Search Results (12)

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation of PV systems. Traditional optimization methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces the Material Generation Algorithm (MGA), inspired by the principles of material chemistry, to estimate PV parameters effectively. The MGA simulates the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. The MGA is applied to estimate parameters for two different PV modules, RTC France and Kyocera KC200GT, considering their manufacturing technologies and solar cell models. The significant nature of the MGA in comparison to other algorithms is further demonstrated by experimental and statistical findings. A comparative analysis of the results indicates that the MGA outperforms the other optimization strategies that previous researchers have examined for parameter estimation of solar PV systems in terms of both effectiveness and robustness. Moreover, simulation results demonstrate that MGA enhances the electrical properties of PV systems by accurately identifying PV parameters under varying operating conditions of temperature and irradiance. In comparison to other reported methods, considering the Kyocera KC200GT module, the MGA consistently performs better in decreasing RMSE across a variety of weather situations; for SD and DD models, the percentage improvements vary from 8.07% to 90.29%. Full article

Healthcare is one of the industries that seeks to deliver medical services to patients on time. One of the issues it currently grapples with is real-time patient data exchange between various healthcare organizations. This challenge was solved by both centralized and decentralized cloud computing architecture solutions. In this paper, we review the current state of these two cloud computing architectures in the health sector with regard to the effect on the efficiency of Health Information Exchange (HIE) systems. Our study seeks to determine the relevance of these cloud computing approaches in assisting healthcare facilities in the decision-making process to adopt HIE systems. This paper considers the system performance, patient data privacy, and cost and identifies research directions in each of the architectures. This study shows that there are some benefits in both cloud architectures, but there are also some drawbacks. The prominent characteristic of centralized cloud computing is that all data and information are stored together at one location, known as a single data center. This offers many services, such as integration, effectiveness, simplicity, and rapid information access. However, it entails providing data privacy and confidentiality aspects because it will face the hazard of a single point of failure. On the other hand, decentralized cloud computing is built to safeguard data privacy and security whereby data are distributed to several nodes as a way of forming mini-data centers. This increases the system’s ability to cope with a node failure. Thus, continuity and less latency are achieved. Nevertheless, it poses integration issues because managing data from several sites could be a problem, and the costs of operating several data centers are higher and complex. This paper also pays attention to the differences in aspects like efficiency, capacity, and cost. This paper assists healthcare organizations in determining the most suitable cloud architecture strategy for deploying secure and effective HIE systems. Full article

Mobile health (mHealth) is a branch of electronic health (eHealth) technology that provides healthcare services using smartphones and wearable devices. However, most mHealth applications were developed without applying mHealth specialized usability guidelines. Although many researchers have used various guidelines to design and evaluate mHealth applications, these guidelines have certain limitations. First, some of them are general guidelines. Second, others are specified for mHealth applications; however, they only cover a few features of mHealth applications. Third, some of them did not consider accessibility needs for the elderly and people with special needs. Therefore, this paper proposes a new set of usability guidelines for mHealth applications (UGmHA) based on Quinones et al.’s formal methodology, which consists of seven stages starting from the Exploratory stage and ending with the Refining stage. What distinguishes these proposed guidelines is that they are easy to follow, consider the feature of accessibility for the elderly and people with special needs and cover different features of mHealth applications. In order to validate UGmHA, an experiment was conducted on two applications in Saudi Arabia using UGmHA versus other well-known usability guidelines to discover usability issues. The experimental results show that the UGmHA discovered more usability issues than did the other guidelines. Full article

Achieving the Saudi Kingdom’s vision 2030 in the higher education sector requires higher education institutions to make a significant simultaneous change in their current practices. This encompasses the transitioning of government-funded educational institutions to be financially independent. Therefore, a prompt, agile transition is required while maintaining a positive socioeconomic impact, entrepreneurship and innovation, and high-quality education. This necessitates the transition to lean processes and the review of current practices. One of the most vital processes in educational institutions is student admission/enrollment capacity planning. This study puts forward a capacity planning decision support system (DSS)-based framework for university student enrollment. The framework was applied to the case of KAU, where current practice and challenges are presented, and from which data were collected. A top-down/bottom-up approach was followed and applied using the goal programming technique and a developed mathematical model, respectively. Results show that the proposed framework effectively affects student admission/enrollment capacity planning on strategic and operational levels. Moreover, it can be used in other planning aspects of higher education in universities, such as human resources planning, teaching load planning, faculty-to-student ratios, accreditation, quality requirements, lab capacity planning, equipment/teaching aids procurement, and financial planning, to mention a few. The implications of this study include assisting decision-makers in higher education institutions in matching their admission/enrollment capacity of student numbers between the macro-strategic and the micro-operational level. Full article

