Tamilarasan K

Developed an intelligent medical image analysis system to detect Alzheimer’s disease at early stages using deep learning techniques. The project leverages Convolutional Neural Networks (CNNs) to automatically extract high-level features from brain MRI images, enabling accurate classification of different stages of Alzheimer’s disease.

To enhance model performance, feature optimization techniques were applied to reduce redundancy, improve classification accuracy, and minimize overfitting. The system was trained and evaluated on a labeled MRI dataset, achieving improved detection efficiency compared to traditional machine learning methods.

Designed an automated street lighting system using Arduino UNO that adjusts light intensity based on environmental conditions and motion detection, reducing power consumption and improving energy efficiency.

Implemented a real-time object detection system using the YOLO (You Only Look Once) algorithm to identify and classify objects in live video streams. Achieved fast and accurate detection using deep learning and computer vision techniques.