, we have used ensemble learning with machine learning algorithms such as AdaBoost, SVM, etc. to improve our model's performance. Our experimental results and findings exhibit 96.24% accuracy to detect extracted malware from Android applications, with a 0.3 False Positive Rate (FPR). The proposed model incorporates ignored detrimental features such as permissions, intents, API calls, and so on, trained by feeding a solitary arbitrary feature, extracted by reverse engineering as an input to the machine.
Project Documentation: https://drive.google.com/open?id=1IAQ17Ie83AuQ3XTkLaFyBg9oNT8aIo_L
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Timestamp: 4-14-2024 18:42:02
Email Address: satyadeepikagandi347@gmail.com
Name of The Student as Per SSC: Gandi satya deepika
Regd Number: 22A21F0024
Project Title: Machine Learning for Fast and Reliable Source-Location Estimation in Earthquake Early Warning
Project Abstract (only): We develop a random forest (RF) model for rapid earthquake location with an aim to assist earthquake early warning (EEW) systems in fast decision making. This system exploits P-wave arrival times at the first five stations recording an earthquake and computes their respective arrival time differences relative to a reference station (i.e., the first recording station). These differential P-wave arrival times and station locations are classified in the RF model to estimate the epicentral location. We train and test the proposed algorithm with an earthquake catalog from Japan. The RF model predicts the earthquake locations with a high accuracy, achieving a Mean Absolute Error (MAE) of 2.88 km. As importantly, the proposed RF model can learn from a limited amount of data (i.e., 10% of the dataset) and much fewer (i.e., three) recording stations and still achieve satisfactory results (MAE<5 km). The algorithm is accurate, generalizable, and rapidly responding, thereby offering a powerful new tool for fast and reliable source-location prediction in EEW.
Project Documentation: https://drive.google.com/open?id=1Jwi-t8CgsKZmRfnleDTGobUqrmySwGDE
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Timestamp: 4-14-2024 18:44:18
Email Address: sridivyajogi2000@gmail.com
Name of The Student as Per SSC: JOGI.SRI DIVYA
Regd Number: 22A21F0042
Project Title: chronic kidney disease stage identification in HIV infected patients using machine learning
Project Abstract (only): chronic kidney disease is one of worldwide medical challenges with high morbidity and death rate.since there is no symptom during the early stages of ckd. patients often fail to diagnose the disease. patients with HIVhave more chances to beaffected with CKD in critical condition. early detection of CKD helps patient to obtain prompt care ald delays the further progression of disease. with the availability of pathology data, the use of machine learning techniques in healthcare for classification and prediction of disease has become more common. based on the glomerular filtration rate the ckd stage are also calculated for patients diagonsed with ckd. DNN model outer performs with 99% of accuracy in classifying ckd patient with HIV.
Project Documentation: https://drive.google.com/open?id=1dJO1JkySZRAu7adOxiLg6_2L-oic5ZnD
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Timestamp: 4-14-2024 18:51:01
Email Address: basavaprasanna2001@gmail.com
Name of The Student as Per SSC: BASAVA DEVI LAKSHMI PRASANNA
Regd Number: 22A21F0007
Project Title: Enabling Efficient Secure and privacy Preserving Mobile Cloud Storage
Project Abstract (only): Our research introduces an innovative Mobile Cloud Storage (MCS) approach prioritizing efficiency, security, and privacy, particularly safeguarding data confidentiality and access patterns. We propose the Oblivious Selection and Update (OSU) protocol, leveraging onion additively homomorphic encryption for data retrieval and updates in an oblivious manner, reducing client-side computation and communication overheads. With fine-grained data structures, lightweight client-side computation, and constant communication overhead, our scheme offers advantages over previous methods, well-suited for MCS environments. Incorporating a "verification chunks" technique enhances defense against malicious cloud activities. Comparative analysis demonstrates superior performance in client and cloud workloads compared to existing solutions.
Project Documentation: https://drive.google.com/open?id=1uNDKGSRWP5W8zy1_bssvVVWEqJApAqEg
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Timestamp: 4-14-2024 19:36:33
Email Address: danieleda924@gmail.com
Name of The Student as Per SSC: DANIEL JOQUIS EDA
Regd Number: 22A21F0020
Project Title: Detection of Cyberbullying on Social Media Using Machine Learning
Project Abstract (only): Cyberbullying is a major problem encountered on internet that affects teenagers and also adults.It has lead to mishappenings like suicide and depression. Regulation of content on Social media platorms has become a growing need. The following study uses data from two different forms of cyberbullying, hate speech tweets from Twittter and comments based on personal attacks from Wikipedia forums to build a model based on detection of Cyberbullying in text data using Natural Language Processing and Machine learning. Three methods for Feature extraction and four classifiers are studied to outline the best approach. For Tweet data the model provides accuracies above 90% and for Wikipedia data it gives accuracies above 80%. Sites for social networking are excellent tools for communication within individuals. Use of social networking has become widespread over the years, though, in general people find immoral and unethical ways of negative stuff. We see this happening between teens or sometimes between young adults. One of the negative stuffs they do is bullying each other over the internet. In online environment we cannot easily said that whether someone is saying something just for fun or there may be other intention of him.
Project Documentation: https://drive.google.com/open?id=1OPRt6mAJ5dGWL_PoqTjQGyOW4u-nuw3-
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Timestamp: 4-14-2024 19:40:01
Email Address: sukanyalotti@gmail.com
Name of The Student as Per SSC: LOTTI SUKANYA
Regd Number: 22A21F0089
Project Title: YOGA POSE DETECTION AND CLASSIFICATION USING ML TECHNIQUES
Project Abstract (only): In recent years yoga has become part of many people in the world. If performed without guidance it may cause some injuries. Here we develop a system that identifies different yoga poses performed by users. For detection of yoga poses in any system human pose estimation is required. The system uses open-source data containing 7 different yoga poses. The system has two phases first to extract the data points data from the video dataset using the media pipe pose estimation library and the second phase is preprocessing the obtained data, training, and testing the data using classification-based machine learning algorithms. The machine learning algorithm used is logistic regression, support vector machine classifier, random forest classifier and k nearest neighbors classifier. The system is developed to work on images, static videos, and live videos.
Project Documentation: https://drive.google.com/open?id=11142Yunnj87bftpxJdMZ1e4nGIYfWhX2
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Timestamp: 4-14-2024 19:46:04
Email Address: ashalahari95@gmail.com
Name of The Student as Per SSC: BUSI ASHA KUMARI
Regd Number: 22A21F0012
Project Title: A STUDENT ATTENDANCE MANAGEMENT METHOD BASED ON CROWDSENSING IN CLASSROOM ENVIRONMENT
Project Abstract (only): In smart cities intelligent learning environment is an important application scenario and class attendance checking is an important measure to urge students and class attend on time and ensure the quality of learning aiming at the existing problems in class attendance checking, such as low efficiency and easy to cheat this paper proposes a student attendance management method named AMMOC(attendance management method based on crowdsensing) AMMOC includes two phases i, e. initialization phase and the authentication phase.in the initialization phase, a teacher sends an attendance checking request to the server.in the authentication phase the server verifies the truth of the location information by sending requests to several students to count the no of students.
Project Documentation: https://drive.google.com/open?id=1OnP2Q5hsBnCgvjve25l1zAxViKBZgF3Y
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Timestamp: 4-14-2024 19:55:55
Email Address: klnkishore06@gmail.com
Name of The Student as Per SSC: KRUTHIVENTI LAKSHMI NARASIMHA KISHORE
Regd Number: 22A21F0085
Project Title: Human Disease Prediction based on Symptoms
Project Abstract (only): There are new diseases discovered in the 21st century which have identical and additional symptoms to the previous diseases but these new diseases are far more dangerous than the previous ones and have some additional symptoms. S o, In this work the new diseases can be identified by taking the input from the user and identifying the disease and displaying it on the users smart screen display. This work aim is to reduce the deaths of people by identifying the disease and cure it in advance. The Random Forest and Naive Bayes algorithms are used for predicting the disease. This prediction is done by considering and comparing the accuracies of both the algorithms and gives the predicted disease as output which has best accuracy. Its implementation is completed via python programming language and tkinter library..
Project Documentation: https://drive.google.com/open?id=1k4rsJqDbWz1U3HnuvQMgTJrhNRLNODWv
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Timestamp: 4-14-2024 20:10:26
Email Address: purni.chelamkuri@gmail.com
Name of The Student as Per SSC: Chelamkuri Purnima Surya Sai Mangatayaru
Regd Number: 22A21F0015
Project Title: Fraud Detection In Online Product Reviews system Via Heterogeneous Graph Transformer
Project Abstract (only): In online product review systems, users are allowed to submit reviews about their purchased items or services. However, fake reviews posted by fraudulent users often mislead consumers and bring losses to enterprises. Traditional fraud detection algorithm mainly utilizes rule-based methods, which is insufficient for the rich user interactions and graph-structured data. In recent years, graph-based methods have been proposed to handle this situation, but few prior works have noticed the camouflage fraudster’s behavior and inconsistency heterogeneous nature. Existing methods have either not addressed these two problems or only partially, which results in poor performance. Alternatively, we propose a new model named Fraud Aware Heterogeneous Graph Transformer (FAHGT), to address camouflages and inconsistency problems in a unified manner. FAHGT adopts a type-aware feature mapping mechanism to handle heterogeneous graph data, then implementing various relation scoring methods to alleviate inconsistency and discover camouflage. Finally, the neighbors features are aggregated together to build an informative representation. Experimental results on different types of real-world datasets demonstrate that FAHGT outperforms the state-of-the-art baselines.
Project Documentation: https://drive.google.com/open?id=1uOrbrQzdJCSW3A4GeKnbYn054It_0M2-
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Timestamp: 4-14-2024 20:46:55
Email Address: pathakotasindhu01@gmail.com
Name of The Student as Per SSC: PATHAKOTA SINDHU
Regd Number: 22A21F00A2
Project Title: Cloud Data Deduplication Scheme Based on BlockChain
Project Abstract (only): To address the problem of untrustworthiness among entities faced in the process of data deduplication in cloud storage environment, this paper proposes a cloud data deduplication scheme based on blockchain. Firstly, to ensure the trustworthiness of data ownership proof in the data deduplication process, a data ownership verification algorithm based on random location sampling is designed with the help of blockchain technology and Merkle hash tree; Secondly, an oblivious pseudo-random protocol is used to obtain convergent encryption key to facilitate deduplication by cloud servers provides. Finally, the scheme security is proved by conducting security analysis, while simulation experiments are conducted to verify the effectiveness of the scheme. With the rapid development of cloud computing and widespread popularity of smart mobile devices, more and more people store their data in the cloud. On the one hand, cloud computing has massive storage space, which can effectively relieve the storage space of local hardware. on the other hand, users could use their data anytime and anywhere with mobile terminals. In recent years, according to an IDC report [1], the data stored in the cloud will be expected to reach 44ZT in 2025. Storing such a tremendous amount of data is a severe challenge for cloud service providers. Moreover, there is much redundancy in such massive data, which seriously reduces the utilization of cloud storage servers. To effectively save storage space and improve storage server utilization, many cloud service providers, such as Google, Amazon, and Dropbox use data deduplication technology.
