This paper presents a deep learning-based intelligent web application for the early detection of eye diseases using retinal fundus images. The dataset used in this study consists of 4,216 retinal fundus images collected from Kaggle, representing multiple eye disease categories. Multiple deep learning architectures, including CNN, DenseNet, InceptionV3, and ResNet, were evaluated and compared with a proposed modified MobileNetV2 architecture. The proposed architecture enhances the baseline MobileNetV2 by optimizing feature extraction and classification layers for improved performance in multi-class eye disease detection. Experimental results show that the proposed model achieved an overall cl... More >

Urban parking inefficiency has become a critical challenge in modern cities, leading to increased traffic congestion, higher fuel consumption, and greater environmental impact. This paper proposes an intelligent smart parking management system that integrates hardware sensing, machine learning, and computer vision to enable real-time parking monitoring and automated vehicle identification. The system combines infrared sensors, camera modules, and microcontroller-based control with vision-based parking space detection and automatic license plate recognition (ALPR). Experimental results demonstrate that the parking space detection module achieves an accuracy of 93.97%, while the license plate... More >