This repository contains Jupyter Notebooks for image classification tasks using Convolutional Neural Networks (CNNs). The included notebooks demonstrate techniques for classifying images from the CIFAR-10 dataset, handwritten digits using the MNIST dataset, and general CNN architectures.

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."

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

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).

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