Project Documentation: https://drive.google.com/open?id=1iXqAjEXhIzEaIpM88LqStKTz0Uluyby2
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Timestamp: 4-14-2024 20:47:02
Email Address: saidurgajavvadi8@gmail.com
Name of The Student as Per SSC: JAVVADI SAIDURGA
Regd Number: 22A21F0076
Project Title: early risk prediction of cervical cancer using machine learning
Project Abstract (only): Cervical cancer is a vital public health issue that affects women worldwide. As it is a fatal disease, early risk prediction of cervical cancer can play an important role in prevention by raising public awareness of this disease. Early prediction using a Machine Learning (ML) model can be a beneficial solution for both healthcare professionals and people at risk. In this study, eleven supervised ML algorithms are utilized to forecast early jeopardies of this disease using a dataset from UCI ML repository. The ML models are rummaged to prophesy the early threats, and performance parameters like accuracy, precision, F1-score, re-call, and ROC-AUC are estimated. Finally, a reasonable analysis is performed, revealing that this study achieved 93.33% prediction accuracy with Multi-Layer Perceptron (MLP) algorithm with default hyperparameters. However, employing the hyperparameter tuning method with Grid Search Cross Validation (GSCV), K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Support Vector Machine (SVM), Random Forest Classifier (RFC), and Multi-Layer Perceptron (MLP) all portrayed accuracy of 93.33%.
Project Documentation: https://drive.google.com/open?id=1cHGwrweCcDEHMz-NkJlUR3K9EiIpCo1U
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Timestamp: 4-14-2024 20:47:04
Email Address: maddilapavankumar09@gmail.com
Name of The Student as Per SSC: MADDILA PAVAN KUMAR
Regd Number: 22A21F0090
Project Title: Cotton leaf Disease Prediction using Convolutional Neural Network (CNN)
Project Abstract (only): Deep learning is a subset of artificial intelligence. It's a form of artificial intelligence and machine learning that attempts to simulate the way humans pick up specific types of information. The goal of this project is to create a deep learning model based on convolutional neural networks that can distinguish between healthy and diseased leaves. Due to its useful features in learner autonomy and extraction of features, it has drawn a great deal of attention in past years from researchers and industry professionals alike. Images of healthy and rotting leaves are included in the dataset. It is widely used in fields such as computational linguistics, voice processing, image processing, and video processing. It has also become a center for studies on agricultural plant protection, such as the detection of plant diseases and the assessment of pest ranges. This study has also discussed about some of the problems and issues that are currently being faced and need to be addressed. Library packages such as KERAS, MATPLOTLIB, NUMPY, and OPENCV have been utilized here.
Project Documentation: https://drive.google.com/open?id=14SPLRDcVibkT7r4yVU2JghNGc0XF6xVY
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Timestamp: 4-14-2024 20:51:24
Email Address: venumadhaviyalla1@gmail.com
Name of The Student as Per SSC: VENU MADHAVI YALLA
Regd Number: 22A21F00C6
Project Title: EARLY DIAGNOSIS FOR DENGUE DISEASE PREDICTION USING MACHINE LEARNING TECHNIQUES
Project Abstract (only): Dengue fever is a worldwide issue, especially in Yemen. Although early detection is critical to reducing dengue disease deaths, accurate dengue diagnosis requires a long time due to the numerous clinical examinations. Thus, this issue necessitates the development of a new diagnostic schema. The objective of this work is to develop a diagnostic model for the earlier diagnosis of dengue disease using Efficient Machine Learning Techniques (EMLT). This paper proposed prediction models for dengue disease based on EMLT using Decision tree classifier, Random forest classifier. All classifiers are trained and tested on the dataset using 10-Fold Cross Validation . On a test set, all models were evaluated using different metrics: accuracy, F1-sore, Recall, Precision. In cross-validation approach, we conclude that the best classifier with high accuracy is Decision tree classifier, which achieved 99.12 %. Accordingly, the proposed dengue prediction system demonstrates its efficacy and effectiveness in assisting doctors in accurately predicting dengue disease.
Project Documentation: https://drive.google.com/open?id=1nCr6ixP338AUhH02iTW9S2JjfS4Z0O2l
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Timestamp: 4-14-2024 21:12:53
Email Address: akhilavelagana@gmail.com
Name of The Student as Per SSC: Velagana Akhilandeswari
Regd Number: 22A21F00C3
Project Title: Cancer Death Cases Forecasting Using Supervised Machine Learning
Project Abstract (only): In India, like in the rest of the world, cancer is a major killer. This research objective is to predict cancer mortality in India, using supervised machine learning methods. Cancer mortality rates in India between 1990 and 2017 are provided by age group, gender, and region using data from the Global Burden of Disease Study. We employ three distinct supervised learning algorithms—linear regression, decision tree regression, and random forest regression—after performing data preprocessing, which includes missing value imputation and feature engineering. Using a variety of criteria, we analyze the effectiveness of these models and conclude that the random forest regression model is superior to the other two. The scope of research is provide a long-term prediction of cancer mortality in India using the best model so it will help health department to work on it. Our research has implications for policymakers and healthcare providers in India, where it may inform efforts to reduce cancer rates and improve cancer care.
Project Documentation: https://drive.google.com/open?id=1fagZrLkQ3QEAhGS37cgySkZ8q2WiomJz
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Timestamp: 4-14-2024 21:27:31
Email Address: sirishanunna04@gmail.com
Name of The Student as Per SSC: NUNNA TULASI SIRISHA
Regd Number: 22A21F00A0
Project Title: Brain Disease Age Estimation Using Deep Learing Alogorithms
Project Abstract (only): Deep neural networks preserve effectively predict chronological age about healthy individuals from neuroimaging data, & predicted brain age may be used as a biomarker towards identify aging-related disorders. Convolutional Neural Network (CNN), a deep learning cascade network, & Support Vector Ma-chine, a machine learning algorithm, are therefore used in proposed method (SVM). These algorithms have been used towards train brain MRI scans certain have been divided into three classes: Normal, which has not been affected through any disease, Alzheimer's disease (AD), & Mild Cognitive Impairment (MCI). & from categorized photographs and determine ages. MRI image dataset is trained primarily using CNN & SVM, & it will be utilized towards do classification & age estimate.
Project Documentation: https://drive.google.com/open?id=1tn-dSH04PMWpiJtlE0uABXltyEnSVomJ
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Timestamp: 4-14-2024 21:30:53
Email Address: jagadeeswarimandapaka999@gmail.com
Name of The Student as Per SSC: Mandapaka Jagadeeswari
Regd Number: 22A21F0054
Project Title: Exploring Machine Learning Algorithms To Find Best Features For Predicting Modes Of Child Birth
Project Abstract (only): The prediction of delivery modalities is critical for improving mother and fetal health outcomes. Machine learning systems have demonstrated promising accuracy in predicting birthing styles. Identifying the most important features for this prediction assignment, on the other hand, remains a challenge..The five different machine learning methods were investigated in order to discover the most significant algorithm for prediction based on 6157 birth data anda minimal set of characteristics. The study discovered 32 variables that might be used to predict modes of birthing and classified them into distinct groups based on their relevance
Project Documentation: https://drive.google.com/open?id=1ulbrztQgMhNIjJssumOfIUgivx9VEnqB
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Timestamp: 4-14-2024 21:31:18
Email Address: kalyankumar4203@gmail.com
Name of The Student as Per SSC: Miriyala Kalyan Kumar
Regd Number: 22A21F0094
Project Title: An Artificial Intelligence System for Detecting the Types of the Epidemic from X-rays
Project Abstract (only): Since the beginning of the COVID-19 pandemic, many lives have been in danger. The visual geometry group network (VGGNet) is used in this research as a model to identify epidemic types. The dataset consisted of 12068 chest X-ray images extracted from the Kaggle website and evaluated in 4 classes: Pulmonary tuberculosis, normal lung, pneumonia, and Covid 19. We have used the VGGNet architecture to diagnose and classify the mentioned disease using the chest X-ray images. To assess the performance of these classes, the parameters such as accuracy, specificity, and sensitivity are measured. Regarding the measured parameters, the accuracy, specificity, and sensitivity values were 0.97, 0.96, and 0.98, respectively. This system can differentiate among these diseases by accurately diagnosing differences in patients' X-ray images. The results showed that the VGG16 model could be more effective than VGG19 in diagnosing epidemics. The VGG16 based technique can facilitate the rapid diagnosis of patients and increase their chances of recovery. The findings also showed that the proposed model based on chest X-ray images is more accurate, simpler, and less expensive than computed tomography (CT) images.
Project Documentation: https://drive.google.com/open?id=10jaxr6duSMDIA3gt9yHUiddmRm9aqPpa
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Timestamp: 4-14-2024 21:34:15
Email Address: satyasreeg2002@gmail.com
Name of The Student as Per SSC: Guttula Venkata Satyasree
Regd Number: 22A21F0035
Project Title: A comparative study on fake job post prediction using different data mining techniques
Project Abstract (only): In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posing prediction leaves a lot of challenges to face. This paper proposed to use different data mining techniques and classification algorithm like KNN, decision tree, support vector machine, naive bayes classifier, random forest classifier, multilayer perceptron and deep neural network to predict a job post if it is real or fraudulent. We have experimented on Employment Scam Aegean Dataset (EMSCAD) containing 18000 samples. Deep neural network as a classifier, performs great for this classification task. We have used three dense layers for this deep neural network classifier. The trained classifier shows approximately 98% classification accuracy (DNN) to predict a fraudulent job post
Project Documentation: https://drive.google.com/open?id=1aYLmCEScX8MVdTE6rGhtdrawLxwTR_0F
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Timestamp: 4-14-2024 21:45:14
Email Address: rajabhanu1234@gmail.com
Name of The Student as Per SSC: RAJA BHANU SWETHA
Regd Number: 22A21F00B1
Project Title: Air Quality Prediction Based On Machine Learning p
Project Abstract (only): One major basic right is clean air that is integral to the concept of citizenship and it's while not a doubt, the responsibility of every subject to try to do his/her half to stay the air clean. Air quality prognostication has been looked into because the key answer of early warning and management of pollution. During this paper, we tend to propose an associate in nursing air quality prediction system supported by a machine learning framework known as the sunshine GBM model, to predict air quality. This model, trained victimization lightweight GBM classifier, take meteorology knowledge jointly of sources for predicting the air quality thereby increasing the prediction accuracy by creating full use of obtainable abstraction data. the prevailing air quality observance stations and satellite meteorologic knowledge offer period air quality observance info that is employed to predict the trend of air pollutants within the future. The projected system was found to administer associate in nursing accuracy of ninety-two.
