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alzheimer-s-disease-classification

This comprehensive web application is designed to accurately classify Alzheimer's disease using a powerful Convolutional Neural Network (CNN) model. The project showcases a seamless integration of frontend and backend technologies, utilizing Streamlit for the user interface and a sophisticated CNN for disease classification.

cansat-ground-station

celebrity-classification

This project introduces a system that utilizes Convolutional Neural Network (CNN) architecture to classify given images into categories representing different celebrities present in the dataset. By employing deep learning techniques, the system accurately identifies and labels images based on the recognizable features of the celebrities.

face-recognition-and-matching

This project introduces a solution that enhances exam security by verifying identities and detecting unusual behavior. By using technologies like DeepFace and Haar Cascade Classifier, the system captures facial images at random intervals, confirms identities, and watches for any irregular actions.

image-matching-for-text-extraction

This project introduces a solution that simplifies the process of extracting and evaluating answers from question paper images. By using image matching techniques, the system intelligently compares a blank question paper image with one containing answers, effectively extracting responses and using Optical Character Recognition (OCR) for evaluation.

language-specific-text-extraction

This project presents a sophisticated solution for efficiently extracting text from multilingual images and PDFs while focusing on a designated language. By leveraging the capabilities of Tesseract-OCR, the project streamlines the process of accurately identifying and extracting text in a language of your choice.

major-project

real-time-hand-sign-recognition

This project combines two powerful technologies – MediaPipe and Neural Networks – to recognize hand signs and gestures in real time. The aim is to enable computers to accurately interpret hand movements, opening doors for various applications across different domains.

sentiment-analysis

This project focuses on the accurate classification of text sentiments into four distinct categories: happy, sad, angry, and other. Utilizing machine learning techniques, it can effectively identify and label the emotional tone conveyed by written text.