All Projects
Browse our complete collection of high-quality source codes.
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. š§ 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 ā” 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)
Payment Fraud Detection (UPI & Cards)
Overview: UPI & Cards Payment Fraud Detection System This project is a standalone, AI-powered fraud detection system specifically designed for UPI (Unified Payments Interface) and card-based payment transactions. It leverages high-performance machine learning and a rule-based validation engine to identify potential fraudulent activity in real-time. Key Features : Real-Time Standalone Inference: Runs entirely within the browser using Pyodide (WebAssembly). This eliminates the need for a backend API for predictions, ensuring low latency and maximum privacy. Dual-Model Ensemble: Uses a weighted ensemble of XGBoost and LightGBM models, achieving a high degree of accuracy by combining the strengths of different gradient boosting architectures. UPI Validation Engine: Includes a specialized rule engine to catch structurally invalid Virtual Payment Addresses (VPAs) and Unique Transaction Reference (UTR) numbers specific to apps like GPay, PhonePe, and Paytm. Privacy-First History: Automatically saves transaction history to a local SQLite3 database (sql.js) stored within the browser's IndexedDB. No sensitive transaction data ever leaves your device. Advanced Feature Engineering: Automatically transforms raw transaction data into 447 engineered features, capturing complex temporal and statistical patterns found in real-world fraud. Comprehensive Metrics: Provides instant feedback with fraud probability scores (0ā100%), risk levels (Low, Medium, High), and detailed performance insights. Technical Specifications: Model Performance: 98.45% Accuracy and 0.965 ROC-AUC, trained on over 590,000 real-world transactions (IEEE-CIS dataset). Frontend Stack: Built with React.js, Vite, and Tailwind CSS for a modern, responsive, and "Cyber-Fintech" user experience. Machine Learning: XGBoost (Binary Logistic) + LightGBM (GBDT) with weighted ensemble logic. Deployment: Fully client-side; can be served as a static site while maintaining full AI capabilities.
Parking Booking and Real Time Analysis
The frontend of the Smart Parking System is a responsive web application built with React and Vite. It provides an intuitive interface for users to interact with the parking predictor system. Key features include: - Real-Time Dashboard: Monitor live parking lot occupancy and slot statuses. - Interactive Map: Navigate to available parking slots using an integrated Leaflet map. - Slot Booking & Dynamic Pricing: Reserve parking slots with real-time price estimation based on current demand and time. - Occupancy Prediction Charts: View LSTM-based predictions for future parking occupancy trends using Chart.js.
PG & Mess Management - Client+Admin (MERN Stack)
PG Life is a comprehensive, MERN-stack based management system that streamlines Paying Guest House and Restaurant operations. It empowers administrators to efficiently manage rooms, mess plans, bookings, and support tickets, while providing a dedicated portal for students and users to request accommodations, report issues, and stay informed with announcements. Admin Features: - Room Management: Create, view, and delete room listings with details like size, price, bathroom status, and owner info. - Mess Management: Manage mess plans and daily menus. - Booking Control: Approve or cancel room and mess bookings. - Support Tickets: Respond to and close support tickets raised by residents. - Announcements: Post system-wide news and updates for all users. - Dashboard Overview: View key statistics like total users, estimated revenue, and open tickets. Client (Student/User) Features: - Room Browsing: Browse available rooms with filtering and search options. - Room Booking: Request room accommodations based on availability. - Mess Booking: Subscribe to mess plans for daily meals. - Support Tickets: Raise tickets for maintenance or any issues. - Announcements: Stay updated with the latest PG news and updates. - Reviews: Submit reviews and ratings for rooms and services.
Fake News Detection (Video, Audio, Text & Image)
FakeDetect is a cutting-edge, multimodal deepfake and fake news detection system designed for enterprise security and media verification. It uses state-of-the-art AI models to analyze Audio, Images, and Videos for signs of manipulation, synthesis, and deepfakes. Features: - Audio Detection: Analyzes wave patterns and voice anomalies to identify synthetic or cloned audio using a Wav2Vec2 sequence classifier. - Image Analysis: Detects AI-generated images, face swaps, and pixel-level manipulation using an EfficientNet V2 model. - Video Verification: Analyzes videos frame-by-frame and temporally using an Xception-based architecture. * Dynamic Heatmaps: Automatically generates temporal probability heatmaps showing the fake probability segment-by-segment. * Frame Extraction: Displays exactly which frames were analyzed by the engine for complete transparency. Tech Stack: - Backend: Python, FastAPI, TensorFlow/Keras (tf-keras), PyTorch, OpenCV, Librosa - Frontend: HTML5, Vanilla JS, CSS (Responsive, Modern UI) - Data Visualization: Matplotlib, Seaborn (for dynamic heatmaps) Overall Accuracy : 92%
Multi Model Cancer Detection
The Multimodal Cancer Diagnosis System is an advanced Neuro-Symbolic AI application designed to assist in the early detection and management of cancer by fusing data from three distinct sources: medical imaging (X-rays/CT scans), clinical notes (text), and patient vitals (structured data). Unlike traditional black-box AI models, this system combines deep learning (ResNet-18 for images, DistilBERT for text) with a deterministic rule-based engine, ensuring that critical medical rules (e.g., age risk factors, specific keywords) directly influence the final risk score for greater reliability and interpretability. The application features a modern, responsive interface with role-based access for both Patients and Doctors. Patients can upload reports for instant analysis, view a timeline of their medical history, receive personalized actionable recommendations (e.g., "Schedule Biopsy"), and interact with a Context-Aware Chatbot powered by Google Gemini that answers health queries using their specific medical records. Doctors have a dedicated dashboard to monitor patient risk scores and issue digital prescriptions, creating a comprehensive ecosystem for cancer care management. Accuracy : 90%+
