Computer Science Research Papers
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Latest Computer Science papers
Snakebite Detection and Treatment Using AI
Snakebite envenomation remains a critical public health challenge, particularly in rural and resource-limited regions where timely diagnosis and treatment are often unavailable. Traditional diagnostic methods rely heavily on clinical symptoms and patient history, which can lead to delays and misclassification. This research introduces an AI-driven snakebite detection and treatment system that leverages machine learning and computer vision to classify snakebite images into specific snake types and recommend appropriate treatment protocols. The model is trained using convolutional neural networks (CNNs) and deployed via the Django framework, ensuring accessibility through a user-friendly interface.
By KOMAL SINGH MARKAM
Expansion.AI: A Globally Scalable AI-Driven Decision-Support Framework for Autonomous International Market
International business expansion is a high-stakes strategic process requiring the synthesis of fragmented data across regulatory, economic, and competitive domains. Traditional expansion strategies rely on human-centric consultancy, which is often cost-prohibitive for Small and Medium Enterprises (SMEs) and prone to cognitive bias. This paper introduces Expansion AI, an integrated decision-support framework that leverages Multi-Criteria Decision Making (MCDM) and Explainable Artificial Intelligence (XAI) to automate global market evaluation. Utilizing real-time data from the World Bank, WTO, and UN Comtrade, Expansion.AI generates transparent, data-driven recommendations that reduce the time-to-insight for strategic planning by over 70%.
By Shivani Mangesh Bhosale
Sketch2Animate
Sketch2Animate is an AI-driven system designed to automatically transform hand-drawn sketches into animated sequences with minimal human intervention.
By DHIRAJ MANGESH RUPVATE
VITA: Visual Intelligent Teaching Assistant
This paper introduces VITA (Visual Intelligent Teaching Assistant), an artificial intelligence–driven system for the automatic generation of animated mathematics explanations. Conventional methods for creating visual educational content are often time-intensive and require specialized skills, limiting their scalability and accessibility. VITA addresses this limitation by enabling users to submit mathematical queries through a simple interface, which are then interpreted using a Large Language Model (LLM). The system converts these queries into executable animation scripts using the Manim library, producing step-by-step visual explanations that may be supplemented with narration. By transforming textual queries into dynamic visual content, the proposed approach enhances conceptual understanding and supports personalized learning experiences. Despite challenges related to computational cost and script accuracy, the system demonstrates strong applicability in digital learning environments. VITA offers a scalable and efficient solution for automated visual instruction in mathematics.
By Manan Patil
Titanium: AI Based Fraud Detection System
The increasing use of digital payment systems and online financial services has led to a significant rise in fraudulent transactions. Traditional rule-based fraud detection methods often fail to identify complex and evolving fraud patterns. This paper presents an AI-powered fraud detection system that utilizes machine learning techniques to analyze transaction data and identify suspicious activities. The proposed system processes historical transaction datasets, performs data preprocessing, and applies machine learning algorithms to classify transactions as legitimate or fraudulent. Experimental results demonstrate that the system can effectively detect fraudulent transactions with improved accuracy and reduced false positives compared to traditional approaches. The proposed model enhances the security of digital financial systems and helps financial institutions minimize financial losses caused by fraud.
By Pranav Mahesh Khamitkar
An Intelligent Framework for AI-Based Crime Prediction and Real-Time Emergency Response in Smart Policing Systems
The increasing complexity of urban environments and rising crime rates demand intelligent, data-driven solutions for effective law enforcement. This paper presents an AI-driven Crime Management System that integrates machine learning techniques — Random Forest for crime classification and K-Means clustering for hotspot detection — to analyze historical and real-time crime data. A real-time SOS emergency module powered by Socket.IO enables instant communication between citizens and law enforcement with live location tracking. Additionally, the system incorporates automated FIR processing with risk-based prioritization. Experimental evaluation demonstrates significant improvements in response time (from 8–10 min to 2–3 min), crime prediction accuracy (≈90%), and resource allocation efficiency, providing a scalable and intelligent framework for smart policing.
By Omkar Rathod
DDAS: A Lightweight Hash-Based Framework for Real-Time Duplicate Detection in Multi-Tenant Web Applications
As the need for storage grows in multi-tenant web apps, redundant data ingestion is still a major problem. This paper presents the Data Download Duplicate Alert System (DDAS), a streamlined framework designed to alleviate storage inefficiencies via real-time duplicate detection. DDAS uses SHA-256 cryptographic hashing in a Flask-based backend to make unique digital signatures for each file that comes in. Before allocating storage, these hashes are checked against a central MySQL relational database. The results of the experiment show that DDAS works well to get rid of unnecessary uploads while keeping latency to a minimum. This study offers a scalable, low-overhead way to improve data integrity and make better use of storage in distributed environments.