Modern cities have imposed a fast-paced lifestyle where more drivers on the road suffer from fatigue and sleep deprivation. Consequently, road accidents have increased, becoming one of the leading causes of injuries and death among young adults and children. These accidents can be prevented if fatigue symptoms are diagnosed and detected sufficiently early. For this reason, we propose and compare two AlexNet CNN-based models to detect drivers’ fatigue behaviors, relying on head position and mouth movements as behavioral measures. We used two different approaches. The first approach is transfer learning, specifically, fine-tuning AlexNet, which allowed us to take advantage of what the model had already learned without developing it from scratch. The newly trained model was able to predict drivers’ drowsiness behaviors. The second approach is the use of AlexNet to extract features by training the top layers of the network. These features were reduced using non-negative matrix factorization (NMF) and classified with a support vector machine (SVM) classifier. The experiments showed that our proposed transfer learning model achieved an accuracy of 95.7%, while the feature extraction SVM-based model performed better, with an accuracy of 99.65%. Both models were trained on a simulated NTHU Driver Drowsiness Detection dataset. Full article

Universities worldwide strive to provide the best student services possible, particularly those that support student achievements and career goals. Therefore, academic advising continues to be a significant part of the student experience, one which universities need to fully understand in terms of its objectives, application processes, and required skill. As a result of significant technological improvements since the turn of the millennium, including expanding internet applications and digital transformations, universities have established computer information systems that support academic advising and course registration services. This study examined the effects of modifications to the electronic academic advising and course registration systems at King Abdulaziz University in 2018, and then again in 2020, following a university-wide system failure in 2018 resulting from a demand overload. In 2018, a preliminary statistical analysis and student feedback survey were conducted by the authors to measure student satisfaction with the online portal On-Demand University Services (ODUS Plus). In addition to recommendations suggested by the 2018 analysis such as balancing the load distribution of the university’s network, organizational (i.e., non-technical) solutions, rules, and regulations were adjusted such as progressive course registration that prioritized those expected to graduate first. The survey and analysis were repeated by the authors in 2020 to assess improvements in student satisfaction. As a result of the changes, the investigation revealed improved student satisfaction with the performance of ODUS Plus and network access. Overall, students were significantly more satisfied in 2020 than in 2018. This research shows that some technical challenges can be resolved using re-engineered processes and organizational solutions. Full article

As social media has shifted from traditional to modern technical patterns, organizations have sought to take advantage of the presence of beneficiaries on social networks. They may serve customers, display ads, and respond to queries on social media accounts such as Twitter. The implementation of these services required a scientific study considering: (1) how to attract beneficiaries, (2) attraction times, and (3) measurement of the impact of that attraction. This study aimed to address these three points through an analysis of data from an educational organization’s Twitter account. We found that the interaction rates with tweets increased in the evening, and we identified the best times for the organization to reach more followers. We examined five months of data (an entire semester), analyzing thousands of tweets and their associated impressions, types of responses, integration ratio, and account usage. We also discovered that the quality of tweets had an impact on attracting new followers, particularly when tweeting media such as photos, videos, and other types of content. Finally, this research serves as a resource for educational organizations on new ways to publish accounts and foster organizational growth through electronic media. Full article

Introduction: Nursing students experience higher levels of stress than those in other health-related disciplines; however, there are limited data exploring stress among these students in a Saudi context. Aim: This study examines sources of stress among nursing students at an academic institution in Jeddah, Saudi Arabia, using a descriptive quantitative cross-sectional research design. Methods: Data were collected from a convenience sample of 500 undergraduate nursing students, with a response rate of 71.8%, using an adapted Stress in Nursing Students (SINS) questionnaire. Results: Nursing student sources of stress fell into three categories: academic concerns, clinical practice, and social factors. Discussion: The results demonstrate commonality between other countries’ sources of stress for nursing students but highlight cultural factors unique to Saudi Arabia. This study shows opportunities for cross-cultural learning and areas needing cultural tailoring to reduce stress among nursing students. Full article

