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Final Year CSE Projects, Source Code, Documentation & 24/7 Assistance.
TRAVELYS – Smart Travel Risk & Cost Analytics System
TRAVELYS – Smart Travel Risk & Cost Analytics System TRAVELYS is a data-driven travel analytics and decision-support platform built with Flutter Web that helps users evaluate international travel destinations using multiple socioeconomic and environmental indicators. Instead of relying only on basic travel blogs or reviews, TRAVELYS aggregates global travel indexes, real-time weather data, currency exchange rates, and tourist attraction information to generate meaningful travel insights. The platform analyzes metrics such as safety, cost of living, quality of life, pollution, climate, and healthcare availability to help travelers make informed decisions before visiting a country. The application provides an interactive dashboard where users can explore country-level analytics, visualize travel-related indicators, and generate downloadable travel reports for deeper insights. Key Highlights: Data-Driven Travel Insights: Combines multiple global indicators including Quality of Life, Safety, Cost of Living, Healthcare, Climate, and Pollution to provide a comprehensive overview of travel conditions in different countries. Smart Travel Recommendation System: Generates a weighted travel recommendation score based on multiple indexes to help users identify destinations that best match their safety, affordability, and lifestyle preferences. Interactive Country Analytics Dashboard: Users can explore detailed country profiles displaying travel-related metrics, environmental conditions, and economic indicators in a clean and intuitive interface. Real-Time Travel Information: Integrates external APIs to fetch current weather conditions, currency exchange rates, and tourist attraction data for selected destinations. Tourist Attractions Explorer: Displays popular tourist destinations, landmarks, and points of interest using geographic data APIs to enhance travel planning. Downloadable Travel Reports: Users can generate downloadable reports summarizing travel insights, making it easier to save or share travel analysis. Share Travel Insights: Allows users to share country travel analytics through built-in sharing functionality. Secure User Authentication: User accounts are protected using Firebase Authentication with email verification to ensure secure access to the platform. Modern Responsive UI: Built using Flutter Web to provide a responsive, modern interface that works smoothly across desktops and browsers. Technology Stack Frontend: Flutter Web Dart Backend / Services: Firebase Authentication APIs Used: Geoapify API – Tourist attractions data Weather API – Real-time weather information Currency Exchange API – Global currency rates Data Sources: Global travel and lifestyle index datasets including: Quality of Life Index Purchasing Power Index Safety Index Health Care Index Cost of Living Index Traffic Commute Time Index Pollution Index Climate Index
Intrusion Detection
AI-Powered Network Intrusion Detection System An intelligent, real-time network intrusion detection system built with deep learning. It uses a CNN-LSTM hybrid neural network trained on the NSL-KDD benchmark dataset to automatically classify network traffic into five categories: Normal, DoS (Denial of Service), Probe, R2L (Remote-to-Local), and U2R (User-to-Root) attacks. The system features a sleek, modern web interface with a glassmorphic design, secure user authentication, and an interactive dashboard where users can input network traffic features and receive instant predictions with full confidence breakdowns across all threat classes. Key Highlights: - Deep Learning Powered: Multi-layer 1D Convolutional Neural Network combined with LSTM for temporal pattern recognition in network traffic, achieving 99% training accuracy. - Real-Time Threat Detection: Analyze network packets on the fly and receive predictions in under 100ms, enabling rapid threat identification and response. - 5-Class Attack Classification: Classifies traffic into DoS, Probe, R2L, U2R, or Normal — covering the most critical categories of network intrusions. - Secure Access: User registration and login with bcrypt-hashed passwords and session-based authentication to protect the dashboard and prediction endpoints. - Probability Breakdown: Every prediction comes with a full confidence distribution across all five threat classes, giving complete transparency into the model's decision. - Modern Web Dashboard: Beautiful, responsive UI with animated glassmorphism effects, interactive charts, and a clean user experience built with vanilla HTML, CSS, and JavaScript. - REST API: Programmatic access to the prediction engine via a JSON-based API endpoint for integration with external tools and automation pipelines. Technology Stack: - Backend: Python, Flask, TensorFlow/Keras - Frontend: HTML5, CSS3 (Glassmorphism), JavaScript - Machine Learning: CNN-LSTM Hybrid Model, Scikit-learn (preprocessing) - Database: SQLite3 (user authentication) - Dataset: NSL-KDD (Canadian Institute for Cybersecurity) - Security: bcrypt password hashing, session-based auth Built for cybersecurity researchers, network administrators, and anyone looking to leverage AI for proactive network defense.
