2025-26

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http://192.168.75.5:4000/handle/123456789/19553

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A Compact Speech-Driven Framework for Multilingual Voice Exchange Using Indian Languages
(2026-02-04) G Ramana Gowda Patil 1NH22CS073; Manjunatha Royal J 1NH22CS090; Yashwanth Reddy K V 1NH22CS101; K S Sharmila 1NH22CS109
The increasing demand for real-time multilingual communication has highlighted the need for speech systems that are lightweight, efficient and capable of delivering natural and accurate outputs. Existing solutions often rely on heavy computational resources and struggle with preserving tone, clarity and expressiveness, which limits their usability in practical environments. To address these challenges, this work introduces INDRA-Lite, a compact three stage framework designed for seamless speech-to speech translation across regional languages. The system incorporates a phoneme-aware speech recognition module that captures linguistic tones and acoustic variations, a bilingual mapping engine built on a gated recurrent architecture with language tokens for precise alignment and a prosody-driven synthesis unit optimized to maintain intonation and clarity in the generated speech.
A Hybrid CNN and Gradient Boosting Framework for Spatio-Temporal Prediction Of Urban Crime Spots
(2026-02-09) Akshara Nair 1NH22CS014; Devarshi Khilariwal 1NH22CS065; Sohani Nagappa 1NH22CS256; Ashmith Maddala 1NH22CS273
Urban crime continues to pose significant challenges to public safety and policy enforcement, necessitating predictive systems that are both accurate and actionable. This research proposes a hybrid framework integrating Convolutional Neural Networks (CNNs) with Gradient Boasting techniques specifically CatBoost and XGBoost for the spatio-temporel prediction of urban crime hotspots. By leveraging historical crime dats enriched with spatial, temporal, and socio-demographic features, the system generates accurate forecasts of high-risk zones. CatBoost nancies categorical features such as crime types and locations, while XGBoost captures trends from numerical variables like population density and socioeconomic indicators. These models are ensemble-combined to improve predictive reliability. Additionally, CNNs are applied to heatmap-transformed data to extract spatial and temporal crime patterns.
A Hybrid Deep Learning Model for Multi Source Disaster Recognition Using ResNet-50 and Efficient Net
(2026-02-04) Kamalesh Navaneethakumar 1NH22CS103; Kamil Nissar 1NH22CS104; Faheera Kounain 1NH23CS404; Pooja Prakash Janagouda 1NH23CS411
The proposed system is designed to classify disasters using heterogeneous visual images. The framework integrated two state-of-the-heart convolutional neural networks: ResNet-50, which provides robust hierarchical feature extraction through residual learning, and EfficientNet, which achieves high accuracy with optimized computational efficiency using compound scaling.
A Lightweight Radar–Camera Fusion Framework For Real-Time Drone Detection And Trajectory Prediction
(2026-02-04) Nishanth Aluvala 1NH22CS147; P Krishna Kowshik Reddy 1NH22CS153; Levaka Umar Reddy 1NH22CS117; K Ashraf Ahmed 1NH22CS102
Unmanned aerial vehicles (UAVs), commonly known as drones, are increasingly used in civil, commercial, and security applications, leading to growing concerns related to airspace safety, privacy, and unauthorized intrusions. Traditional drone monitoring systems that rely on a single sensing modality often struggle under real-world conditions such as poor lighting, occlusions, cluttered backgrounds, or adverse weather, resulting in unreliable detection and tracking. To address these challenges, this project presents a lightweight radar–camera fusion framework for real-time drone detection and trajectory prediction. The system combines visual information from a monocular camera with motion-aware cues from radar data to achieve robust and consistent performance. A YOLOv8n model is employed for efficient drone detection from video frames, while a radar CNN processes range-Doppler maps to generate confidence scores that remain reliable even when visual cues degrade.