Project Documentation: https://drive.google.com/open?id=1O_uaPNo9MjmK5iV3IobLjFnTY0_bvI-3
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Timestamp: 4-14-2024 21:56:03
Email Address: karrihemanth4@gmail.com
Name of The Student as Per SSC: karri Naga Hemanth Kumar
Regd Number: 22A21F0045
Project Title: Personalized Adaptive Learning Technologies Using Machine Learning
Project Abstract (only): Artificial intelligence (AI) approaches have been used in personalized adaptive education systems to overcome the limitations of statically determined learning styles (LSs). These approaches utilize algorithms from machine learning (ML) to tackle the challenge of personalizing e-learning by mapping students’ behavioral attributes to a particular LS automatically and dynamically to optimize the individual learning process. Motivated by the many influential studies in this field and the current developments in ML and AI, a comprehensive systematic literature review was conducted from 2015 to 2022. Influential scientific literature was analyzed to identify the emerging trends and gaps in the literature in terms of LS models and possible ML techniques employed for personalized adaptive learning platforms. The outcomes of this paper include a review and analysis of the current trends of this emerging field in terms of the applications and developments in using ML approaches to implement more intelligent and adaptive e-learning environments to detect learners’ LSs automatically for enhancing learning. In addition, the following issues were also investigated: the platforms that stimulated research; identifying LS models utilized in e-learning; the evaluation methods used; and the learning supports provided. The results indicated an increasing interest in using artificial neural network approaches to identify LSs. However, limited work has been conducted on the comparison of deep learning methods in this context. The findings suggest the need to consider and stimulate further empirical investigation in documenting the adoption and comparison of deep learning algorithms in classifying LSs to provide higher adaptability.
Project Documentation: https://drive.google.com/open?id=1VYdYvXCpQ6zl4CRCedctSg8nVPwxiqLR
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Timestamp: 4-15-2024 0:24:51
Email Address: balub9144@gmail.com
Name of The Student as Per SSC: BODDANI BALAKRISHNA
Regd Number: 22A21F0008
Project Title: A Multi Stream Future Fusion Approach For Traffic Prediction
Project Abstract (only): Accurate and timely traffic flow prediction is crucial for intelligent transportation systems (ITS). Recent advances in graph-based neural networks have achieved promising prediction results. However, some challenges remain, especially regarding graph construction and the time complexity of models. In this paper, we propose a multi-stream feature fusion approach to extract and integrate rich features from traffic data and leverage a data-driven adjacent matrix instead of the distance-based matrix to construct graphs. We calculate the Spearman rank correlation coefficient between monitor stations to obtain the initial adjacent matrix and fine-tune it while training. As to the model, we construct a multi-stream feature fusion block (MFFB) module, which includes a three-channel network and the soft attention mechanism. The three-channel networks are graph convolutional neural network (GCN), gated recurrent unit (GRU) and fully connected neural network (FNN), which are used to extract spatial, temporal and other features, respectively. The soft-attention mechanism is utilized to integrate the obtained features. The MFFB modules are stacked, and a fully connected layer and a convolutional layer are used to make predictions. We conduct experiments on two real-world traffic prediction tasks and verify that our proposed approach outperforms the state-of-the-art methods within an acceptable time complexity.
Project Documentation: https://drive.google.com/open?id=1yajlqSet3CS-t0xG3lRpYMMlnM7ssv17
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Timestamp: 4-15-2024 0:57:01
Email Address: manikantaguttula90@gmail.com
Name of The Student as Per SSC: GUTTULA MANIKANTA
Regd Number: 22A21F0032
Project Title: Predicting Urban Water Quality With Ubiquitous Data
Project Abstract (only): Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. However, predicting the urban water quality is a challenging task since the water quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, water usage patterns, and land uses. In this work, we forecast the water quality of a station over the next few hours from a data-driven perspective, using the water quality data and water hydraulic data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, pipe networks, structure of road networks, and point of interests (POIs). First, we identify the influential factors that affect the urban water quality via extensive experiments. Second, we present a multi-task multi-view learning method to fuse those multiple datasets from different domains into an unified learning model. We evaluate our method with real-world datasets, and the extensive experiments verify the advantages of our method over other baselines and demonstrate the effectiveness of our approach.
Project Documentation: https://drive.google.com/open?id=1wo3oWntNevsrsOiHcVcgFLqFHOWJxEfu
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Timestamp: 4-15-2024 11:38:57
Email Address: aesrp44@gmail.com
Name of The Student as Per SSC: ADDALA EDUKONDALA SAIRAMA PAVAN
Regd Number: 22A21F0002
Project Title: Train Time Delay Prediction for High-Speed Train Dispatching Based on Spatio-Temporal Graph Convolutional Network
Project Abstract (only): Train delay prediction can improve the quality of train dispatching, which helps the dispatcher to estimate the running state of the train more accurately and make reasonable dispatching decision. The delay of one train is affected by many factors, such as passenger flow, fault, extreme weather, dispatching strategy. The departure time of one train is generally determined by dispatchers, which is limited by their strategy and knowledge. The existing train delay prediction methods cannot comprehensively consider the temporal and spatial dependence between the multiple trains and routes. In this paper, we don’t try to predict the specific delay time of one train, but predict the collective cumulative effect of train delay over a certain period, which is represented by the total number of arrival delays in one station. We propose a deep learning framework, train spatio-temporal graph convolutional network (TSTGCN), to predict the collective cumulative effect of train delay in one station for train dispatching and emergency plans. The proposed model is mainly composed of the recent, daily and weekly components. Each component contains two parts: spatio-temporal attention mechanism and spatio-temporal convolution, which can effectively capture spatio-temporal characteristics. The weighted fusion of the three components produces the final prediction result. The experiments on the train operation data from China Railway Passenger Ticket System demonstrate that TSTGCN clearly outperforms the existing advanced baselines in train delay predictions.
Project Documentation: https://drive.google.com/open?id=1eFuuoqj4IHFXvgLliKmbp6gIeSFB5lGt
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Timestamp: 4-15-2024 12:00:29
Email Address: marymary54461@gmail.com
Name of The Student as Per SSC: PITTA MARY FLORENCE
Regd Number: 22A21F0061
Project Title: EFFECT OF E-LEARNING ON STUDENT HEALTH DURING COVID-19 LOCKDOWN
Project Abstract (only): E-learning is the promising venture in the entire world. During the COVID-19 lockdown-learning successfully providing potential information to the students and researchers . in developing nation like India with limitted resource, e-learning tools and platforms provide a chance to make education available to middle and low class house holds. This papers give incite about three different online services like google classroom , zoom, Microsoft team being used three different educational institutions we aim to analyze the efficiency and acceptability of e-learning tools among Indian students during covid19 lockdown. the paper also aim to evaluate the impact of e-learning on student health during covid 19 lockdown, however the mental health is impacted as e-learning may leads to self isolation and reduce in academic achievements that may leads to anxiety and mental depression, due to uses of electronic devices for learning, the eyes and neck muscles may put in strain , having deleterious effect on physical health
Project Documentation: https://drive.google.com/open?id=1EGfAPj5fguOl7kRLGmsNn2RJ2HsB89PT
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Timestamp: 4-15-2024 12:09:57
Email Address: srinubalaji66@gmail.com
Name of The Student as Per SSC: VELIDI SRINU BALAJI
Regd Number: 22A21F00C4
Project Title: Will the Student Get an A Grade Machine Learning based Student Performance Prediction
Project Abstract (only): As an important component in the structure of smart campus, student performance prediction can help to observe the academic progress of students and make timely decisions towards improving the overall learning process. In order to achieve accurate performance prediction, it is required to extract useful data such as the past performance of students from the student information system and employ the extracted data to train an appropriate machine learning model. In this paper, different machine learning models are utilized to predict whether or not a student will get an A grade (i.e, 90 or higher). The work utilizes a real dataset that is extracted from three different courses that require computer and programming skills. The dataset includes three features about the student past performance, namely, high school grade, course midterm grade, and absence percentage. The dataset is then used to train different machine learning models, specifically, linear discriminant, logistic regression, Naive Bayes, support vector machine, decision tree, K-nearest neighbors, and bagged trees. In order to highlight the effectiveness of these classifiers, different metrics were used to evaluate the classification performance such as accuracy, precision, recall, and F1-score. Besides, these models are tested considering one, two, and three features from the dataset to evaluate the significance of each feature in the classification process. After comparing the performance of these machine learning models, it is shown that predicting student performance is indeed applicable with an accuracy that reached 99%. It is also shown that the bagged trees and K nearest neighbors succeeded to achieve the highest classification accuracy compared to the other models.
Project Documentation: https://drive.google.com/open?id=1E758TT19sR7cf-J2MV4RS34Bb1BiwFtP
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Timestamp: 4-15-2024 12:14:20
Email Address: pvenkannababu131@gmail.com
Name of The Student as Per SSC: Pindi Venkanna
Regd Number: 22A21F00A6
Project Title: Securing Sharable Electronic Health Records On Cloud Storage
Project Abstract (only): Due to the potential of data breaches and the resultant compromise of patient’s sensitive data, medical organizations find it difficult to secure the data stored on cloud storage. Thus, storing and sharing data on cloud presents numerous security issues related to authentication, identity management, access control, trust management etc. Electronic Health Records (EHR) is of the paramount importance in the domain of research as the researchers use these data/reports to analyze and find new diseases. EHR data stored on cloud contain sensitive information about the patient and cannot be disclosed to unauthorized users. Thus, sharing sensitive information is still remains unexplored. Therefore, this article proposes a novel framework to store and share the data in a secured manner using Attribute Based Encryption (ABE). Storing and sharing EHR data on the cloud allows the research community to analyze various patient reports, which intern helps pharmacy and other healthcare-companies to expand their business.
Project Documentation: https://drive.google.com/open?id=1ON3xQZrmuM0lFLBjsRj-2AtXPqf6NTr8
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Timestamp: 4-15-2024 12:25:30
Email Address: bvslramyakachchala@gmail.com
Name of The Student as Per SSC: KACHCHALA B V SATYA LAKSHMI RAMYA
Regd Number: 22A21F0043
Project Title:
Project Abstract (only): Ensuring women’s safety in smart cities is a need of the hour. Even though several legal and technological steps are adopted worldwide, women’s safety continues to be an international concern. Criminal records are maintained by law enforcement agencies and are most often not available to the public in an easily comprehensible form. While some wearable devices and mobile applications are available which are touted to aid in ensuring women’s safety, they utilize limited societal intervention and are not very efficient in ensuring the safety of the women as and when required. Most often the crime response, crime analysis, and crime prevention schemes are not integrated, leading to gaps in ensuring women’s safety. Our major contribution is in developing a holistic system encompassing the three crucial aspects, i.e crime analysis and mapping, crime prevention, and emergency response by leveraging societal participation for women safety management. This work applies the Geographic Information System (GIS) for the identification of hotspots and patterns of crime. The proposed system uses data generated from the mobile application and/or wearable gadget prototyped as a part of this work along with the criminal history records for crime response, analysis, and prevention. The system for the hotspot identification is demonstrated for the Pilani town in the Jhunjhunu district in the state of Rajasthan, India, and can be easily scaled up geographically and utilized as a safety strategy for smart cities. While the common man is provided a costeffective solution via the developed mobile application or wearable gadget, the various components are integrated into a website for supervisory management and can be utilized by law enforcement agencies.
