Theft Detection & Live Location Surveillance AI is a smart security solution designed to prevent theft and monitor assets in real time using artificial intelligence and IoT technologies. The system integrates computer vision models (such as YOLO or OpenCV-based motion detection) to analyze live video feeds for suspicious or unauthorized activities. Upon detecting potential theft or forced access, the system instantly sends smart alerts to the user via app notifications or SMS. In parallel, the project employs GPS-based live tracking using microcontrollers like Raspberry Pi or Arduino with GPS modules to monitor the exact location of the object or person in motion. The combination of AI-powered detection and real-time location surveillance ensures that stolen items or intruders can be identified and tracked instantly. All events are time-stamped and logged, enabling forensic tracking, auditing, or legal evidence. This project is ideal for securing homes, vehicles, delivery systems, and remote infrastructure. The solution is scalable, cost-effective, and easy to deploy with minimal hardware and cloud integration.

AGRO-SCAN is a smart agriculture solution designed to empower farmers through the use of AI, machine learning, and computer vision. Our project aims to address critical challenges in agriculture such as early crop disease detection, soil condition analysis, and timely advisory for improved crop yield. The system allows users (farmers or agri-experts) to upload images of their plants or soil. Using a pre-trained deep learning model, AGRO-SCAN identifies plant diseases or classifies soil health based on texture and visual indicators. Upon detection, the platform provides tailored recommendations, including treatment steps, preventive actions, and optimal farming practices. Key Features: 🌿 Plant Disease Detection via Image Upload 🌱 Soil Health Classification using Computer Vision 📊 Real-time Analytics Dashboard 📍 Multilingual Support for Local Farmers 📱 Mobile-Friendly Interface for Field Use With AGRO-SCAN, we aim to reduce crop loss, support sustainable farming, and empower farmers with AI-driven insights—all in an easy-to-use digital platform.

This Flutter-based project is an AI-powered Voice Emotion Detection system that classifies human emotions from voice inputs using deep learning. Users can upload or record audio through an integrated Gradio web interface. The app extracts Mel-Frequency Cepstral Coefficients (MFCCs) using the librosa library to capture key features of the voice signal. These features are processed by a pre-trained Multilayer Perceptron (MLP) model built with TensorFlow/Keras, which identifies emotions like happy, sad, angry, and neutral. The predicted emotion is displayed in real-time within the Flutter UI. This app can be used in virtual assistants, customer support, mental health tracking, and interactive systems.

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