The traditional way of diagnosing malaria takes time, as physicians have to check about 5000 cells to produce the final report. The accuracy of the final report also depends on the physician’s expertise. In the event of a malaria epidemic, a shortage of qualified physicians can become a problem. In the manual method, the parasites are identified by visual identification; this technique can be automated with the use of new algorithms. There are numerous publicly available image datasets containing the intricate structure of parasites, and deep learning algorithms can recognize these complicated patterns in the images. This study aims to identify and localize malaria parasites in the photograph of blood cells using the YOLOv5 model. In this research, a publicly available malaria trophozoite dataset is utilized which contains 1182 data samples. YOLOv5, with the novel technique of weight ensemble and traditional transfer learning, is trained using this dataset, and the results were compared with the other object detection models—for instance, Faster RCNN, SSD net, and the hybrid model. It was observed that YOLOv5 with the ensemble weights yields better results in terms of precision, recall, and mAP values: 0.76, 0.78, and 0.79, respectively. The mAP score closer to 1 signifies a higher confidence in localizing the parasites. This study is the first implementation of ensemble YOLOv5 in the malaria parasite detection field. The proposed ensemble model can detect the presence of malaria parasites and localize them with bounding boxes better than previously used models.

Mobile learning (m-learning) adoption has increased and shall be demonstrated superior performance by implementing related computing paradigms, such as IoT, edge, mobile edge, fog, AI, and 5G. Mobile cloud architectures (MCAs) enable m-learning with several benefits and face limitations while executing real-time applications. This study investigates the state-of-the-art m-learning architectures, determines a layered m-learning-MCA obtaining numerous benefits of related computing paradigms, and expands m-learning functional structure. It evaluates m-learning performance across the four physical layer’s MCAs—distance cloud, cloudlet, operator-centric cloud, ad hoc cloud, and emerging computing architectures. Surprisingly, only distance-cloud MCA is adopted for developing m-learning systems by ignoring the other three. Performance evaluation shows m-learning gets terrific benefits and users QoE in related computing paradigms. Mobile edge computing offers ultralow latency, whereas the current architecture improves task execution time (1.87, 2.01, 2.63, and 3.97) for the resource-intensive application (i.e., 4.2 MB). Fog using AI algorithms is exceptional for more complex learning objects, IoT is superior for intelligent learning tools, and 5G ultrawideband services are more significant for intelligent video analytics. These findings help learners, educators, and institutions adopt an appropriate model for achieving their academic objectives across educational disciplines. The presented approach enables future research to design innovative architectures considering resource-intensive m-learning application execution requirements, such as video content analytics and virtual reality learning models.

Objectives: To provide an efficient and secure method for verifying the authenticity of vehicles by using QR codes and adopting distributing computing in a mobile environment. Methods: The proposed mobile application utilizes the QR code as the unique identifier for vehicle verification. It employs an iterative model to develop the application. The system captures the QR code image using the mobile device’s camera and processes it to extract the vehicle’s relevant information. The extracted data is then validated and verified against the central database to ensure the legitimacy of the vehicle. Further, this approach collected participants’ data, considered the technical parameters, and compared them with gold standards. Findings: The research findings demonstrate that the intelligent QR code-based mobile application is highly effective in identifying and authenticating vehicles. The system achieves high accuracy in decoding QR codes in current practices and successfully matches the extracted information with the database records, ensuring the genuineness of the vehicles. Novelty: This research introduces a novel approach to vehicle identification and authentication by leveraging distributed computing potentials and QR codes in a mobile environment. The integration of advanced image processing and machine learning techniques enhances the accuracy and reliability of the system compared to existing practices. This application provides an efficient platform for verifying the authenticity of vehicles. Keywords: Mobile Application; QR Code; Iterative Model; QR Code; Mobile Application; Vehicle Identification; Authentication

This study identifies the importance and ability of geographic information systems and remote sensing technologies to manage and monitor surface water in the Jazan region of Saudi Arabia. This study uses several methods to establish a geographical database for the study area: hydrologic and water quality data, a digital Geographical Information System (GIS) elevation model, the spatial distribution and investigation of water samples in Jazan and watershed delineation and flowing tracing tools. The proposed spatial data architecture attempts to implement flood prevention tools and practical land-use proposals, as well as the design of a computerised GIS database for flood risk assessment and mapping.

This study aims to summarize the capabilities available in learning techniques that can be used in managing the educational process in all academic disciplines during the Corona pandemic (Covid 19). In this research, the blackboard system and the Moodle system were chosen to highlight the capabilities available in it through three axes: Teaching strategies Used in the administration and delivery of the course, and the evaluation methods that are used to evaluate the performance of students in the course, and the last axis is to ensure the quality of the educational process by following up the educational process from the concerned administration. The study relied on the exploratory descriptive approach to identify the types of teaching strategies used and classify them according to their support for the majors, as well as the methods of evaluation of students ’performance in the course according to the scientific specializations by examining many types of research to sort and classify teaching strategies and evaluation methods for scientific specialties according to the opinions of the studies that have been relied upon. The study revealed that there are about 28 teaching styles that can be used in the four main practical disciplines, which are arts and humanities, education in the medical field, science and engineering, and social sciences. The study also showed the mechanism of implementing these methods through the tools available on the Blackboard Learning Management System and the Moodle Learning Management System.