This project aims to develop an AI-powered platform that helps engineering students manage academics, skills, and time effectively while reducing frustration and burnout. The system will generate personalized study roadmaps and weekly schedules based on the student’s branch, semester, goals (placements, research, GATE, etc.), available study hours, and weak subjects. The platform will recommend resources such as coding sheets, books, and learning materials while also providing productivity and stress analysis through a simple burnout risk scoring mechanism. The goal is to help students balance CGPA, coding, projects, and personal well being in a structured and sustainable way. The project will involve concepts from AI/ML, recommendation systems, backend development, database management, and analytics/dashboard visualization. Expected Features are Personalized academic roadmap, Smart time management planner, Resource recommendation system, Productivity and burnout analysis Progress tracking dashboard. Tech Stack are Python, Scikit-learn, STREAMLIT /React, SQLite/MySQL and optionally backend integration FASTAPI/FLASK Looking for teammates interested in AI/ML, frontend, backend and EdTech related development.
The goal of the project is to build a prototype of a Plant which can desalinate sea-water using solar thermal technology is goes as follows, treated sea water is sent through the Concentrated Solar Plant setup through which the sea-water is converted to steam using concentrated solar light & the sediments stay at the bottom of the tank & the vapours get condensed from steam to get freshwater, the steam moves a turbine to make electricity & finally the sediments are collected to produce salt through this process from sunlight we get electricity, freshwater & salt, the plant runs 24*7 whole year relaying on molten salt method of heat storage or sand battery setup, We are looking on enthusiastic team members willing to work the whole summer with dedication, no prerequisites need pure energy & enthusiasm to build the prototype as the end goal of the project during summer.
Automated Attendance and Movement Tracking System using Face Recognition is an AI-based attendance system that automatically marks attendance in a classroom using face recognition while also tracking student movement and punctuality. In this project, instead of uploading existing images, student faces are registered in real time using a camera connected to a Raspberry Pi, ensuring that up-to-date and accurate facial data is captured. During class, the same camera continuously monitors the classroom, detects multiple faces in real time, and matches them with the registered data to identify students. The system ensures that attendance is marked only once per student per session and further records entry and exit times, calculates how long a student stays outside the classroom, and detects late arrivals by comparing entry time with the class start time. It also applies rule-based conditions, such as not marking attendance if a student arrives after a defined threshold like 10–15 minutes. The project involves concepts from machine learning and AI (face detection and recognition), computer vision (image processing), IoT and hardware integration (camera with Raspberry Pi), backend development (APIs and logic handling), database management (storing student data and logs), and real-time processing (continuous monitoring). The final system provides automatic attendance marking, entry/exit tracking, late arrival analysis, and rule-based attendance decisions, making it a complete AI-powered solution for managing and analyzing student presence in real time.
This project proposes the development of a machine learning framework to predict human disengagement risk across domains such as employee attrition and student dropout. The core idea is to model behavioral patterns that indicate a decline in participation, enabling early intervention and improved retention strategies. The system will be built using a stacking ensemble architecture composed entirely of tree-based models. At the base level, multiple models such as Decisions Trees, Random Forests and Gradient Boosting methods( XG Boost, Light GBM) will independently learn different aspects of the data, capturing non- linear relationships and feature interactions. A meta learner will then combine these predictions to produce a more robust and accurate final output. The project will focus not only on prediction accuracy but also on interpretability. Feature importance analysis and explainability techniques ( such as SHAP values) will be incorporated to understand the key factors contributing to disengagement. This is particularly important in real-world applications where actionable insights are required by HR teams or educational institutions.
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