Project Documentation: https://drive.google.com/open?id=1RZ6cG9IffE5CfU7yWSrjxotASt89uiwT
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Timestamp: 4-15-2024 12:28:48
Email Address: munshiabdulhooorunnisa@gmail.com
Name of The Student as Per SSC: Munshi Abdul Hoorunnisa
Regd Number: 22A21F0097
Project Title: Severity Level Prediction In Flight Accident using ML
Project Abstract (only): The safety of the airlines and their passengers should be our top priority. Various safety checks are performed continuously and manually round-the-clock, and the airline team takes care of all safety precautions and measures, but there are still some cases of accidents due to a variety of factors. To improve aviation safety and stop future accidents, it is essential to estimate how severe a flying mishap would be. In this study, we provide a method that estimating the seriousness of flying incidents. Our findings show that the suggested method beats conventional machine learning methods, predicting the severity of aviation accidents with an accuracy of up to 85%. Our work stresses the value of enhancing the effectiveness of models for predicting the seriousness of aircraft accidents. The suggested method may be applied by regulators and specialists in aviation safety to improve aircraft safety by creating more potent accident prevention measures.
Project Documentation: https://drive.google.com/open?id=1-2MmuyD0e6ca85sC-EWgfmd_QuYmu7jr
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Timestamp: 4-15-2024 12:58:36
Email Address: kumpatlahari@gmail.com
Name of The Student as Per SSC: Kumpatla Hari
Regd Number: 22A21F0036
Project Title: DETECTDUI: AN IN-CAR DETECTION SYSTEM FOR DRINK DRIVING AND BACS
Project Abstract (only): As one of the biggest contributors to road accidents and fatalities, drink driving is worthy of significant research attention. However, most existing systems on detecting or preventing drink driving either require special hardware or require much effort from the user, making these systems inapplicable to continuous drink driving monitoring in a real driving environment. In this paper, we present DetectDUI, a contactless, non-invasive, real-time system that yields a relatively highly accurate drink driving monitoring by combining vital signs (heart rate and respiration rate) extracted from in-car WiFi system and driver’s psychomotor coordination throughsteering wheel operations. The framework consists of a series of signal processing algorithms for extracting clean and informative vital signs and psychomotor coordination, and integrate the two data streams using a self-attention convolutional neural network (i.e., C-Attention). In safe laboratory experiments with 15 participants, DetectDUI achieves drink driving detection accuracy of 96.6% and BAC predictions with an average mean error of 2 _ 5mg/dl. These promising results provide a highly encouraging case for continued development.
Project Documentation: https://drive.google.com/open?id=14ybTxhW4jJDUuAiqawIiQHHd0Frwif6q
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Timestamp: 4-15-2024 13:43:12
Email Address: ganeshgudala12@gmail.com
Name of The Student as Per SSC: GUDALA DUBEY GANESH
Regd Number: 22A21F0028
Project Title: Prediction Of Elections Results Based on Social Media Reviews
Project Abstract (only): The way politicians communicate with the electorate and run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SNs). Due to the inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using the SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial and many times challenged. In this context, this article aims to investigate and summarize how research on predicting elections based on the SM data has evolved since its beginning, to outline the state of both the art and the practice, and to identify research opportunities within this field. In terms of method, we performed a systematic literature review analyzing the quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation, and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances in process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-the-art machine learning approaches.
Project Documentation: https://drive.google.com/open?id=1d5XXXMK1MhVx2_o6J3PPGuvQwUu5yiLY
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Timestamp: 4-15-2024 13:47:17
Email Address: tirumanilokeshkumar311@gmail.com
Name of The Student as Per SSC: TIRUMANI LOKESH KUMAR
Regd Number: 22A21F00B7
Project Title: A REAL TIME CROWD DETECTION AND MONITORING SYSTEM USEING MEACHIN LEARNING
Project Abstract (only): The COVID-19 pandemic has unquestionably warned all of us that, the outbreak of an infection can lead to a pandemic- like situation all over the world. In order to prevent outbreaks and provide better healthcare, appropriate crowd detection and monitoring systems must be deployed in public areas. By effectively implementing social distancing measures, the number of new infections can be greatly decreased. This idea served as the inspiration for the creation of a real-time Crowd Detection and Monitoring System (CDMS) for social distancing. This paper proposes a fully autonomous system for Real-Time Crowd Detection and Monitoring to help the educational institutions to monitor the students inside the premises more effectively. This system is developed using an OpenCV based Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) detector to detect and count the number of people gathered at an instance. The system raises an alarm to alert the people and adhere to the rules if the gathering is more than the threshold/permitted number of people in the cluster.
Project Documentation: https://drive.google.com/open?id=1p5_0ft8DkKUygzZwmNUxH391YJldPY8x
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Timestamp: 4-15-2024 13:53:16
Email Address: mani1925110@gmail.com
Name of The Student as Per SSC: KANDREGULA MANIKANTA
Regd Number: 22A21F0078
Project Title: WATER QUALITY PREDICTION USING MACHINE LEARNING
Project Abstract (only): The major goal of this project is to use machine learning techniques to measure water quality. A potability is a numerical phrase that is used to assess the quality of a body of water. The following water quality parameters were utilised to assess the overall water quality in terms of potability in this study. ph, Hardness, Solids, Chloromines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes, Turbidity were the parameters. To depict the water quality, these parameters are used as a feature vector. To estimate the water quality class, the paper used two types of classification algorithms: Decision Tree (DT) and K- Nearest Neighbor (KNN).
Project Documentation: https://drive.google.com/open?id=1F2qEvf2cddjGYBNhD6-_V14OWkZ8AAss
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Timestamp: 4-15-2024 13:59:59
Email Address: koppineedikrishna5@gmail.com
Name of The Student as Per SSC: KOPPINEEDI VENKATA MANI SAIKRISHNA
Regd Number: 22A21F0082
Project Title:
Project Abstract (only): It is very important to increase the crop yield to satisfy the needs of increasing population. Most of the Indian farmers hold fragmented cropland and their yield is dependent on availability of various factors like soil-quality, rainfall, and environmental conditions. Average annual soil loss in India is about 5.3 billion tonnes. Degraded land loses it’s capacity to produce adequate yield. Agriculture in India is conditioned by the poor fertility of the soil, which depends on the levels of its nutrients; further a soil can be suitable for certain crops and yield a good production while prove to be otherwise for some other crops. The physical, chemical, and biological properties of the soil are useful to evaluate its fertility, to devise a cultivation plan and to predict the crop productivity.
Project Documentation: https://drive.google.com/open?id=1k1_fDX1Lg9Y21YDo-WBhy0KLl2YQVcQT
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Timestamp: 4-15-2024 14:03:56
Email Address: vamsimanchiganti6@gmail.com
Name of The Student as Per SSC: MANCHIGANTI VAMSI SRINIVAS
Regd Number: 22A21F0091
Project Title: Resume screening based on skillset using ML techniques
Project Abstract (only): In today’s competitive world, it is a very complicated process to hire candidates with manual verification of resumes. This work is an experimental method for ranking of hiring resumes because manually ranking is quite a complicated job for the hiring team, as it takes more time to verify each of the candidates resumes. If the resumes are high in number then man power will also increase for the same task. To rectify these problems a new solution has been proposed. In order to make this whole resume screening process more effective, an application for processing the resumes screening using machine learning is proposed. This work uses methods such as optimizing the candidates’ performance in the preferred skill mentioned in the resume and also ranking method to display the selected candidates based on their overall performance according to the skill requirement of the company’s required job position. The whole idea is implemented using python language and the results are sure to make the recruitment process efficient.
Project Documentation: https://drive.google.com/open?id=1QS5O4xdCadzwIrPVwZHWd8AJTXCDEFWW
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Timestamp: 4-15-2024 14:32:43
Email Address: aswinibandaru6@gmail.com
Name of The Student as Per SSC: Aswini Bandaru
Regd Number: 22A21F0006
Project Title: A Spam Transformer Model for SMS Spam Detection
Project Abstract (only): In this paper, we aim to explore the possibility of the Transformer model in detecting the spam Short Message Service (SMS) messages by proposing a modified Transformer model that is designed for detecting SMS spam messages. The evaluation of our proposed spam Transformer is performed on SMS Spam Collection v.1 dataset and UtkMl's Twitter Spam Detection Competition dataset, with the benchmark of multiple established machine learning classifiers and state-of-the-art SMS spam detection approaches. In comparison to all other candidates, our experiments on SMS spam detection show that the proposed modified spam Transformer has the optimal results on the accuracy, recall, and F1-Score with the values of 98.92%, 0.9451, and 0.9613, respectively. Besides, the proposed model also achieves good performance on the UtkMl's Twitter dataset, which indicates a promising possibility of adapting the model to other similar problems.
Project Documentation: https://drive.google.com/open?id=1HDptf5gBnwR4yy30kYHBjRNdFZFc61_V
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Timestamp: 4-15-2024 14:41:21
Email Address: lathasripodala2002@gmail.com
Name of The Student as Per SSC: Podala Latha Sri Venkata Surya Padma valli
Regd Number: 22A21F0062
Project Title: Agricultural Crop Recommendations based on Productivity and Season
Project Abstract (only): As a coastal state, Tamil Nadu faces uncertainty in agriculture which decreases its production. With more population and area, more productivity should be achieved but it cannot be reached. Farmers have words-of-mouth in past decades but now it cannot be used due to climatic factors. Agricultural factors and parameters make the data to get insights about the Agri-facts. Growth of IT world drives some highlights in Agriculture Sciences to help farmers with good agricultural information. Intelligence of applying modern technological methods in the field of agriculture is desirable in this current scenario. Machine Learning Techniques develops a well-defined model with the data and helps us to attain predictions. Agricultural issues like crop prediction, rotation, water requirement, fertilizer requirement and protection can be solved. Due to the variable climatic factors o f the environment, there is a necessity to have a efficient technique to facilitate the crop cultivation and to lend a hand to the farmers in their production and management. This may help upcoming agriculturalists to have a better agriculture. A system o f recommendations can be provided to a farmer to help them in crop cultivation with the help o f data mining. To implement such an approach, crops are recommended based on its climatic factors and quantity. Data Analytics paves a way to evolve useful extraction from agricultural database. Crop Dataset has been analyzed and recommendation o f crops is done based on productivity and season.
Project Documentation: https://drive.google.com/open?id=1BWZGz5XHVpeR1b4hFZaS8tyMl5EP4_7B
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Timestamp: 4-15-2024 15:04:22
Email Address: saladiasrithadevi@gmail.com
Name of The Student as Per SSC: Saladi Asritha Devi
Regd Number: 22A21F0063
Project Title: DSAS A secure data sharing and authorized searchable framework for e-health care system
Project Abstract (only): In e-healthcare system, an increasing number of patients enjoy high-quality medical services by sharing encrypted personal healthcare records (PHRs) with doctors or medical research institutions. However, one of the important issues is that the encrypted PHRs prevent effective search of information, resulting in the decrease of data usage. Another issue is that medical treatment process requires the doctor to be online all the time, which may be unaffordable for all doctors (e.g., to be absent under certain circumstances). In this paper, we design a new secure and practical proxy searchable re-encryption scheme, allowing medical service providers to achieve remote PHRs monitoring and research safely and efficiently. Through our scheme DSAS, (1) patients' healthcare records collected by the devices are encrypted before uploading to the cloud server ensuring privacy and confidentiality of PHRs; (2) only authorized doctors or research institutions have access to the PHRs; (3) Owner (doctor-in-charge) is able to delegate medical research and utilization to User (doctor-in-agent) or certain research institution through the cloud server, supporting minimizing information exposure to the cloud server. We formalize the security definition and prove the security of our scheme. Finally, performance evaluation shows the efficiency of our scheme.