Abusive Content Detection
Project Name: ToxicShield Description: A full-stack social media application containing a modern React frontend and a FastAPI backend with built-in AI toxicity moderation to ensure a safe, positive community environment. Key Features: - User Authentication: Secure sign-up and login interactions using JSON Web Tokens (JWT). - Social Feed: Users can create posts, upload image media, interact with the feed, and comment on others' posts. - Real-time AI Toxicity Moderation: All user-generated text is routed and analyzed in real-time by an advanced Deep Learning AI model (Detoxify/PyTorch) to detect toxic, obscene, threatening, or insulting language. - Automated Strike System: A zero-tolerance 3-strike moderation system automatically tracks and penalizes users who repeatedly violate community guidelines. - Admin Analytics Dashboard: View platform health, total warnings, and actively track the most toxic users. How the AI Moderation Works (Examples): 1. Innocent Content (Toxicity Score < 0.50) - Input Example: "I just had a wonderful day at the park!" - Result: The content is successfully published to the social feed. No strikes are added to the user's account. 2. Toxic Content Warning (Toxicity Score > 0.50) - Input Example: "Your opinion is ridiculous and you are dumb." - Result: The content is blocked from being published. The frontend displays a warning explicitly indicating a community guideline violation, and 1 strike point is added to their profile. 3. Severe Toxicity (Toxicity Score > 0.80) - Input Example: "You are an absolute piece of trash." - Result: The content is immediately blocked. The AI flags this as severe toxicity, and 3 strike points are instantly applied to the user's account. 4. Automated Account Suspension (Threshold >= 3 Strikes) - Condition: A user accumulates 3 or more strike points (either by 3 separate minor warnings or 1 severe toxicity violation). - Result: The user's account is automatically suspended for 7 days. They are forcefully logged out and blocked from logging back into the platform until the ban timeframe expires.
Ambulance Booking (Patient, Driver & Admin Dashboard)
A full-stack, real-time online ambulance booking and dispatch system. It connects patients with the nearest available ambulances within a 50km radius using GPS tracking. Technologies Used : Frontend : React Backend : Flask (Python) DB : SQLite3 Location Service : OpenStreetMap Features : Features include real-time location tracking on interactive maps, a 4-digit OTP verification system for secure patient pickups, driver dispatch notifications, and an automated fare/distance calculation upon trip completion. Important : The system supports three user roles: Patients (book rides), Drivers (accept/manage trips via a driver portal), and Hospital Admins (manage fleet and hospital data).
Marine Life & Waste Detection
An AI-powered underwater detection system for identifying marine species and underwater waste using computer vision and deep learning. Features : - Underwater Waste Detection - Detect 14+ types of underwater pollution (bottles, bags, nets, etc.) - Marine Species Detection - Identify sharks, turtles, rays, fish, and invertebrates - Video Detection - Process uploaded videos for detection - Live Analysis - Real-time detection from YouTube live streams and videos
Healthcare Patient Flow & Waiting Room Analytics
Healthcare Patient Flow & Waiting Room Analytics : PulseFlow Auditor is an advanced healthcare operational intelligence system that analyzes 171,064 (dataset cce licensed / kaggle) patient journeys to optimize hospital efficiency, predict capacity constraints, and quantify financial impact in Indian healthcare context. The system uses data-driven statistical analysis instead of arbitrary benchmarks to provide actionable insights for healthcare administrators. 🔧 Technical Stack : Backend: Python (Pandas, NumPy, SQLite3, PyWebView) Frontend: React 18 (Recharts, TailwindCSS, Vite) Database: SQLite with 171,064 patient journeys Architecture: Real-time Python-JavaScript bridge ⚡ Key Features : Advanced Analytics 9 Comprehensive KPIs: Operational, financial, and patient experience metrics Predictive Modeling: 24-hour volume forecasting with confidence intervals Real-Time Monitoring: Live alerts and capacity tracking Statistical Engine: Percentile-based thresholds (no arbitrary values)
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