By Basavakruti Umesh Mantale
Academic Assistant AI Platform Integrated Using Chatbot.
The Academic Assistance AI Platform is designed to revolutionize student support through the use of intelligent conversational artificial intelligence. This platform leverages advanced natural language processing and large language models (LLMs) to provide real-time, context-aware academic guidance. Students can interact with the AI assistant to resolve academic queries, access learning materials, check schedules, and receive personalized assistance. Developed using Python, Flask, MongoDB, and DeepSeek LLM API, the system seamlessly integrates AI with college data systems to deliver fast, reliable, and efficient responses. This report outlines the motivation, design, implementation, and future scope of the project, emphasizing its potential to enhance academic engagement and accessibility. This paper details the research, design, implementation and evaluation of the platform, demonstrating its potential to revolutionize student support service.
By Soham Sanjay Kank
Farmatics
Plant diseases are one of the leading causes of agricultural losses worldwide, annually destroying an estimated 20–40% of global crop production and threatening the food security of billions. Early and accurate identification of plant diseases is critical to enabling timely intervention and minimizing crop losses. Farmatics is a deep learning-based plant disease detection system specifically designed to assist farmers in identifying diseases at an early stage across multiple crop varieties directly from leaf photographs captured via a mobile or web interface. The system leverages a custom Convolutional Neural Network (CNN) architecture trained on the PlantVillage benchmark dataset comprising 54,306 images across 38 disease classes and 14 crop varieties. Unlike existing solutions that are limited to single-crop detection, require constant internet connectivity, or lack early-stage detection capability, Farmatics supports multi-crop inference and is optimized for practical agricultural deployment. Experimental evaluation demonstrates that the proposed system achieves 96.4% classification accuracy, outperforming established baselines including VGG16 (91.2%), ResNet50 (92.5%), MobileNetV2 (89.7%), and InceptionV3 (93.1%). The system further provides confidence-scored disease identification, crop-specific treatment recommendations, and a confidence thresholding mechanism for quality assurance. Farmatics represents a significant step toward democratizing precision agricultural technology for smallholder farmers in India and comparable developing economies. Index Terms — Plant Disease Detection, Convolutional Neural Network, Deep Learning, Precision Agriculture, Image Classification, Early Detection, Multi-Crop Analysis, PlantVillage, Transfer Learning, Computer Vision.
By Ayush Santosh Naik
Suraksha Setu: AI-Driven Criminal Identification Using Facial Recognition Technology with Aadhaar Integration for Scalable Intelligent Public Safety in Urban India
Suraksha Setu is an AI-powered dual-platform public safety ecosystem designed to address the critical operational gaps in Indian law enforcement. The system integrates Facial Recognition Technology (FRT) using the DeepFace framework (VGGFace2 backbone), real-time multi-class object detection via YOLOv8 fine-tuned on a 14,200-image custom dataset, and biometric identity verification through the UIDAI Aadhaar Authentication API 2.5. The architecture comprises two primary subsystems: (1) a Citizen Safety Portal providing geofence-triggered crime alerts via Firebase Cloud Messaging, SOS GPS triangulation, and a community gamification layer; and (2) a Police Operational Dashboard delivering live AI-monitored CCTV analytics, LIDAR/UGS-based restricted-zone intrusion detection, crime heatmap visualization via MongoDB GeoJSON, and centralized Aadhaar-linked offender database access. Comprehensive evaluation demonstrates that DeepFace achieves 94.8% facial recognition accuracy under operational CCTV conditions, surpassing FaceNet (91.7%) and ArcFace (89.3%). YOLOv8 attains a mean Average Precision (mAP@0.5) of 91.3% across five threat categories, with weapon detection reaching 92.1% F1-score. System response latency averages 1.2 seconds for SOS dispatch and 2.8 seconds for FRT matching — a 75–80% reduction over conventional systems. An ablation study confirms that each architectural component contributes measurably to system performance. A security assessment across seven threat vectors demonstrates compliance with NIST SP 800-175B, UIDAI regulations, and OWASP Top-10 standards. Suraksha Setu represents a replicable, nationally scalable framework for AI-integrated, constitutionally compliant smart policing
By Sachin Chaurasiya
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