Project Documentation: https://drive.google.com/open?id=16jpTykaatmfEhmDtomhLuQ_UK8y6b3Nj
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Timestamp: 4-15-2024 15:12:59
Email Address: sureshmyla6@gmail.com
Name of The Student as Per SSC: MYLA SURESH KUMAR
Regd Number: 22A21F0057
Project Title: Secure Cloud Data Deduplication with Efficient Re-Encryption
Project Abstract (only): Data deduplication technique has been widely adopted by commercial cloud storage providers, which is both important and necessary in coping with the explosive growth of data. To further protect the security of users’ sensitive data in the outsourced storage mode, many secure data deduplication schemes have been designed and applied in various scenarios. Among these schemes, secure and efficient re-encryption for encrypted data deduplication attracted the attention of many scholars, and many solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system and show that the recently designed lightweight rekeying-aware encrypted deduplication scheme (REED) is vulnerable to an attack which we call it stub-reserved attack. Furthermore, we propose a secure data deduplication scheme with efficient re-encryption based on the convergent all-or-nothing transform (CAONT) and randomly sampled bits from the Bloom filter. Due to the intrinsic property of one-way hash function, our scheme can resist the stub-reserved attack and guarantee the data privacy of data owners’ sensitive data. Moreover, instead of re-encrypting the entire package, data owners are only required to re-encrypt a small part of it through the CAONT, thereby effectively reducing the computation overhead of the system. Finally, security analysis and experimental results show that our scheme is secure and efficient in re-encryption.
Project Documentation: https://drive.google.com/open?id=1_BCqruLwzAlPoVAreF2ICuwAKmJHzHOb
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Timestamp: 4-15-2024 15:22:31
Email Address: bbsgayatri@gmail.com
Name of The Student as Per SSC: BORRA BHUVANA SRI GAYATHRI
Regd Number: 22A21F0011
Project Title: CRYPT CLOUD SECURE AND EXPRESSIVE DATA ACCESS CONTROL FOR CLOUD STORAGE
Project Abstract (only): Secure cloud storage is a service that keeps your data safe and allows you to access it from anywhere. One way to protect this data is through a method called Ciphertext-Policy Attribute-Based Encryption (CP-ABE). This method is promising but can have a security risk where someone might misuse their access rights to decrypt data. We looked at two main ways this misuse can happen: one is when the authority managing access is not completely trustworthy, and the other is when a cloud user abuses their access. To address this, we've created a system called CryptCloud that can hold these authorities accountable and revoke access if needed. This system also has features for tracking and auditing access, which helps ensure its security. We've tested our system to show that it works effectively.
Project Documentation: https://drive.google.com/open?id=1OnUnEK_iM93j6sRRsmJdow1S_OjYKjoh
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Timestamp: 4-15-2024 15:25:07
Email Address: lavanyakandrekula445@gmail.com
Name of The Student as Per SSC: KANDREKULA VENKATA LAVANYA
Regd Number: 22A21F0044
Project Title: WATERNET A NETWORK FOR MONITORING AND ACESSING WATER QUALITY FOR DRINKING AND IRRIGATION PURPOSES
Project Abstract (only): Water is a fundamental requirement for human, animal, and plant survival. Despite its importance, quality water is not always _t for drinking, domestic and/or industrial use. Numerous factors such as industrialization, mining, pollution, and natural occurrences impact the quality of water, as they introduce or alter various parameters present therein, thus, affecting its suitability for human consumption or general use. The World Health Organization has guidelines which stipulate the threshold levels of various parameters present in water samples intended for consumption or irrigation. The Water Quality Index (WQI) and Irrigation WQI (IWQI) are metrics used to express the level of these parameters to determine the overall water quality. Collecting water samples from different sources, measuring the various parameters present, and bench-marking these measurements against pre-set standards, while adhering to various guidelines during transportation and measurement can be extremely daunting.
Project Documentation: https://drive.google.com/open?id=1DpL141RnsCHJ0JKhwSiSbckw0J2HR_TD
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Timestamp: 4-15-2024 15:50:06
Email Address: mokadivya3@gmail.com
Name of The Student as Per SSC: MOKA DIVYA
Regd Number: 22A21F0095
Project Title: leaf and spike wheat disease detection &classification using an improved deep convolution architecture
Project Abstract (only): Wheat is the third most harvested and consumed grain in the world. However, a large part of wheat crop becomes spoiled due to diseases. There are over two dozen of wheat diseases that are harmful to the crops. Therefore, the manual diagnosis of these diseases becomes very challenging. Automatic wheat disease classification can be helpful in improving the quantity and quality of the crop yield. Further, it can be a useful mechanism for crop quality assessment, and pricing. Deep learning based image analysis has applications in disease diagnosis and classification. The spike and leaves are the most affected parts of a wheat plant. Majority of diseases can be recognized by the characteristics of these parts. The paper presents a novel wheat disease classification method. A new deep learning model is trained to accurately classify wheat diseases in 10 classes. The proposed method has a high testing accuracy of 97.88%. Furthermore, it gives an improvement of 7.01% and 15.92% for the accuracy metric over the other two popular deep learning models – VGG16 and RESNET50, respectively. EXperimental results establish that the proposed method performs better on other parameters such as precision, recall, and f-score.
Project Documentation: https://drive.google.com/open?id=1pLCcTgXdoOpHwBOBTsVSb9g3sNXe0eui
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Timestamp: 4-15-2024 15:56:02
Email Address: srinivasakana17@gmail.com
Name of The Student as Per SSC: AKANA SRINIVAS
Regd Number: 22A21F0003
Project Title: Prediction of Modernized Loan Approval System Based on Machine Learning Approach
Project Abstract (only): Technology has boosted the existence of humankind the quality of life they live. Every day we are planning to create something new and different. We have a solution for every other problem we have machines to support our lives and make us somewhat complete in the banking sector candidate gets proofs/ backup before approval of the loan amount. The application approved or not approved depends upon the historical data of the candidate by the system. Every day lots of people applying for the loan in the banking sector but Bank would have limited funds. In this case, the right prediction would be very beneficial using some classes-function algorithm. An example the logistic regression, random forest classifier, support vector machine classifier, etc. A Bank's profit and loss depend on the amount of the loans that is whether the Client or customer is paying back the loan. Recovery of loans is the most important for the banking sector. The improvement process plays an important role in the banking sector. The historical data of candidates was used to build a machine learning model using different classification algorithms. The main objective of this paper is to predict whether a new applicant granted the loan or not using machine learning models trained on the historical data set
Project Documentation: https://drive.google.com/open?id=1R99mbBC1YhMv4AnN8sx9M0t1zm01pL6Y
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Timestamp: 4-15-2024 16:47:42
Email Address: mounikakoppineedi@gmail.com
Name of The Student as Per SSC: KOPPINEEDI SURYA MOUNIKA
Regd Number: 22A21F0052
Project Title: Heart Disease Identification Method Using Machine Learning Classification in E-HealthCare
Project Abstract (only): Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyper parameter tuning.
Project Documentation: https://drive.google.com/open?id=19xlnqcufmUv8NkrPTgyLK2Gh3WCAjen3
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Timestamp: 4-15-2024 18:14:39
Email Address: kumarsai72880@gmail.com
Name of The Student as Per SSC: Koppada Kumar Sai
Regd Number: 22A21F0081
Project Title: analyze and forecast the cyber attack detection process using machine learning techniques
Project Abstract (only): One of the most crucial global concerns is the issue of cybercrime, which leads to significant financial losses for nations and their citizens every day. The frequency of cyberattacks has steadily increased, emphasizing the need to identify the individuals behind these criminal activities and understand their strategies. Detecting and preventing cyber- attacks pose significant challenges, but recent advancements have introduced security models and prediction tools based on artificial intelligence to tackle these issues. Although there is a wealth of literature on crime prediction strategies, they may need to be more effectively suited for awaiting cybercrime and cyber-attack techniques
Project Documentation: https://drive.google.com/open?id=1kJPEetX2kY-WErVbNUCQ2yFA2-6a38fl
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Timestamp: 4-15-2024 18:17:27
Email Address: sairajeev77@gmail.com
Name of The Student as Per SSC: CHEGONDI RAJEEV SAI
Regd Number: 22A21F0014
Project Title: Electricity theft detection in power grids with deep learning and random forests
Project Abstract (only): As one of the major factors of the nontechnical losses (NTLs) in distribution networks, the electricity theft causes significant harm to power grids, which influences power supply quality and reduces operating profits. In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, a novel hybrid convolutional neural network-random forest (CNN-RF) model for automatic electricity theft detection is presented in this paper. In this model, a convolutional neural network (CNN) firstly is designed to learn the features between different hours of the day and different days from massive and varying smart meter data by the operations of convolution and down sampling. In addition, a dropout layer is added to retard the risk of overfitting, and the backpropagation algorithm is applied to update network parameters in the training phase. And then, the random forest (RF) is trained based on the obtained features to detect whether the consumer steals electricity. To build the RF in the hybrid model, the grid search algorithm is adopted to determine optimal parameters. Finally, experiments are conducted based on real energy consumption data, and the results show that the proposed detection model outperforms other methods in terms of accuracy and efficiency.
Project Documentation: https://drive.google.com/open?id=1wg8DNahthJCUCBkJ4dtHgl2stYe-GJ7u
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Timestamp: 4-15-2024 18:42:39
Email Address: lavanyainti14@gmail.com
Name of The Student as Per SSC: Lavanya inti
Regd Number: 22A21F0037
Project Title: Audience Behavior Mining By Integrating Tv Ratings with Multimedia Content
Project Abstract (only): TV ratings are a widely used indicator in the TV broadcasting field. While TV ratings are mainly used in advertising, they can also be used as a social sensor that reflects the interests of people. This paper presents a framework for discovering audience behavior through the mining of TV ratings. We have established a framework that enables discovery of numerous patterns of audience behavior from TV ratings. Used along with other multimedia contents such as video and text, it enables various types of knowledge to be semi-automatically found, such as what types of news programs are of most interest and what are the key visual features for acquiring high TV ratings. The discovery of audience behavior is achieved by focusing on the people purchasing particular Tv channel and user feedback on that channel after purchasing. Rich descriptions that characterize these points are extracted from multimedia contents, and then various filtering techniques are used to extract specific patterns of interest. Several applications of this framework for discovering knowledge demonstrated that it can effectively extract various types of audience behavior. To the best of our knowledge, this work is the first work to analyze the use of ratings data in combination with video and other multimedia data.
Project Documentation: https://drive.google.com/open?id=1QUq2fFOmZg7CLgV3rYORfqfuvGIokQvo
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Timestamp: 4-15-2024 18:52:17
Email Address: polumurimounika525@gmail.com
Name of The Student as Per SSC: POLUMURI LAKSHMI MOUNIKA
Regd Number: 22A21F00A9
Project Title: Scrutinizing machine learning models for cancer prediction
Project Abstract (only): Due to some abnormal changes in genes of cells, enforces cells to divide uncontrollably, due to which tumors are formed, which infiltrates and damages the normal body tissues, and this condition is called “Cancer”. Lung cancer is a type of cancer where the infected cells in the lungs multiply rapidly at a high rate. This abnormal growth of cells, which eventually leads to cancer can be identified using modern data analysis. Detecting cancer symptoms at an early stage plays a crucial role for the patients who may suffer later, if not detected. One of the major problems is the increasing fad of smoking tobacco in youngsters. Air pollutants from industries which get inhaled by people are some of the main causes of increasing lung cancer in India. The main focus of this study is to predict lung cancer in different patients using Machine Learning (ML) algorithms such as a random forest classifier(RFC), k-nearest neighbour(KNN), K-means, Support vector machine(SVM), and decision tree classifier(DTC). The key objective of this research is the analysis of different machine learning algorithms based on their performance metrics.
