Mudu Ganesh's Portfolio

Hi, I'm Mudu Ganesh

A dedicated B.Tech student with a strong passion for artificial intelligence, machine learning, and full-stack development. My expertise lies in developing intelligent systems that address real-world challenges, ranging from drowsiness detection and emotion recognition to sign language interpretation and nationality classification. With a deep understanding of computer vision, natural language processing, and UI/UX design, I strive to create solutions that are both innovative and user-friendly.

A Bit About Me

I’m Ganesh, a 3rd-year Data Science student at VNR VJIET with hands-on experience in projects like drowsiness detection, sign language and emotion recognition, SAR image change detection, and an autonomous bus system. I’ve also built a chatbot-based ticketing system and a farmer-focused market access app. As the Design Head of VJ Data Qusters, I lead creative efforts to grow our data science community, while actively contributing to open-source and taking on leadership roles across campus.

Emotion Detection

Developed a real-time emotion recognition system using a CNN model with TensorFlow/Keras and OpenCV. The model classifies emotions like Happy, Sad, Angry, and Neutral with high accuracy, optimized through data augmentation and hyperparameter tuning. Demonstrates strong skills in deep learning and real-time AI integration.

Drowsiness Detection

Developed a real-time system using OpenCV, dlib, and deep learning to monitor driver fatigue via Eye Aspect Ratio (EAR) analysis. The system detects drowsiness under varying lighting conditions and triggers alerts to enhance road safety, showcasing skills in computer vision and real-time .

Dashboard Data-Analysis-Automation

This project successfully automates the data pipeline for Premier League statistics, integrating web scraping, database storage, and Power BI reporting. The system enhances efficiency, providing up-to-date insights for analysis and decision-making. Additional.

Nationality Detecation

Nationality Detection using Deep Learning – Developed a deep learning-based model to classify a person's nationality based on facial features. Utilized Keras and TensorFlow for training a Convolutional Neural Network (CNN) on diverse datasets. Integrated OpenCV for real-time face detection and preprocessing. Optimized model accuracy through hyperparameter tuning and data augmentation. This project showcases expertise in computer vision, deep learning, and real-time classification tasks, with potential applications in security, border control, and personalized services.