A Project Report on Kannada Kagunitha
(2026) Geethashree J M 1NH22CS079; Hema Pushpa J 1NH22CS088; Ashok G 1NH23CS402; Sunil Kumar D O 1NH23CS420
Educational Technology (EdTech) has advanced rapidly in the past decade, transforming how learners interact with academic content. Traditional classroom teaching has been augmented by digital learning platforms, self-paced modules, and interactive exercises that make education more accessible and enjoyable. This evolution has provided significant benefits for language learning, especially for early learners who respond well to visual, auditory, and interactive stimuli.
A Secure File Sharing System
(2026-02-06) T Praneeth 1NH22CS229; Uday Kumar V 1NH22CS233; VVS Abhiram 1NH22CS238; Venkat Mohan Krishna V 1NH22CS241
The Secure File Sharing System is an application designed to provide a fast, reliable, and user-friendly method of transferring files between devices connected to the same local network. This system enables seamless communication between users by automatically discovering nearby devices, establishing secure connections, and allowing efficient file transfers without the need for internet access or external cloud services. The system consists of three major components: a Discovery Server for peer detection, a Backend Service for sending and receiving files, and a Frontend Interface that enables users to easily choose files, view active devices, and monitor transfer status.
A Smart IOT -ML Framework for Adaptive Crop and Fertilizer Recommendation in Precision Agriculture
(2026) Aishwarya K N 1NH22CS012; Bhagyashree M 1NH22CS043; Chaya K N 1NH22CS055; Deepthishree V 1NH22CS064
Agriculture has always been one of the most significant contributors to the global economy and human survival. It serves as the foundation for food security, economic development and employment generation, particularly in developing countries like India. However, in recent years, the agricultural sector has been facing several critical challenges such as climate change, soil degradation, water scarcity, unpredictable rainfall and the excessive use of fertilizers and pesticides. These challenges not only affect crop productivity but also threaten environmental sustainability and farmer livelihoods.
Accurate Evaluation System for Web Based Exam Management
(2026-02-06) Raushni P 1NH22CS176; Sanjana M 1NH22CS191; Shradhdha Sharad Kulkarni 1NH22CS202; Sinchana N 1NH22CS209
The Accurate Evaluation System is an application that aims at offering a smooth and effective system of conducting exams, managing students, and assisting the administration-related services within the learning institutions. This web-based system allows three roles, which are Admin, Student, and Teacher. Admins can control the users, assign teachers to the courses, and also check the profiles of the students. Students have the ability of enrolling, log-in, undertaking tests, and looking up their outcome and attendance. Educators will be able to test, tick attendance, and assess learners.
AGROXAI:Explainable Al for Smart Farming
(2026-02-04) Bachhala Venkata Parthu 1NH22CS039; Balla Pavan Kumar 1NH22CS040; Allagadda Pranay Karthik Reddy 1NH22CS016; Bonala Praneeth Kumar Reddy 1NH22CS051
It is designed as a support system that encompasses Machine Learning and Explainable Artificial Intelligence-XAI for accurate, transparent, and user-friendly predictions for crop recommendation, rainfall prediction, and yield forecasting. The web application lets the user enter the nutrient levels of the soil and climatic parameters through an interactive interface. Trained machine learning models compute predictions in real time.
AI Based Nanorobot Assisted Endoscopic Imaging For Early Detection Of Gastric Disorders
(2026-02-09) Chandresh M 1NH22CS054; Patan Izaz Khan 1NH22CS281; Agnes Arul 1NH22EC006; Akshayaasri S 1NH22EC009
This project introduces a comprehensive intelligent system designed for the early identification of gastric disorders through AI-driven CT image analysis, paired with a prototype for drug delivery inspired by nanorobotics. The early detection of stomach tumors can be quite difficult due to the subtlety of symptoms and the intricacies involved in manually interpreting CT images. To bridge this gap, the proposed model utilizes a Convolutional Neural Network (CNN) created with Python 3.13 and TensorFlow, which automatically classifies CT images of the stomach and accurately identifies potential tumor regions. The system employs sophisticated image preprocessing methods, including noise reduction, contrast enhancement, and normalization, to enhance diagnostic reliability.