Project Documentation: https://drive.google.com/open?id=17FYlmK_obSWUmePsb-c5wSo_-DuPkHIj
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Timestamp: 4-15-2024 18:55:10
Email Address: pavankuna446@gmail.com
Name of The Student as Per SSC: KUNA DURGA PAVAN DESA RAO
Regd Number: 22A21F0086
Project Title: Sentiment Analysis Study of Human Thoughts using Machine Learning Techniques
Project Abstract (only): This study explores machine learning's role in analyzing human emotions from social media data, employing algorithms like Naive Bayes, RNNs and LSTM Networks. Results indicate varied algorithm performance based on text length, with combinations improving accuracy. Findings suggest machine learning's potential for understanding human emotions, impacting fields like marketing, politics, and mental health. The article provides a comprehensive review of sentiment analysis methods, categorizing techniques and comparing their strengths and weaknesses.
Project Documentation: https://drive.google.com/open?id=1roSKCi-3oXI9OP_PLohBsnQpmK2Eq21x
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Timestamp: 4-15-2024 19:32:21
Email Address: sasikumariyandrapu@gmail.com
Name of The Student as Per SSC: YANDRAPU MONIKA SASI KUMARI
Regd Number: 22A21F0066
Project Title: MALICIOUS URL DETECTION USING MACHINE LEARNING
Project Abstract (only): Currently, the risk of network information insecurity is increasing rapidly in number and level of danger. The methods mostly used by hackers today is to attack end-to-end technology and exploit human vulnerabilities. These techniques include social engineering, phishing, pharming, etc. One of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs). As a result, malicious URL detection is of great interest nowadays. There have been several scientific studies showing a number of methods to detect malicious URLs based on machine learning and deep learning techniques. In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behaviors and attributes. Moreover, big data technology is also exploited to improve the capability of detection malicious URLs based on abnormal behaviors.
Project Documentation: https://drive.google.com/open?id=1ABZQFKdRUZAQSPi9poJihQt9WFdhMyz2
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Timestamp: 4-15-2024 20:00:41
Email Address: rakeshnaidu835@gmail.com
Name of The Student as Per SSC: DARABANDHAM TRAKEEH
Regd Number: 22A21F0021
Project Title: Food Safety Traceability System for Peoples Health Using the Internet of Things and Big Data
Project Abstract (only): In the context of epidemic prevention and control, food safety monitoring, data analysis and food safety traceability have become more important. At the same time, the most important reason for food safety issues is incomplete, opaque, and asymmetric information. The most fundamental way to solve these problems is to do a good job of traceability, and establish a reasonable and reliable food safety traceability system. The traceability system is currently an important means to ensure food quality and safety and solve the crisis of trust between consumers and the market. Research on food safety traceability systems based on big data, artificial intelligence and the Internet of Things provides ideas and methods to solve the problems of low credibility and difficult data storage in the application of traditional traceability systems. Therefore, this research takes rice as an example and proposes a food safety traceability system based on RFID two dimensional code technology and big data storage technology in the Internet of Things.
Project Documentation: https://drive.google.com/open?id=1sAjbhr1vEqW6axGWk79iP-7DMsRHug4-
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Timestamp: 4-15-2024 20:59:28
Email Address: sarveswararaokovvuri123@gmail.com
Name of The Student as Per SSC: KOVVURI SARVESWARA RAO
Regd Number: 22A21F0084
Project Title: Benchmarking Probabilistic Deep Learning Methods
Project Abstract (only): Learning-based algorithms for automated license plate recognition implicitly assume that the training and test data are well aligned. However, this may not be the case under extreme environmental conditions, or in forensic applications where the system cannot be trained for a specific acquisition device. Predictions on such out-of-distribution images have an increased chance of failing. But this failure case is oftentimes hard to recognize for a human operator or an automated system. Hence, in this work we propose to model the prediction uncertainty for license plate recognition explicitly. Such an uncertainty measure allows to detect false predictions, indicating an analyst when not to trust the result of the automated license plate recognition. In this paper, we compare three methods for uncertainty quantification on two architectures. The experiments on synthetic noisy or blurred low-resolution images show that the predictive uncertainty reliably finds wrong predictions. We also show that a multi-task combination of classification and super-resolution improves the recognition performance by 109% and the detection of wrong predictions by 29%.
Project Documentation: https://drive.google.com/open?id=1kJpx7DndDTI1IVY8Rx1Uhd_9DIhn8IC3
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Timestamp: 4-15-2024 22:18:50
Email Address: rajeshgandikota1111@gmail.com
Name of The Student as Per SSC: GANDIKOTA RAJESH
Regd Number: 22A21F0025
Project Title: Emotion Detection in IT Professionals by Image Processing and Machine Learning
Project Abstract (only): The main motive of our project is to detect Emotion in the IT professionals using vivid Machine learning and Image processing techniques. Our system is an upgraded version of the old stress detection systems which excluded the live detection and the personal counseling but this system comprises of live detection and periodic analysis of employees and detecting physical as well as mental stress levels in his/her by providing them with proper remedies for managing stress by providing survey form periodically. Our system mainly focuses on managing stress and making the working environment healthy and spontaneous for the employees and to get the best out of them during working hours.
Project Documentation: https://drive.google.com/open?id=1u3l3RaW_R6LnhHprplBp5Tb79Z2W9EPr
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Timestamp: 4-15-2024 22:39:51
Email Address: gollusaikumar2000@gmail.com
Name of The Student as Per SSC: GOLLU SAI KUMAR
Regd Number: 22A21F0026
Project Title: SentiTrust A New Trust Model for Decentralized Online Social Media
Project Abstract (only): Online Social Media (OSM) are dominating the wide range of Internet services. Due to their vast audience, it is crucial to evaluate the interpersonal trust among OSM users that can identify reliable sources of information, the meaningfulness of a relationship, or the trustworthiness of other users. SentiTrust is an innovative trust model for Decentralized Online Social Networks that is based on AI-powered Sentiment Analysis. It enriches the trust definition by exploiting important features that are enabled because of the adoption of Social Media through mobile devices. The model can be easily extended and customized according to the scenario of interest. The sentiment analysis component has been tested by involving 30 participants who completed several guided tasks using a social media application while their electrodermal activity and rate responses were measured. The results suggest that low arousal states are related to receiving happy faces and to sending more messages per minute. Furthermore, positive interactions result in shorter interactions and multimedia exchanges
Project Documentation: https://drive.google.com/open?id=1PHgkU0vkymyhq07wMZpryu_silQo78tF
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Timestamp: 4-15-2024 23:01:00
Email Address: kattabhavya42@gmail.com
Name of The Student as Per SSC: KATTA BHAVYA
Regd Number: 22A21F0047
Project Title: Trustworthiness Assessment of Users in Social Reviewing Systems
Project Abstract (only): In today's digital era, social reviewing systems play a pivotal role in shaping consumer decisions across various domains, from product purchases to service selections. However, the reliability and authenticity of user-generated content within these systems remain a significant concern. This project aims to investigate methodologies for assessing the trustworthiness of users in social reviewing systems. The research will delve into the existing literature on trustworthiness assessment techniques, including sentiment analysis, user reputation analysis, and network analysis. By synthesizing these approaches, the project seeks to develop a comprehensive framework for evaluating user trustworthiness. Furthermore, the project will explore the challenges associated with detecting fraudulent or biased reviews and propose strategies to mitigate such issues. Leveraging machine learning algorithms and data mining techniques, the framework aims to provide more accurate and reliable assessments of user credibility. Ultimately, this research endeavors to enhance the integrity and reliability of social reviewing systems, fostering trust among consumers and promoting informed decision-making in the digital landscape.
Project Documentation: https://drive.google.com/open?id=1oE7JueDn7omh4J6MYWiT2VstdxKThHiz
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Timestamp: 4-15-2024 23:19:12
Email Address: tejareddybommareddy123@gmail.com
Name of The Student as Per SSC: Bommareddy Teja Reddy
Regd Number: 22A21F0009
Project Title: Fraud Detection and Analysis for Insurance Claim using Machine Learning
Project Abstract (only): Fraud can be spread broadly and it is extremely costly to the therapeutic protection framework. Unscrupulous protection might be a case created to cover up or twist information that is intended to deliver social insurance edges. Cheats might be of the numerous sorts and submitted by the protection guarantor or the safeguarded. The unscrupulous social insurance providers are the reason for extortion in the wellbeing segment. The commitment of this case misrepresentation discovery is Associate in nursing trial study on extortion recognizable proof and exploitative examples. Along these lines, to identify the misrepresentation information handling procedures are utilized. For the most part essential based oddities are implemented exploitation applied math call rules and k-means, rule-based mining and affiliation rule bolstered appropriation calculations are applied. Through these abnormalities the extortion in certifiable information is recognized. Be that as it may, there might be a great deal of progress done by exploitation various information handling procedures. In this way the arranged methodology has been assessed basing on the protection information and furthermore the trial results from our methodology are efficient in human services misrepresentation. Other self-advancing misrepresentation location ways can likewise be applied on this protection information.
Project Documentation: https://drive.google.com/open?id=1hipKsCHrTAviK6HvgZTe4AGNFJBlOq24
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Timestamp: 4-16-2024 0:14:57
Email Address: jogiramya27@gmail.com
Name of The Student as Per SSC: Jogi Ramya
Regd Number: 22A21F0041
Project Title: Predicting Drug – Drug interactions based on integrated similarity using semi supervised learning
Project Abstract (only): A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause the major cause of adverse drug reactions and even result in the drug withdrawal from the market and the patient death. The Gaussian Interaction Profile kernel similarity of drugs is also calculated based on known DDIs. A semi-supervised learning method (the Regularized Least Squares classifier) is used to calculate the interaction possibility scores of drug-drug pairs. In terms of the 5-fold cross validation, 10-fold cross validation and de novo drug validation, DDI-IS-SL can achieve the better prediction performance than other comparative methods.
Project Documentation: https://drive.google.com/open?id=1rYqXaVy9BnXFBJsdSEmZhHQMQdk-GT00
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Timestamp: 4-16-2024 0:17:20
Email Address: tshanmukha5@gmail.com
Name of The Student as Per SSC: TELAGAREDDY SHANMUKHA PRIYA
Regd Number: 22A21F00B5
Project Title: music gener classification using convalutional nwural network
Project Abstract (only): Feature extraction is a crucial part of many MIR tasks. Many manual-selected features such as MFCC have been applied to music processing but they are not effective for music genre classification. In this work, we present an algorithm based on spectrogram and convolutional neural network (CNN). Compared with MFCC, the spectrogram contains more details of music components such as pitch, flux, etc. We use feature detector as filter to convolve spectrogram to get four feature maps, which can catch trends of spectrogram in both time and frequency scale. Then sub-sample layer is applied to reduce dimension and enhance resistance to translation in pitch and tempo. Finally the extracted high-level features are connected to a multi-layer perceptron (MLP) classifier. A classification accuracy of 72.4% is obtained on Tzanetakis dataset by uing the proposed features, which performs better than MFCC.
