The number of accidental fires in buildings has been significantly increased in recent years in Saudi Arabia. Fire Safety Equipment (FSE) plays a crucial role in reducing fire risks. However, this equipment is prone to defects and requires periodic checks and maintenance. Fire safety inspectors are responsible for visual inspection of safety equipment and reporting defects. As the traditional approach of manually checking each piece of equipment can be time-consuming and inaccurate, this study aims to improve the inspection processes of safety equipment. Using computer vision and deep learning techniques, a detection model was trained to visually inspect fire extinguishers and identify defects. Fire extinguisher images were collected, annotated, and augmented to create a dataset of 7,633 images with 16,092 labeled instances. Then, experiments were carried out using YOLOv5, YOLOv7, YOLOv8, and RT-DETR. Pre-trained models were used for transfer learning. A comparative analysis was performed to evaluate these models in terms of accuracy, speed, and model size. The results of YOLOv5n, YOLOv7, YOLOv8n, YOLOv8m, and RT-DETR indicated satisfactory accuracy, ranging between 83.1% and 87.2%. YOLOv8n was chosen as the most suitable due to its fastest inference time of 2.7 ms, its highest mAP0.5 of 87.2%, and its compact model size, making it ideal for real-time mobile applications.

Sign language (SL) is a means of communication that is used to bridge the gap between the deaf, hearing-impaired, and others. For Arabic speakers who are hard of hearing or deaf, Arabic Sign Language (ArSL) is a form of nonverbal communication. The development of effective Arabic sign language recognition (ArSLR) tools helps facilitate this communication, especially for people who are not familiar with ArSLR. Although researchers have investigated various machine learning (ML) and deep learning (DL) methods and techniques that affect the performance of ArSLR systems, a systematic review of these methods is lacking. The objectives of this study are to present a comprehensive overview of research on ArSL recognition and present insights from previous research papers. In this study, a systematic literature review of ArSLR based on ML/DL methods and techniques published between 2014 and 2023 is conducted. Three online databases are used: Web of Science (WoS), IEEE Xplore, and Scopus. Each study has undergone the proper screening processes, which include inclusion and exclusion criteria. Throughout this systematic review, PRISMA guidelines have been appropriately followed and applied. The results of this screening are divided into two parts: analysis of all the datasets utilized in the reviewed papers, underscoring their characteristics and importance, and discussion of the ML/DL techniques’ potential and limitations. From the 56 articles included in this study, it was noticed that most of the research papers focus on fingerspelling and isolated word recognition rather than continuous sentence recognition, and the vast majority of them are vision-based approaches. The challenges remaining in the field and future research directions in this area of study are also discussed.

This study investigated the accessibility problems and barriers faced by visually impaired Arabic speakers when using banking mobile applications. The sample included mobile applications for the top three banks in Saudi Arabia in terms of their customer base, including applications for AlRajhi, SNB, and Riyad bank. The focus was on the three most popular e-services provided by those applications. Using a mixed method approach, accessibility was inspected manually according to current Web Content Accessibility Guidelines (WCAG 2.1). In addition, in-depth usability testing for those applications was conducted with 12 blind Arabic-speaking users. The results revealed that the accessibility level for all sample applications did not conform to WCAG 2.1, which reflected the low accessibility level provided by these applications. However, AlRajhi’s application was the best among the tested applications. In manual inspection, it had the lowest number of violations of success criteria compared to SNB’s and Riyad’s applications. It also achieved the best results in usability testing in terms of effectiveness, efficiency and satisfaction compared to the other two applications. The overall results showed that the current situation of accessibility of Saudi banking applications is not very satisfactory in terms of number of violations to WCAG 2.1 success criteria. The most violated principle by all the applications was operable, with around 37% of total violations, followed by perceivable, then robust and understandable.

Learning to write the Arabic alphabet is crucial for Arab children’s cognitive development, enhancing their memory and retention skills. However, the lack of Arabic language educational applications may hamper the effectiveness of their learning experience. To bridge this gap, SamAbjd was developed, an interactive web application that leverages deep learning techniques, including air-writing recognition, to teach Arabic letters. SamAbjd was tailored to user needs through extensive surveys conducted with mothers and teachers, and a comprehensive literature review was performed to identify effective teaching methods and models. The development process involved gathering data from three publicly available datasets, culminating in a collection of 31,349 annotated images of handwritten Arabic letters. To enhance the dataset’s quality, data preprocessing techniques were applied, such as image denoising, grayscale conversion, and data augmentation. Two models were experimented with using a convolution neural network (CNN) and Visual Geometry Group (VGG16) to evaluate their effectiveness in recognizing air-written Arabic characters. Among the CNN models tested, the standout performer was a seven-layer model without dropout, which achieved a high testing accuracy of 96.40%. This model also demonstrated impressive precision and F1-score, both around 96.44% and 96.43%, respectively, indicating successful fitting without overfitting. The web application, built using Flask and PyCharm, offers a robust and user-friendly interface. By incorporating deep learning techniques and user feedback, the web application meets educational needs effectively.

Blended learning, therefore, involves the use of conventional face-to-face teaching and the application of information technologies that support learning in the classroom. This research investigates the effect of implementing blended learning in the Saudi Arabian context on fourth-grade students’ reading and writing in Arabic and English regarding the mediating effects of student engagement and academic attitudes. A cross-sectional descriptive survey design distributed a structured questionnaire among 200 teachers and educational experts. The analysis of Structural Equation Modeling (SEM) suggests that blended learning enhances language skills; student engagement and academic attitudes act as mediating variables. There is evidence that well-planned and executed blended learning strategies can promote improvement in language learning and motivation, providing valuable recommendations for practice and policy about the education of bilingual students.