| Title of Project/Research Work | Student Name(s) | Guide/Supervisor Name & Designation | Department/Program | Year of Completion | Certificate File Name (PDF) | Abstract/Summary (300 words max) |
| HealMe- Your Pocket Doctor | Abhinav Srivastava Aastha Gupta Ayush Kumar Rai,Aditya Jaiswal |
Mr. Indrajeet Kumar Sinha, Assistant Professor | AIML | 2026 | In Progress | HealMe is an AI-powered healthcare system designed to provide early lung cancer risk assessment and streamline patient-doctor interaction through a unified digital platform. Lung cancer remains one of the leading causes of mortality worldwide, primarily due to late-stage diagnosis. The HealMe application addresses this challenge by implementing a hybrid diagnostic approach that combines symptom-based prediction and CT-scan analysis using Convolutional Neural Networks (CNNs) . The system’s first diagnostic module evaluates a patient’s health status through a structured 16-question symptom assessment form and predicts lung cancer probability using a trained machine learning model. The second module enables users to upload CT scan images, which are processed and classified by a CNN to detect early-stage cancer indicators. The integration of these two complementary diagnostic pathways enhances accuracy and supports early identification. |
| TINYWIZ-AI powered Learning Companion | Sujal Raj, Shivam Rai, Divyansh Yagyik, Karan Singh Yadav |
Mr. Akash Bhashney, Assistant Professor | AIML | 2026 | In Progress | The increasing exposure of children to smartphones and digital media has transformed the learning landscape, often creating challenges related to unregulated screen time, reduced attention spans, and exposure to non-educational or inappropriate content. These challenges are further amplified for working parents who may be unable to consistently supervise or guide their child’s digital interactions. To address this gap, the present work proposes TinyWiz, an AI-powered Parent–Child Learning System designed to combine emotional presence, personalized education, and digital wellbeing management within a unified platform. The system leverages a voice cloning engine to replicate a parent’s voice, enabling stories, educational content, and instructions to be delivered in a familiar and emotionally reassuring manner. A Large Language Model (LLM) generates personalized stories, quizzes, and learning modules based on the child’s behavior, learning pace, and interaction patterns. Telemetry-driven analytics continuously assess engagement and performance, allowing the platform to adapt content difficulty in real time while promoting healthier digital habits through screen-time regulation and automated lock features. |
| Annapurna.AI-Nutrient Intake Tracker and Smart Meal Planner | Rohit Singh Bhojak, Nilisha Bharadwaj, Sanidhya Mishra, Sanskriti Singh |
Mr. Pramod Kumar Singh, Assistant Professor | AIML | 2026 | In Progress | With the rapid rise in lifestyle- related diseases such as obesity, diabetes, and cardiovascular disorders, nutrition has emerged as a crucial factor in preventive healthcare. Despite the availability of numerous diet and fitness applications, most focus primarily on calorie counting and macronutrient tracking, while neglecting micronutrient balance, cultural eating patterns, and region- specific dietary preferences. Annapurna.AI is designed to overcome these challenges by providing a personalized nutrition dashboard tailored specifically to Indian food habits. |
| GLOFs-Early warning System for Glacial Lake Outburst Floods | Asmit Patel, Molisha Agarwal, Krishna Yadav, Vedanshi |
Dr. Asha Rani Mishra, Associate Professor | AIML | 2026 | In Progress | Glacial Lake Outburst Floods (GLOFs) occur when ice barriers collapse, releasing large volumes of water that cause severe downstream damage. Due to climate change, glacial lakes are expanding, making GLOFs more frequent and unpredictable. Traditional monitoring like manual observation and fixed maps cannot track these rapidly changing glacial conditions. SafeFlow addresses this issue through an end-to-end deep learning–based forecasting system that predicts GLOF Risk Scores. |
| PCOS/PCOD Detection using Machine Learning | Pranjana Dwivedi, Shambhavi Singh |
Ms. Jyoti Nagpal, Assistant Professor | AIML | 2026 | In Progress | Polycystic Ovary Syndrome (PCOS) and Polycystic Ovarian Disease (PCOD) are two of the most prevalent hormonal disorders seen in women during their reproductive years. If these disorders remain unrecognized, they may contribute to irregular menstrual cycles, infertility, obesity, and multiple metabolic issues. Due to symptom overlap and variations in presentation, PCOS and PCOD may be challenging to identify early using solely traditional diagnostic methods. Machine learning (ML) as a complementary approach to healthcare is an innovative and exciting opportunity to identify potential cases of PCOS and PCOD through data-driven processes. This project, "PCOS and PCOD Detection Using Machine Learning," will develop a predictive intelligent model that can identify the likelihood of PCOS/PCOD through clinical and lifestyle features. |
| Embrace Autism- Advocating for Inclusion,Special Attention and Inclusiveness of Neurodiverse Persons | Purushottam Kumar, Naina , Nikhil Vashishth , Arpana Singh |
Dr. Uday Singh, Assistant Professor | AIML | 2026 | In Progress | Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects how individuals perceive and interact with the world around them. People with autism often experience challenges in social communication, behavioural adaptation, and sensory processing. Embrace Autism is an innovative web-based platform designed to support individuals on the autism spectrum by integrating learning, community engagement, and early screening assistance through artificial intelligence. The project addresses the existing gap in digital resources, where most platforms are either limited to medical diagnosis or provide fragmented support. Recognizing the need for a holistic approach, Embrace Autism combines interactive learning modules, mentorship programs, and AI-driven visual detection to create a comprehensive and inclusive ecosystem. |