Project Documentation: https://drive.google.com/open?id=1qwdvR9z61BNEQHcYFB9uTEQrPFxbD8rR
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Timestamp: 4-16-2024 5:59:34
Email Address: panjachaitanya1128@gmail.com
Name of The Student as Per SSC: PANJA CHAITANYA VENKATA PADMARAO
Regd Number: 22A21F0059
Project Title: A Proxy Re Encryption Approach to Secure Data Sharing in The Internet of Things based on Block Chain
Project Abstract (only): The evolution of the Internet of Things has seen data sharing as one of its most useful applications in cloud computing. As eye-catching as this technology has been, data security remains one of the obstacles it faces since the wrongful use of data leads to several damages. In this article, we propose a proxy re-encryption approach to secure data sharing in cloud environments. Data owners can outsource their encrypted data to the cloud using identity-based encryption, while proxy re-encryption construction will grant legitimate users access to the data. With the Internet of Things devices being resource-constrained, an edge device acts as a proxy server to handle intensive computations. Also, we make use of the features of information-centric networking to deliver cached content in the proxy effectively, thus improving the quality of service and making good use of the network bandwidth. Further, our system model is based on blockchain, a disruptive technology that enables decentralization in data sharing. It mitigates the bottlenecks in centralized systems and achieves fine-grained access control to data. The security analysis and evaluation of our scheme show the promise of our approach in ensuring data confidentiality, integrity, and security
Project Documentation: https://drive.google.com/open?id=1wCiA_nsaaT4b-D9Dny-3WJVB5FPO_a93
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Timestamp: 4-16-2024 9:26:49
Email Address: lohithanagasaichennamsetti@gmail.com
Name of The Student as Per SSC: chennamsetti lohithanagasai
Regd Number: 22A21F0017
Project Title: A Novel Time-Aware Food Recommender-SystemBasedonDeep Learning and Graph Clustering
Project Abstract (only): Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food recommender-system to overcome the shortcomings of previous systems, such as ignoring food ingredients, time factor, cold start users, cold start food items and community aspects. The proposed method involves two phases: food content-based recommendation and user-based recommendation. Graph clustering is used in the first phase, and a deep-learning based approach is used in the second phase to cluster both users and food items. Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet.
Project Documentation: https://drive.google.com/open?id=16oGwTsPXD55pz5Mw444Hf0s5Tt4on4ii
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Timestamp: 4-16-2024 11:33:31
Email Address: vijayalakshmiachanta7@gmail.com
Name of The Student as Per SSC: ACHANTA VIJAYA LAKSHMI
Regd Number: 22A21F0001
Project Title: FAKE PROFILE IDENTIFICATION ON SOCIAL NETWORK USING MACHINE LEARNING ANA NLP
Project Abstract (only): At present social network sites are part of the life for most of the people. Every day several people are creating their profiles on the social network platforms and they are interacting with others independent of the user’s location and time. The social network sites not only providing advantages to the users and also provide security issues to the users as well their information. To analyze, who are encouraging threats in social network we need to classify the social networks profiles of the users. From the classification, we can get the genuine profiles and fake profiles on the social networks. Traditionally, we have different classification methods for detecting the fake profiles on the social networks. But, we need to improve the accuracy rate of the fake profile detection in the social networks. In this paper we are proposing Machine learning and Natural language Processing (NLP) techniques to improve the accuracy rate of the fake profiles detection. We can use the Support Vector Machine (SVM) and Naïve Bayes algorithm
Project Documentation: https://drive.google.com/open?id=14gs4M2EZY4KtI-L_jEvWc-Lpx102vQRG
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Timestamp: 4-16-2024 12:05:02
Email Address: lakshmipriyankapolumuri@gmail.com
Name of The Student as Per SSC: POLUMURI LAKSHMI PRIYANKA
Regd Number: 22A21F00B0
Project Title: A Machine Learning Model to Classify Indian Taxi System in Tourism Industry
Project Abstract (only): India is now becoming a tourism hotspot for tourist. To support the growng tourism industry, the taxi services play a major role and also it plays an important role in urban transportation. In view of the popularity of Taxi services, we have analyzed the sentiment of the taxi industry by taking the reviews of the customer on different taxi service providers. In this research, we addressed text sentiment analysis of taxi reviews, posted by customers on online review sites. All the reviews are based on Indian review sites only. We have compared many machine learning techniques with the dataset. To determine the sentiments of text reviews, machine learning techniques are used, which explore the feeling of a customer and also give the in-hand idea of the taxi services and its amenities. The study presents that among all the common machine learning techniques, Support Vector Machine (SVM) performs better than other algorithms. Considering different evaluation parameters like Accuracy, F1- Score, and Recall value, SVM gives the best result with 89%, 82%, and 86% respectively.
Project Documentation: https://drive.google.com/open?id=1C6nHeXwEpp-8yvffAaKilp1M_ui3OYhU
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Timestamp: 4-16-2024 12:28:21
Email Address: rupeshrk8309@gmail.com
Name of The Student as Per SSC: Kasimsetti Rupesh
Regd Number: 22A21F0046
Project Title: Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning.docx
Project Abstract (only): Since coronavirus has shown up, inaccessibility of legitimate clinical resources is at its peak, like the shortage of specialists and healthcare workers, lack of proper equipment and medicines etc. The entire medical fraternity is in distress, which results in numerous individual’s demise. Due to unavailability, individuals started taking medication independently without appropriate consultation, making the health condition worse than usual. As of late, machine learning has been valuable in numerous applications, and there is an increase in innovative work for automation. This paper intends to present a drug recommender system that can drastically reduce specialists heap. In this research, we build a medicine recommendation system that uses patient reviews to predict the sentiment using various vectorization processes like Bow, TF-IDF, Word2Vec, and Manual Feature Analysis, which can help recommend the top drug for a given disease by different classification algorithms. The predicted sentiments were evaluated by precision, recall, f1score, accuracy, and AUC score. The results show that classifier LinearSVC using TF-IDF vectorization outperforms all other models with 93% accuracy.
Project Documentation: https://drive.google.com/open?id=1zsIODD3oC-Dh6XTvLZKNBMaG6a78snid
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Timestamp: 4-16-2024 13:38:05
Email Address: chaitanyat668@gmail.com
Name of The Student as Per SSC: TIRUMANI CHAITANYA
Regd Number: 22A21F00B6
Project Title: XORSHIFT AND RANDOM NUMBER GENERATOR FOR IMAGE ENCRYTION
Project Abstract (only): With the rapid development of computer science, fast and secure transmission of data has gained great importance. Undoubtedly, one of the most common data types is digital images. The attractiveness of digital images is result of wide range usage from social media to defense industry. In order to transmit through correct and secure channels, images must be encrypted before they are sent. The relevant process is realized by means of encryption algorithms. Symmetric steams encryption algorithms which have been proposed so far have weaknesses in terms of speed and processing power. Therefore, encryption quality drops dramatically in certain scenarios. A novel symmetric stream encryption algorithm called XorShiftAnd has been proposed in this study.
Project Documentation: https://drive.google.com/open?id=16AdvlZen1rwpin5MrghLLKrf2wOUGjpi
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Timestamp: 4-16-2024 14:15:25
Email Address: saikrishna.pippalla@gmail.com
Name of The Student as Per SSC: PIPPALLA SAI KRISHNA
Regd Number: 22A21F00A7
Project Title: Reliably Filter Drug-Induced Liver Injury Literature with Natural Language Processing and Conformal Prediction
Project Abstract (only): Drug-induced liver injury describes the adverse effects of drugs that damage the liver. Life-threatening results were also reported in severe cases. Therefore, liver toxicity is an important assessment for new drug candidates. These reports are documented in research papers that contain preliminary in vitro and in vivo experiments. Conventionally, data extraction from publications relies on resource-demanding manual labeling, which restricts the efficiency of the information extraction. The development of natural language processing techniques enables the automatic processing of biomedical texts. Herein, based on around 28, 000 papers (titles and abstracts) provided by the Critical Assessment of Massive Data Analysis challenge, this study bench marked model performances on filtering liver-damage-related literature. Among five text embedding techniques, the model using term frequency-inverse document frequency (TF-IDF) and logistic regression outperformed others with an accuracy of 0.957 on the validation set. Furthermore, an ensemble model with similar overall performances was developed with a logistic regression model on the predicted probability given by separate models with different vectorization techniques. The ensemble model achieved a high accuracy of 0.954 and an F1 score of 0.955 in the hold-out validation data in the challenge. Moreover, important words in positive/negative predictions were identified via model interpretation. The prediction reliability was quantified with conformal prediction, which provides users with a control over the prediction uncertainty. Overall, the ensemble model and TF-IDF model reached satisfactory classification results, which can be used by researchers to rapidly filter literature that describes events related to liver injury induced by medications.
Project Documentation: https://drive.google.com/open?id=1be_bpmYgb2ELshUoqqiuHz1YeWAgS_c7
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Timestamp: 4-16-2024 14:48:26
Email Address: saimanikantakunasani0@gmail.com
Name of The Student as Per SSC: kunasani sai manikanta
Regd Number: 22A21F0087
Project Title: Efficient Data-driven Machine Learning Models for Hypertension Risk Prediction
Project Abstract (only): Hypertension is a chronic condition characterized by high pressure in the arteries of the human body. As a result, the heart is forced to work more intensively for the normal circulation of blood in the body. It is one of the most important risk factors for future fatal and non-cardiovascular diseases, stroke and kidney failure. In this article, Machine Learning (ML) is used to design effective models for predicting the long-term risk of older participants (over 50 years old) being diagnosed with hypertension. Our purpose is to train models with high sensitivity in identifying subjects at risk to avoid the future development and occurrence of hypertension following the proper interventions. In the context of the adopted methodology, two different class balancing methods are considered, under which features ranking is applied, and two ML models (namely, Decision tree and Naive Bayes) are compared based on Precision, Recall, F-Measure, Accuracy and Area Under Curve (AUC)
Project Documentation: https://drive.google.com/open?id=1OOgo30mpaaCxzx3L7cNmJQ64dMbigzaB
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Timestamp: 4-16-2024 15:33:25
Email Address: dinesh20010128@gmail.com
Name of The Student as Per SSC: GUTTULA DINESH KUMAR
Regd Number: 22A21F0074
Project Title: Research on Artificial Intelligence Deep Learning to Identify Plant Species
Project Abstract (only): Nowadays, people pay more attention in artificial intelligence(AI) research, and they try to make Al smarter. The machine learning became a popular subject, especially in object recognition area. Aiming at providing a faster and more accurate plant species recognition program, the author introduced the deep learning and convolution neural network(CNN), and decided to build a CNN project with pycharm, anaconda, keras to find the best way to improve recognition program accuracy and recognition speed. The author tried to change the learning epoch time and learning data set capacity to found the best solution. After tests were finished, the result of output plots analyze is that both adding learning epochs time and extend training image set are all helpful to improve recognition accuracy and speed. As for the effect of increase learning time, it is more obvious in improving accuracy while extend training set size, which is a better method to reduce recognition time. The end of the thesis contained the experiment result, the deficiency of this essay and the future prospect forecast of the machine learning applied in plant area.