AI Based Symptom Checker
(2026-02-05) D Sai Adithya 1NH22CS266; Y Raghava Tharun 1NH22CS252; Sudarshan R 1NH22CS219; Sunkara Dileep 1NH22CS223
The AI-Based Symptom Checker is an intelligent digital health support system designed to help users understand their health concerns in a calm, structured, and reliable manner. Traditional approaches to symptom understanding often involve random internet searches, static questionnaires, or rigid rule-based tools that either overwhelm users with worst-case scenarios or fail to capture the complexity of real symptoms. This system addresses those limitations by reimagining symptom assessment as a conversational, adaptive, and safety-focused experience that prioritizes clarity, personalization, and responsible guidance.
AI Powered Sign Language to Text Translator For Regional Languages
(2026-02-05) Ritisha Reddy Konda 1NH22CS180; S Ruchita 1NH22CS183; S V Sruthi 1NH22CS184; Sangeetha B 1NH22CS190
Communication between hearing-impaired individuals and the general population remains a significant challenge, particularly in multilingual countries like India where linguistic diversity adds an additional layer of complexity. Although several sign- language recognition tools exist, most are limited to static gesture detection, single- language support, restricted datasets, and non–real-time processing capabilities. To address these limitations, this project proposes an AI-powered, real-time Sign Language to Text Translator with integrated support for multiple Indian regional languages. The primary objective is to create an intelligent, accessible, and adaptive system that can accurately interpret hand gestures and convert them into meaningful, grammatically structured text, thereby enabling smoother and more inclusive communication. The system employs a deep learning pipeline built using Convolutional Neural Networks (CNNs) trained on a combination of publicly available and custom-curated datasets.
AI ToolKit: A Smart Way of Learning
(2026-02-04) Aditya Gopinath 1NH22CS010; Bipin Sai Surya Guttula 1NH22CS048; Aryan Mishra 1NH22CS029; Bhupalam Venkata Mohit 1NH22CS046
AI ToolKit: A Smart Way of Learning is an AI-powered educational platform designed to support students in understanding concepts across mathematics, science, and computer science through intelligent automation and interactive tools. The system combines a Django backend, a responsive Bootstrap and JavaScript frontend, a lightweight SQLite database, and Google’s Gemini API to deliver a seamless learning experience. AI ToolKit integrates four major modules—Smart Canvas, PDF Extraction and Summarization, MCQ Generator and Test Engine, and Path Finder (BFS Visualizer).
AI-Backed Risk Intelligence for Decentralized Credit Rating
(2026-02-09) Likith P Reddy 1NH22CS118; Manoj M 1NH22CS132
This projectpresentsanAI-drivencreditscorepredictionsystemintegratedwith block chain technology to ensure data transparency, privacy, and security. The system analyses key financial attributes such as income, savings, liabilities, and expenditure patterns to predict an individual’s credit score using advanced machine learning models Including Random Forest(RF), XGBoost, and LSTM. A user-friendly web application, developed using HTML, CSS, and Flask, allows users to register, login, and submit their financial details.
AI-Driven Skill Evaluation and Recruitment Platform with Blockchain Credentials
(2026-02-06) Sameer Yadav 1NH22CS274; Syed Rayan 1NH22CS277; Niraj Thapa R 1NH22CS280
The project titled "Al-Driven Recruitment Platform with Blockchain-Based Certification" aims to revolutionize how candidates and recruiters interact within a digital hiring ecosystem. Designed with two distinct dashboards, the platform empowers candidates to explore personalized job opportunities, attempt skill-based quizzes, and earn blockchain-backed certificates upon achieving a minimum qualifying score of 85%. These tamper-proof certificates enhance credibility and eliminate fraudulent claims during the hiring process. The candidate dashboard also includes real-time analytics, showcasing user performance and engagement. Additionally, the platform introduces a community module where users can create interest-based groups, exchange ideas, share code snippets, and collaborate on projects-fostering a more inclusive and dynamic tech community.