Android Malware Detection
Android Malware Detection - A tool for quantitative risk analysis of Android applications based on machine learning techniques. Android Malware Detection is a tool for quantitative risk analysis of Android applications written in Java, which is used to check the permissions of the apps, and Python, which is used to compute a risk value based on apps' permissions. The tool uses classification techniques through scikit-learn, a machine learning library for Python, in order to generate a numeric risk value between 0 and 100 for a given app. In particular, the following classifiers of scikit-learn are used in Android Malware Detection (this list is chosen after extensive empirical assessments): * Support Vector Machines (SVM) * Multinomial Naive Bayes (MNB) * Gradient Boosting (GB) * Logistic Regression (LR) Unlike other tools, Android Malware Detection does not take into consideration only the permissions declared in the app manifest, but carries out reverse engineering on the apps to retrieve the bytecode and then infers through static analysis which permissions are actually used and which are not. In this way, it extracts four sets of permissions for every analyzed app: * Declared permissions: extracted from the app manifest * Exploited permissions: declared and actually used in the bytecode * Ghost permissions: not declared but with usages in the bytecode * Useless permissions: declared but never used in the bytecode
Disease Detection Using ML (Breast, Diabetes, Heart)
A machine learning-based application for detecting multiple diseases using clinical parameters. The system provides a unified interface to predict the likelihood of various health conditions. The app is working correctly with the three disease prediction models : ā Breast Cancer (92.98% accuracy) ā Diabetes (75.32% accuracy) ā Heart Disease (80.33% accuracy)
Use Gesture to Solve Maths
Hand Gesture Math Solver is an AI-powered computer vision project that allows users to solve mathematical expressions by drawing them in the air using hand gestures. The system captures real-time video from a webcam, tracks hand movements, and converts gestures into written mathematical expressions on a virtual canvas. Once the user submits the expression using a specific gesture, the drawing is sent to Google Gemini AI, which interprets and solves the math problem. The result is then displayed back in the application interface. This project demonstrates the powerful combination of Computer Vision, Gesture Recognition, and Generative AI, making it an innovative and interactive way to perform mathematical problem-solving.
Deepfake Detection (Only Video)
About the Project - The Deepfake Detection System is an AI-driven platform developed to identify manipulated digital media, focusing on videos. It integrates deep learning model to analyze visual artifacts and motion inconsistencies. The system is deployed through a Flask-based web application, providing users with authenticity scores, visual explanations, and secure access features. Key Points : - Detects fake videos and images using deep learning - Combines temporal and frame-level analysis for video deepfake detection - Uses ResNeXt50 + LSTM for motion inconsistency analysis in videos - Applies Xception network for frame-level artifact detection - Uses EfficientNet-B0 for image authenticity classification - Built with Flask backend and HTML/CSS/JavaScript frontend - Powered by PyTorch and TensorFlow/Keras frameworks - Provides fake probability scores and heatmaps for explainability - Includes a secure user authentication system Accuracy : ~96%
Deepfake Detection (Video, Image & Audio)
About This Project - The Deepfake Detection System is an AI-driven platform designed to identify manipulated digital media, including videos, images, and audio. It integrates multiple deep learning models to analyze visual artifacts, motion inconsistencies, and synthetic voice patterns. The system is deployed through a Flask-based web application that provides users with authenticity scores, visual explanations, and secure access features. Key Points : - Detects fake videos, images, and audio using advanced deep learning - Combines temporal and frame-level analysis for video deepfake detection - Uses ResNeXt50 + LSTM for motion inconsistency analysis in videos - Applies Xception Network for frame-level visual artifact detection - Uses EfficientNet-B0 for image authenticity classification - Integrates audio deepfake detection using spectrogram-based CNN models to analyze synthetic voice patterns - Built with a Flask backend and HTML/CSS/JavaScript frontend - Powered by PyTorch and TensorFlow/Keras frameworks - Provides fake probability scores, confidence graphs, and heatmaps for explainability - Includes a secure user authentication system Accuracy : ~96% overall detection accuracy across video, image, and audio deepfake datasets.
Skin Cancer Detection
Overview Skin Cancer Detection is an AI-powered medical imaging project designed to identify potential skin cancer from images of skin lesions. The system analyzes visual patterns in moles or abnormal skin growths and classifies them as benign (non-cancerous) or malignant (cancerous) using deep learning. This technology assists in early detection, which is critical because skin cancer especially melanoma can spread rapidly if not treated in time.
Deepfake Detection (Video & Image)
The Deepfake Detection System is an AI-driven platform developed to identify manipulated digital media, focusing on videos and images. It integrates multiple deep learning models to analyze visual artifacts and motion inconsistencies. The system is deployed through a Flask-based web application, providing users with authenticity scores, visual explanations, and secure access features. Key Points : - Detects fake videos and images using deep learning - Combines temporal and frame-level analysis for video deepfake detection - Uses ResNeXt50 + LSTM for motion inconsistency analysis in videos - Applies Xception network for frame-level artifact detection - Uses EfficientNet-B0 for image authenticity classification - Built with Flask backend and HTML/CSS/JavaScript frontend - Powered by PyTorch and TensorFlow/Keras frameworks - Provides fake probability scores and heatmaps for explainability - Includes a secure user authentication system Accuracy : ~96%