Project Documentation: https://drive.google.com/open?id=1jzPx2dka0aLa8EaBIk5p2xXfr-KgBJxS
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Timestamp: 4-16-2024 19:04:29
Email Address: chadaramveerapavan2002@gmail.com
Name of The Student as Per SSC: CHADARAM VEERA PAVAN
Regd Number: 22A21F0013
Project Title: Spammer detection and fake user identification on social networks
Project Abstract (only): Social networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased those results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features.We are hopeful that the presented study will be a useful resource for researchers tofind the highlights of recent developments in Twitter spam detection on a single platform.
Project Documentation: https://drive.google.com/open?id=1dqg7Mfcxd12h25EIgtNveyMotFKEqpyQ
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Timestamp: 4-16-2024 21:32:25
Email Address: saikumarkotichukkala@gmail.com
Name of The Student as Per SSC: KOTICHUKKALA SAI KUMAR
Regd Number: 22A21F0083
Project Title: Forest Fire Detection and Protection Based on Convolutional Neural Network Using Deep learning Models
Project Abstract (only): Every year, thousands of forest fire across the globe cause disasters beyond measure and description. There are a huge amount of very well studied solutions available for testing or even ready for use to resolve this problem. People are using sensors to detect the fire. But this case is not possible for large acres of forest. In this paper, we proposed a new approach for fire detection, in which modern technologies are used. In particular, we proposed a platform of Artificial Intelligence. The computer vision methods for recognition and detection of smoke and fire, based on the still images or the video input from the cameras. Deep learning method “convolution neural network” can be used for finding the amount of fire. This will enable the video surveillance systems on forest to handle more complex situations in real world.
Project Documentation: https://drive.google.com/open?id=1AMZhbpbun45xjleQraZeqZToNT5jyQdM
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Timestamp: 4-16-2024 23:07:02
Email Address: gunnudileepkumar@gmail.com
Name of The Student as Per SSC: GUNNU DILEEP KUMAR
Regd Number: 22A21F0030
Project Title: Prediction Of Used Car Prices Using Artificial Neural Networks And Machine Learning
Project Abstract (only): The number of cars on Mauritian roads has been rising consistently by 5% during the last decade. In 2014, 173 954 cars were registered at the National Transport Authority. Thus, one Mauritian in every six owns a car, most of which are second hand reconditioned cars and used cars. The aim of this study is to assess whether it is possible to predict the price of second-hand cars using artificial neural networks. Thus, data for 200 cars from different sources was gathered and fed to four different machine learning algorithms. We found that support vector machine regression produced slightly better results than using a neural network or linear regression. However, some of the predicted values are quite far away from the actual prices, especially for higher priced cars. Thus, more investigations with a larger data set are required and more experimentation with different network type and structures is still required in order to obtain better predictions.
Project Documentation: https://drive.google.com/open?id=1zYhLayIx0mb6UEq5cd7Nb162-DZlKX7y
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Timestamp: 4-17-2024 10:27:15
Email Address: nareshjonnala6016@gmail.com
Name of The Student as Per SSC: JONNALA NARESH
Regd Number: 22A21F0077
Project Title: Machine Learning based Weather Prediction: A Comparative Study of Regression and Classification Algorithms
Project Abstract (only): Accurate weather forecasting is essential in many industries, including agriculture, transportation, and disaster management, making it a prime use case for machine learning algorithms. In this study, we investigate how to forecast several types of weather, including rain, sunshine, clouds, fog, drizzle, and snow, using a variety of fundamental machine learning methods and boosting algorithms. To train and evaluate the various algorithms, we utilized a dataset made up of historical meteorological data, including characteristics like temperature, humidity, wind speed, and pressure. We performed tests on many machine learning methods, some of which you may be familiar with: decision trees, random forests, naive bayes, knearest neighbors, and support vector machines. We also used boosting techniques like XGBoost and AdaBoost to further enhance the precision of our forecasts. Our results indicated that XGBoost and AdaBoost, two popular boosting algorithms, achieved the highest levels of accuracy (87.86% and 87.33%) compared to the other algorithms we tested. The findings were verified using ROC Curve Analysis and Lift Curve Analysis, which demonstrated that the XGBoost and AdaBoost models performed better in terms of true positive rate, false positive rate, and lift.
Project Documentation: https://drive.google.com/open?id=1K7H_kbqznekMPgjvxbj_4Q5zEy3QN4s4
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Timestamp: 4-18-2024 19:11:31
Email Address: chandusekhar6404@gmail.com
Name of The Student as Per SSC: NADIPUDI POORNA CHANDRA SEKHAR
Regd Number: 22A21F0098
Project Title: LIVER DISEASE PREDICTION USING MACHINE LEARNING CLASSIFICATION TECHNIQUES
Project Abstract (only): This paper explores the application of supervised machine learning on liver patient data from the UCI Repository. Various algorithms including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Gradient Boosting, Extreme Gradient Boosting, and LightGB are employed to predict future patient outcomes. Through feature selection, these algorithms demonstrate promising accuracy, offering potential for improving patient care based on historical data analysis.
Project Documentation: https://drive.google.com/open?id=1rjXhikIqkMI3Ctru8ZvZdG3TXnspZGOm
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Timestamp: 4-18-2024 19:27:09
Email Address: himanthkavuru143@gmail.com
Name of The Student as Per SSC: KAVURU HIMANTH SAI
Regd Number: 22A21F00A8
Project Title: Comparison Of Different Machine Learning Methods Applied to Obesity Classification
Project Abstract (only): Estimation for obesity levels is always an importanttopic in medical field since it can provide useful guidance for people that would like to lose weight or keep fit. The article triesto find a model that can predict obesity and provides people with the information of how to know overweight. To be more specific, this article applied dimension reduction to the data set to simplifythe data and tried to figure out a most decisive feature of obesitythrough Principal Component Analysis (PCA) based on the dataset. The article also used some machine learning methods likeSupportVector Machine (SVM), Decision Tree to do predictionof obesity and wanted to find the major reason of obesity. Inaddition, the article uses Artificial Neural Network (ANN) to doprediction which has more powerful feature extraction ability todo this. Finally, the article found that family history of obesity isthe most decisive feature, and it may because of obesity may begreatly affected by genes or the family eating diet may have greatinfluence. And both ANN and Decision tree’s accuracy ofprediction is higher than 90%.
Project Documentation: https://drive.google.com/open?id=1Lv8zisdSIV6ymcy_QEBxGw8Ae8w_mqw9
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Timestamp: 4-18-2024 20:32:45
Email Address: sivasankarguttula99@gmail.com
Name of The Student as Per SSC: GUTTULA SIVASANKAR
Regd Number: 22A21F0033
Project Title: Cyber attack and Mitigation for Distributed Systems via Machine Learning
Project Abstract (only): This project aims to enhance the accuracy of machine learning models utilized in distributed systems for cyberattack detection and mitigation. With the increasing sophistication of cyber threats, traditional rule-based methods often fall short in providing robust defense mechanisms. Machine learning offers a promising avenue for bolstering security by leveraging data-driven approaches. However, the accuracy of these models is contingent upon the quality and quantity of data available for training. To address this limitation, this project explores augmentation techniques to enhance the effectiveness of machine learning models in detecting and mitigating cyberattacks within distributed systems. By integrating augmentation strategies with machine learning algorithms, this research seeks to improve model robustness and resilience against adversarial attacks, thereby fortifying the security posture of distributed systems.
Project Documentation: https://drive.google.com/open?id=1Iijh-EPhnxC-z0fRwRtGIQ81eLtT_CtW
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Timestamp: 4-19-2024 10:25:08
Email Address: bujjinandu67@gmail.com
Name of The Student as Per SSC: YEDIDI NAMRATHA
Regd Number: 22A21F00D1
Project Title: Towards Understanding Fairness and its Components Ensemble in Machine learning
Project Abstract (only): Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way. How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble? Can fair learners result in an unfair ensemble? Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in different categories of ensembles. Understanding the impact of ensemble hyperparameters on fairness will help programmers design fair ensembles. Today, we do not understand these fully for different ensemble algorithms. In this paper, we comprehensively study popular real-world ensembles: Bagging, Boosting, Stacking, and Voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design. Finally, our benchmark can be leveraged for further research on fair ensembles. To the best of our knowledge, this is one of the first and largest studies on fairness composition in ensembles yet presented in the literature.
Project Documentation: https://drive.google.com/open?id=1jMLtjBpEgOVl0tSKJrM-DDVaTdnTPeJH
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Timestamp: 4-19-2024 11:06:47
Email Address: mokasai1234@gmail.com
Name of The Student as Per SSC: MOKA SAIKRISHNA
Regd Number: 22A21F0096
Project Title: Image based bird species identification using convolutional neural network
Project Abstract (only): Life's routine tempo appears to be rapid and energetic and includes diverse tasks. Bird-watching is a popular hobby which offers relaxation in everyday life. Innumerable people visit bird sanctuaries to observe the elegance of different species of birds. To provide birdwatchers with a convenient tool for identifying the birds in their natural habitat, we developed a Deep Learning model to help birders recognize 60 bird species. We implemented this model to extract information from bird images using the Convolutional Neural Network (CNN) algorithm. We gathered a dataset of our own using Microsoft’s Bing Image Search API v7. We created an 80:20 random split of the data. The classification accuracy rate of CNN on the training set was observed to be 93.19%. The accuracy on testing set was observed to be 84.91%. The entire experimental research was carried out on Windows 10 Operating System in Atom Editor with TensorFlow library.
Project Documentation: https://drive.google.com/open?id=1K_lS6mFRy1-O5BmnVNp9BujuPnFRBr-6
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Timestamp: 4-19-2024 11:13:21
Email Address: varaprasadyandras@gmail.com
Name of The Student as Per SSC: YANDRA VARAPRASAD
Regd Number: 22A21FOOC7
Project Title: Scrutinizing machine learning models for cancer prediction
Project Abstract (only): Due to some abnormal changes in genes of cells, enforces cells to divide uncontrollably, due to which tumors are formed, which infiltrates and damages the normal body tissues, and this condition is called “Cancer”. Lung cancer is a type of cancer where the infected cells in the lungs multiply rapidly at a high rate. This abnormal growth of cells, which eventually leads to cancer can be identified using modern data analysis. Detecting cancer symptoms at an early stage plays a crucial role for the patients who may suffer later, if not detected. One of the major problems is the increasing fad of smoking tobacco in youngsters. Air pollutants from industries which get inhaled by people are some of the main causes of increasing lung cancer in India. The main focus of this study is to predict lung cancer in different patients using Machine Learning (ML) algorithms such as a random forest classifier (RFC), k-nearest Neighbour (KNN), K-means, Support vector machine (SVM), and decision tree classifier (DTC). The key objective of this research is the analysis of different machine learning algorithms based on their performance metrics.
Project Documentation: https://drive.google.com/open?id=1c5wc8Jq-Ug26JMAmKuqBfydyEpRWdV7Y
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