This is a supervised machine-learning project — the models learn from human-labelled datasets where each sample is tagged as either Accident or Non-Accident.
A machine learning-based project aimed at detecting and identifying plant diseases from images of affected plants. Plant Disease Detection Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
This project aims to design and develop a computer vision-based system that detects whether individuals are wearing face masks in images. Images Face Mask Detection System Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
This project uses transfer learning with EfficientNetB0 to classify images into "Smoke" and "Fire" categories, achieving ~76% accuracy. The model leverages fine-tuning, feature extraction, and data augmentation, with high precision for "Fire" and perfect recall for "Smoke."
This project builds a Convolutional Neural Network (CNN) to classify landmarks from images. It includes developing a CNN from scratch and enhancing performance with transfer learning (achieving 75% accuracy using ResNet18).
A deep learning-based approach for automatic detection of brain tumors from magnetic resonance imaging (MRI) scans. Brain Tumor Detection using Deep Learning Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
The Image Recognition System is a deep learning-based project that enables computers to recognize and classify images into different categories. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
The Malware Detection Using Deep Learning Project aims to develop an efficient and accurate malware detection system using deep learning techniques. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
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