AI Projects Hub

Hi there, I'm Raafat Nagy, an AI & Computer Vision Engineer passionate about building intelligent systems and applying machine learning and deep learning techniques to solve real-world problems.
This repository serves as a central hub for my projects in Computer Vision, Deep Learning, and Machine Learning.

Table of Contents

• Computer Vision Projects
• Deep Learning Projects
• Machine Learning Projects
• Future Work

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Computer Vision Projects

1. Vehicle Detection, Tracking, Counting and Speed Estimation

   • Developed a real-time traffic monitoring system using YOLO and ByteTrack.
   • Implemented speed estimation with perspective transformation for accurate measurements.
   • Supported configurable counting zones and custom YOLO models.
   • GitHub Repo  -  Demo Video

2. YOLO Object Detection App

   • Built a real-time object detection web app with FastAPI backend and JavaScript frontend.
   • Features drag & drop uploads, multiple model options, smart video streaming, and dark mode UI.
   • Integrated advanced CV models with smooth asynchronous processing.
   • GitHub Repo  -  Demo Video

3. Smart Face Attendance System

   • Created a real-time face recognition attendance system using OpenCV and face_recognition.
   • Automated attendance logging with webcam detection and CSV export.
   • Optional API integration for backend synchronization.
   • GitHub Repo

4. Student Entry and Exit Tracking

   • Developed a system for tracking and counting students entering/exiting halls using YOLO and OpenCV.
   • Used Shapely for zone-based direction detection.
   • Enabled CSV logging and API reporting.
   • GitHub Repo

5. Facial Landmark and Drowsiness Detection

   • Implemented real-time facial landmark detection and drowsiness monitoring using the EAR method.
   • Built with OpenCV and dlib for accurate fatigue alerts.
   • Enhanced driver and workplace safety.
   • GitHub Repo  -  Demo Video

6. Object Detection Telegram Bot

   • Developed asynchronous Telegram bot for real-time object detection on user-submitted images.
   • Automated annotation and detailed detection summaries.
   • Built with OpenCV and python-telegram-bot for efficient performance.
   • GitHub Repo  -  Demo Video

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Deep Learning Projects

All projects are included in Deep Learning Projects repository:

1. Brain Tumor MRI Classification

   • Developed a model using TensorFlow and ResNet50V2 for brain tumor detection from MRI images.
   • Improved performance via data augmentation and transfer learning.
   • Evaluated with confusion matrix and classification reports.

2. Oral Diseases Classification

   • Built a multi-class classification model to identify six oral diseases using TensorFlow and ResNet50V2.
   • Applied preprocessing, augmentation, and fine-tuned pre-trained layers.
   • Assessed accuracy with detailed reports and confusion matrices.

3. Plant Disease Detection

   • Designed a CNN to classify 38 plant disease categories with TensorFlow/Keras.
   • Used batch normalization and dropout for better generalization.
   • Achieved high validation accuracy through image augmentation.

4. MNIST Handwritten Digit Classification

   • Created CNN for 10-class digit classification on grayscale images using TensorFlow.
   • Applied data augmentation, dropout, early stopping, and learning rate scheduling.
   • Validated with accuracy metrics and prediction visualizations.

5. Autoencoder Projects on MNIST

   • Developed convolutional, simple, and denoising autoencoders for image compression and noise removal.
   • Used convolution, max-pooling, and upsampling layers in encoder-decoder architecture.
   • Evaluated reconstruction quality by comparing original and reconstructed images.

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Machine Learning Projects

1. Machine Learning From Scratch

   • Developed fundamental ML algorithms (Linear Regression, Logistic Regression, SVM, Decision Trees, KNN, Clustering, PCA) from scratch in Python.
   • Emphasized mathematical understanding and clean, well-documented code for educational use.
   • GitHub Repo

2. Diabetes Prediction Project

   • Built ML models for diabetes prediction using patient health data with Python and scikit-learn.
   • Conducted data exploration, visualization, feature engineering, and hyperparameter tuning.
   • Deployed an interactive Streamlit app for real-time risk prediction.
   • GitHub Repo  -  Streamlit App

3. Iris Flower Species Prediction

   • Created an SVM model to classify Iris species based on sepal and petal measurements.
   • Performed data preprocessing and exploratory analysis.
   • Developed a Streamlit web app for user-friendly species prediction with dynamic visualization.
   • GitHub Repo  -  Streamlit App

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Future Work

This repository will continue to be updated with new projects and research work in the areas of:

• Machine Learning
• Deep Learning
• Computer Vision
• AI Systems Integration