The current era is characterized by the rapidly increasing use of computer-aided diagnosis (CAD) systems in the medical field. These systems need a variety of datasets to help develop, evaluate, and compare their performances fairly. Physicians indicated that breast anatomy, especially dense ones, and the probability of breast cancer and tumor development, vary highly depending on race. Researchers reported that breast cancer risk factors are related to culture and society. Thus, there is a massive need for a local dataset representing breast cancer in our region to help develop and evaluate automatic breast cancer CAD systems. This paper presents a public mammogram dataset called King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD) version 1. To our knowledge, KAU-BCMD is the first dataset in Saudi Arabia that deals with a large number of mammogram scans. The dataset was collected from the Sheikh Mohammed Hussein Al-Amoudi Center of Excellence in Breast Cancer at King Abdulaziz University. It contains 1416 cases. Each case has two views for both the right and left breasts, resulting in 5662 images based on the breast imaging reporting and data system. It also contains 205 ultrasound cases corresponding to a part of the mammogram cases, with 405 images as a total. The dataset was annotated and reviewed by three different radiologists. Our dataset is a promising dataset that contains different imaging modalities for breast cancer with different cancer grades for Saudi women. Full article

Big Data are highly effective for systematically extracting and analyzing massive data. It can be useful to manage data proficiently over the conventional data handling approaches. Recently, several schemes have been developed for handling big datasets with several features. At the same time, feature selection (FS) methodologies intend to eliminate repetitive, noisy, and unwanted features that degrade the classifier results. Since conventional methods have failed to attain scalability under massive data, the design of new Big Data classification models is essential. In this aspect, this study focuses on the design of metaheuristic optimization based on big data classification in a MapReduce (MOBDC-MR) environment. The MOBDC-MR technique aims to choose optimal features and effectively classify big data. In addition, the MOBDC-MR technique involves the design of a binary pigeon optimization algorithm (BPOA)-based FS technique to reduce the complexity and increase the accuracy. Beetle antenna search (BAS) with long short-term memory (LSTM) model is employed for big data classification. The presented MOBDC-MR technique has been realized on Hadoop with the MapReduce programming model. The effective performance of the MOBDC-MR technique was validated using a benchmark dataset and the results were investigated under several measures. The MOBDC-MR technique demonstrated promising performance over the other existing techniques under different dimensions. Full article

Universities seek to qualify students for their academic and career futures and meet labor market requirements. Hence, a preparatory year is provided to bridge the gap between high school outcomes and the needs of university study plans. The preparatory year is the first year of support in the life of university students, and for decades, it has been recognized as important. It is considered the most crucial stage in the life of university students, where they build and refine their skills and choose their academic major, in which they complete their academic and career life. Due to the importance of this year, which requires the full attention and care of the higher authorities in terms of preparation, development, and renewal, this research outlines the importance of the preparatory year at a local level and in international institutions. Moreover, it sheds light on the details of King Abdulaziz University (KAU) students as a case study. It measures the relationship between the admission weighted ratio (AWR), the college enrollment allocation weighted ratio (CEAWR), and the performance of three batches of male and female students (three consecutive years), with details of students’ college allocation after the end of the preparatory year. More importantly, it aims to realize students’ progress through their weighted averages during their preparatory year, and the extent to which the goals of the preparatory year are achieved. After an analytic survey of the reality of the preparatory year, based on the statistical tests conducted, this study found that it is not possible to be satisfied with the weighted ratio for colleges’ direct allocation of high school students. The tests showed a difference between the AWR and that of the CEAWR, which indicates a change in the level of students’ performance from high school to university, due to the positive impact of the preparatory year. More precisely, it was noted that there is a possibility of studying the sufficiency of the weighted ratio for the direct allocation of some colleges in future research. Full article

Computer-controlled virtual characters are essential parts of most virtual environments and especially computer games. Interaction between these virtual agents and human players has a direct impact on the believability of and immersion in the application. The facial animations of these characters are a key part of these interactions. The player expects the elements of the virtual world to act in a similar manner to the real world. For example, in a board game, if the human player wins, he/she would expect the computer-controlled character to be sad. However, the reactions, more specifically, the facial expressions of virtual characters in most games are not linked with the game events. Instead, they have pre-programmed or random behaviors without any understanding of what is really happening in the game. In this paper, we propose a virtual character facial expression probabilistic decision model that will determine when various facial animations should be played. The model was developed by studying the facial expressions of human players while playing a computer videogame that was also developed as part of this research. The model is represented in the form of trees with 15 extracted game events as roots and 10 associated animations of facial expressions with their corresponding probability of occurrence. Results indicated that only 1 out of 15 game events had a probability of producing an unexpected facial expression. It was found that the “win, lose, tie” game events have more dominant associations with the facial expressions than the rest of game events, followed by “surprise” game events that occurred rarely, and finally, the “damage dealing” events. Full article