AI-Enabled Chronic Disease Management System Using Bidirectional LSTM with Interactive Web UI
(2026-02-03) Joyson C J 1NH22CS094; M S Vikram 1NH22CS122; Mahendhar Singh J 1NH22CS125; Nikhil Chander 1NH22CS145
This project aims to fill those gaps by creating an AI-Enabled Chronic Disease Management System. This system offers disease risk predictions, educational support, emergency assistance, and professionally formatted health reports—all in one platform. The system features a responsive, multi-step data collection interface along with an efficient Flask-based backend and a powerful Bi-Directional Long Short-Term Memory (BiLSTM) model. This design helps the system capture sequential symptom patterns, analyze various health traits, and produce probability-based predictions for multiple chronic disease types.
AI-Powered Smart Farming Assistant
(2026-02-04) Diganth Badrahally Kiran 1NH22CS068; Blessy Hephzibah Stephen 1NH22CS049; Aasthaa Sumeet Malik 1NH22CS005; Amogh Dayanand Patadi 1NH22CS018
Agriculture plays a vital role in ensuring food security and economic stability, yet farmers continue to face challenges related to crop selection, soil fertility management, plant dis-ease identification, and changing weather conditions. This project presents an AI-Powered Smart Farming Assistant, a comprehensive web- based application designed to assist farmers in making informed agricultural decisions. The system integrates multiple intelligent modules, including crop recommendation, fertilizer recommendation, plant disease identification, and real-time weather information.
AI-Rescue Force
(2026-02-09) Nivetha Madhavan 1NH22CS150; Harshitha M Anil Kumar 1NH22CS257; Mahi Srivastava 1NH22CS127; Vishal Shetty 1NH23CS422
With their growing frequency and unpredictable nature, disasters represent a serious threat to infrastructure and human life, necessitating quicker, more intelligent, and more coordinated responses. In order to automatically identify different disasters like floods, wildfires, and earthquakes from satellite and sensor data, this research suggests a multi-disaster detection and alarm system powered by deep learning, specifically Convolutional Neural Networks (CNNs). The suggested method provides a scalable, cross-disaster platform that not only classifies occurrences but also assesses their severity based on confidence scores, occurrence frequency, and location spread, in contrast to conventional systems that concentrate on a single hazard type and lack real-time responsiveness.
Al-Integrated Focus Learn
(2026-02-09) M Vashishta Varma 1NH22CS124; P Akshay Reddy 1NH22CS152; Tejas P 1NH22CS228; Jambu Jahnavi 1NH22CS260
Al-Integrated Focus Learn is a distraction-free e-learning platform designed to enhance focus, personalization and engagement in digital learning. Unlike traditional video platforms, it organizes educational content into structured learning joumeys where users can add individual YouTube videos or import entire playlists that are automatically converted into chapters for systematic learning. To promote focused study, the platform uses a custom video player that removes ads, recommendations and unnecessary interface elements. An integrated note-taking panel allows learners to write, save, and downioad notes alongside videos, supporting active leaming and better retention. The platform incorporates Al-powered features, including automatic note generation and a 24/7 Al tutor that helps users clarify doubts instantly.
AMB-Alert: V2V Emergency Response
(2026-02-05) Saran Kumar Sekar 1NH22CS194; Vismaya S 1NH22CS248; Rithika Rose Martin 1NH22CS270; Muhamed Shaheer VT 1NH22CS275
The project AMB-Alert: V2V Emergency Response is a cross-platform mobile application designed to improve emergency communication and reduce response time through real-time, technology-driven alerts. Developed using the Flutter framework, the system integrates Firebase for backend operations such as authentication, cloud data handling, and scalable message delivery. By incorporating Google Maps services, the application enables accurate geolocation tracking of emergency vehicles and real-time updates for nearby users. The addition of text-to-speech (TTS) ensures instant audio alerts, enabling faster recognition of emergency situations even when users are not actively viewing their devices.

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