Deaf and hard-of-hearing people mainly communicate using sign language, which is a set of signs made using hand gestures combined with facial expressions to make meaningful and complete sentences. The problem that faces deaf and hard-of-hearing people is the lack of automatic tools that translate sign languages into written or spoken text, which has led to a communication gap between them and their communities. Most state-of-the-art vision-based sign language recognition approaches focus on translating non-Arabic sign languages, with few targeting the Arabic Sign Language (ArSL) and even fewer targeting the Saudi Sign Language (SSL). This paper proposes a mobile application that helps deaf and hard-of-hearing people in Saudi Arabia to communicate efficiently with their communities. The prototype is an Android-based mobile application that applies deep learning techniques to translate isolated SSL to text and audio and includes unique features that are not available in other related applications targeting ArSL. The proposed approach, when evaluated on a comprehensive dataset, has demonstrated its effectiveness by outperforming several state-of-the-art approaches and producing results that are comparable to these approaches. Moreover, testing the prototype on several deaf and hard-of-hearing users, in addition to hearing users, proved its usefulness. In the future, we aim to improve the accuracy of the model and enrich the application with more features.

Smart cities utilize advanced technologies to enhance quality of life by improving urban services, infrastructure, and environmental sustainability. Effective pothole detection and repair strategies are essential for improving quality of life as they directly impact the comfort and safety of road users. In addition to causing serious harm to residents’ lives, potholes can also cause costly vehicle damage. In this study, a pothole detection system utilizing unmanned aerial vehicles, called PDS-UAV, is developed. The system aids in automatically detecting potholes using deep learning techniques and managing their status and repairs. In addition, it allows road users to view an overlay of the detected potholes on the maps based on their selected route, enabling them to avoid the potholes and increase their safety on the roads. Two data collection methods were used, an interview and a questionnaire, to gather data from the target system users. Based on the data analysis, the system’s requirements, design, and implementation were completed. For the pothole detection, a deep learning model using YOLOv8 was developed, which achieved an overall performance of 95%, 98%, and 92% for F1 score, precision, and recall, respectively. Different types of testing has been performed on the target users to ensure the system’s validity, effectiveness, and ease of use, including unit testing, integration testing, and usability testing. As a future work, more features will be added to the system in addition to improving the deep learning model accuracy.

The advancement of Artificial Intelligence (AI) and the Internet of Things (IoT) has accelerated the development of Intelligent Transportation Systems (ITS) in smart cities, playing a crucial role in optimizing traffic flow, enhancing road safety, and improving the driving experience. With urban traffic becoming increasingly complex, timely detection and response to congestion and accidents are critical to ensuring safety and situational awareness. This paper presents Passable, an intelligent and adaptive traffic light control system that monitors traffic conditions in real time using deep learning and computer vision. By analyzing images captured from cameras at traffic lights, Passable detects road incidents and dynamically adjusts signal timings based on current vehicle density. It also employs wireless communication to alert drivers and update a centralized dashboard accessible to traffic management authorities. A working prototype integrating both hardware and software components was developed and evaluated. Results demonstrate the feasibility and effectiveness of designing an adaptive traffic signal control system that integrates incident detection, instantaneous communication, and immediate reporting to the relevant authorities. Such a design can enhance traffic efficiency and contribute to road safety. Future work will involve testing the system with real-world vehicular communication technologies on multiple coordinated intersections while integrating pedestrian and emergency vehicle detection.

High-performance computing (HPC) and its supercomputers are essential for solving the most difficult issues in many scientific computing domains. The proliferation of computational resources utilized by HPC systems has resulted in an increase in the associated error rates. As such, modern HPC systems promote a hybrid programming style that integrates the message-passing interface (MPI) and open multi-processing (OpenMP). However, this integration often leads to complex defects, such as deadlocks and race conditions, that are challenging to detect and resolve. This paper presents a novel approach: using machine learning algorithms to predict defects in C++-based systems by employing hybrid MPI and OpenMP models. We focus on employing a balanced dataset to enhance prediction accuracy and reliability. Our study highlights the effectiveness of the support vector machine (SVM) classifier, enhanced with term frequency (TF) and recursive feature elimination (RFE) techniques, which demonstrates superior accuracy and performance in defect prediction when compared to other classifiers. This research contributes significantly to the field by providing a robust method for early defect detection in hybrid programming environments, thereby reducing development time, costs and improving the overall reliability of HPC systems.

Text localization and recognition from natural scene images has gained a lot of attention recently due to its crucial role in various applications, such as autonomous driving and intelligent navigation. However, two significant gaps exist in this area: (1) prior research has primarily focused on recognizing English text, whereas Arabic text has been underrepresented, and (2) most prior research has adopted separate approaches for scene text localization and recognition, as opposed to one integrated framework. To address these gaps, we propose a novel bilingual end-to-end approach that localizes and recognizes both Arabic and English text within a single natural scene image. Specifically, our approach utilizes pre-trained CNN models (ResNet and EfficientNetV2) with kernel representation for localization text and RNN models (LSTM and BiLSTM) with an attention mechanism for text recognition. In addition, the AraElectra Arabic language model was incorporated to enhance Arabic text recognition. Experimental results on the EvArest, ICDAR2017, and ICDAR2019 datasets demonstrated that our model not only achieves superior performance in recognizing horizontally oriented text but also in recognizing multi-oriented and curved Arabic and English text in natural scene images.

This study explores the optimization of Automated Question Generation (AQG) for educational assessments using Large Language Models (LLMs) and ontologies. Three approaches are evaluated: template-based structured ontology question generation, LLM-based structured ontology question generation, and LLM-based flat concept list question generation, using BERT Precision, Recall, F1-score, and Semantic Similarity as performance metrics. The results show that: i) the template-based structured ontology approach achieved a BERT Precision of 0.833, Recall of 0.844, and F1-score of 0.838, with a Semantic Similarity of 0.563, ii) the LLM-based structured ontology method showed improvements with a BERT Precision of 0.856, Recall of 0.863, and F1-score of 0.859, but a lower Semantic Similarity of 0.534, and iii) the LLM-based flat concept list approach provided the best results, achieving BERT Precision, Recall, and F1-score of 0.859, along with the highest Semantic Similarity of 0.567. Despite the higher semantic similarity of the LLM-based flat concept list, qualitative analysis revealed that the unstructured ontology sometimes produced hallucinated or unrelated questions. These findings suggest that LLM-based methods provide a balance of relevance and diversity in question generation, with LLM-based flat concept list offering the most optimal results for question generation, while LLM-based structured ontology strikes a balance between Precision and Recall.