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6 papers in Computer ScienceClear
  1. Computer ScienceIndia

    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.

    KOMAL SINGH MARKAM · Parul Institute of Engineering and Technology, Parul University, Vadodara, India

  2. Computer ScienceIndia

    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%.

    Shivani Mangesh Bhosale · University of Mumbai

  3. Computer ScienceIndia

    Sketch2Animate

    Sketch2Animate is an AI-driven system designed to automatically transform hand-drawn sketches into animated sequences with minimal human intervention.

    DHIRAJ MANGESH RUPVATE · UNIVERSITY OF MUMBAI

  4. Computer ScienceIndia

    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.

    Basavakruti Umesh Mantale · University of Mumbai

  5. Computer ScienceIndia

    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.

    Soham Sanjay Kank · Vishwaniketan's Institute of Management Entrepreneurship and Engineering Technology.

  6. Computer ScienceIndia

    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

    Sachin Chaurasiya · Vishwaniketan iMEET

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