No abstract

Due to a shortage of fuel sources and the increment in environmental pollution, efficient techniques should be introduced. The best solution is to move to the use of electric vehicles. The article aims to develop a solution for electric vehicle (EV) charging station locations that utilize the internet of things (IoT) technology. The IoT is a paradigm that uses sensors and transmitting networks to provide current facilities with a real-time global communication perspective of the physical world. This paper proposes a real-time system to provide a real-time update to EV location and charging stations (CSs) location to reduce time lost by users searching CSs, and provides real-time charging station (CS) recommendations for EV users by displaying the nearest CS, provide estimation arrival time to the nearest CS, display distance between nearest CS and EV real-time updated. The work of the proposed system was tested, and the most significant error rate (17 meters) is represented by the difference in the distance obtained from the system and the distance obtained from Google Map. The total accuracy of the design for the tested case is (98.014%).

No abstract

Osteoarthritis (OA) is a disease of human joints, especially the knee joint, due to significant weight of the body. This disease leads to rupture and degeneration of parts of the cartilage in the knee joint, which causes severe pain. Diagnosis of this disease can be obtained through X-ray. Deep learning has become a popular solution to medical issues due to its fast progress in recent years. This research aims to design and build a classification system to minimize the burden on doctors and help radiologists to assess the severity of the pain, enable them to make an optimal diagnosis and describe the correct treatment. Deep learning-based approaches, such as Convolution Neural Networks (CNNs), have been used to detect knee OA using transfer learning with fine-tuning. This paper proposed three versions of pre-trained networks (VGG16, VGG19, and ResNet50) for handling the classification task. According to the classification results, The proposed model ResNet50 outperformed the other models a validation accuracy of 91.51% has been obtained.

No abstract