Explore the vast range of titles available.

With the growth of e-services in the past two decades, the concept of web accessibility has been given attention to ensure that every individual can benefit from these services without any barriers. Web accessibility is considered one of the main factors that should be taken into consideration while developing webpages. Web Content Accessibility Guidelines 2.0 (WCAG 2.0) have been developed to guide web developers to ensure that web contents are accessible for all users, especially disabled users. Many automatic tools have been developed to check the compliance of websites with accessibility guidelines such as WCAG 2.0 and to help web developers and content creators with designing webpages without barriers for disabled people. Despite the popularity of accessibility evaluation tools in practice, there is no systematic way to compare the performance of web accessibility evaluators. This paper first presents two novel frameworks. The first one is proposed to compare the performance of web accessibility evaluation tools in detecting web accessibility issues based on WCAG 2.0. The second framework is utilized to evaluate webpages in meeting these guidelines. Six homepages of Saudi universities were chosen as case studies to substantiate the concept of the proposed frameworks. Furthermore, two popular web accessibility evaluators, Wave and SiteImprove, are selected to compare their performance. The outcomes of studies conducted using the first proposed framework showed that SiteImprove outperformed WAVE. According to the outcomes of the studies conducted, we can conclude that web administrators would benefit from the first framework in selecting an appropriate tool based on its performance to evaluate their websites based on accessibility criteria and guidelines. Moreover, the findings of the studies conducted using the second proposed framework showed that the homepage of Taibah University is more accessible than the homepages of other Saudi universities. Based on the findings of this study, the second framework can be used by web administrators and developers to measure the accessibility of their websites. This paper also discusses the most common accessibility issues reported by WAVE and SiteImprove.

During the past 10 years, exploiting engineered nanoparticles in agricultural sector has been rapidly increased. Nanoparticles are used to increase the productivity of different crops particularly under biotic and abiotic stresses. This study aims to test the ability of nanosilica (NS) to ameliorate the detrimental impact of Na + with different concentrations on the seed germination and the growth of common bean seedlings. Five doses of Na + have been prepared from NaCl, i.e., 1000, 2000, 3000, 4000, and 5000 mg L −1 , and distilled water was applied as a control. Seeds and seedlings were treated with three different NS concentrations (100, 200, and 300 mg L −1 ). The results proved that Na + concentrations had detrimental effects on all measured parameters. However, treating seeds and seedlings with NS improved their growth and resulted in higher values for all measurements. For instance, the addition of 300 mg L −1 NS leads to an increase of the final germination percentage, vigor index, and germination speed for seeds irrigated with 5000 mg Na +  L −1 by 19.7, 80.7, and 22.6%, respectively. Although common bean seedlings could not grow at the highest level of Na + , fortification seedlings with NS helped them to grow well under 5000 mg L −1 of Na + . An increase of 11.1 and 23.1% has been measured for shoot and root lengths after treating seedlings with 300 mg L −1 NS under irrigation with 5000 mg Na +  L −1 solutions, and also at the same treatment, shoot and root dry masses are enhanced by 110.9 and 328.0%, respectively. These results proved the importance of using NS to relieve the detrimental effects of Na + -derived salinity. This finding could be reinforced by low Na content which was measured in plant tissues after treating seedlings with 300 mg L −1 of NS.

Sign language is an essential means of communication for individuals with hearing disabilities. However, there is a significant shortage of sign language interpreters in some languages, especially in Saudi Arabia. This shortage results in a large proportion of the hearing-impaired population being deprived of services, especially in public places. This paper aims to address this gap in accessibility by leveraging technology to develop systems capable of recognizing Arabic Sign Language (ArSL) using deep learning techniques. In this paper, we propose a hybrid model to capture the spatio-temporal aspects of sign language (i.e., letters and words). The hybrid model consists of a Convolutional Neural Network (CNN) classifier to extract spatial features from sign language data and a Long Short-Term Memory (LSTM) classifier to extract spatial and temporal characteristics to handle sequential data (i.e., hand movements). To demonstrate the feasibility of our proposed hybrid model, we created a dataset of 20 different words, resulting in 4000 images for ArSL: 10 static gesture words and 500 videos for 10 dynamic gesture words. Our proposed hybrid model demonstrates promising performance, with the CNN and LSTM classifiers achieving accuracy rates of 94.40% and 82.70%, respectively. These results indicate that our approach can significantly enhance communication accessibility for the hearing-impaired community in Saudi Arabia. Thus, this paper represents a major step toward promoting inclusivity and improving the quality of life for the hearing impaired.

Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber abuse, where rumors or lies about the victim are posted on the internet to send threatening messages or to share the victim’s personal information. During pandemics, a large amount of rumors spreads on social media very fast, which have dramatic effects on people’s health. Detecting these rumors manually by the authorities is very difficult in these open platforms. Therefore, several researchers conducted studies on utilizing intelligent methods for detecting such rumors. The detection methods can be classified mainly into machine learning-based and deep learning-based methods. The deep learning methods have comparative advantages against machine learning ones as they do not require preprocessing and feature engineering processes and their performance showed superior enhancements in many fields. Therefore, this paper aims to propose a Novel Hybrid Deep Learning Model for Detecting COVID-19-related Rumors on Social Media (LSTM–PCNN). The proposed model is based on a Long Short-Term Memory (LSTM) and Concatenated Parallel Convolutional Neural Networks (PCNN). The experiments were conducted on an ArCOV-19 dataset that included 3157 tweets; 1480 of them were rumors (46.87%) and 1677 tweets were non-rumors (53.12%). The findings of the proposed model showed a superior performance compared to other methods in terms of accuracy, recall, precision, and F-score.

Background: With digital transformation and growing social media usage, kids spend considerable time on the web, especially watching videos on YouTube. YouTube is a source of education and entertainment media that has a significant impact on the skill improvement, knowledge, and attitudes of children. Simultaneously, harmful and inappropriate video content has a negative impact. Recently, researchers have given much attention to these issues, which are considered important for individuals and society. The proposed methods and approaches are to limit or prevent such threats that may negatively influence kids. These can be categorized into five main directions. They are video rating, parental control applications, analysis meta-data of videos, video or audio content, and analysis of user accounts. Objective: The purpose of this study is to conduct a systematic review of the existing methods, techniques, tools, and approaches that are used to protect kids and prevent them from accessing inappropriate content on YouTube videos. Methods: This study conducts a systematic review of research papers that were published between January 2016 and December 2022 in international journals and international conferences, especially in IEEE Xplore Digital Library, ACM Digital Library, Web of Science, Google Scholar, Springer database, and ScienceDirect database. Results: The total number of collected articles was 435. The selection and filtration process reduced this to 72 research articles that were appropriate and related to the objective. In addition, the outcome answers three main identified research questions. Significance: This can be beneficial to data mining, cybersecurity researchers, and peoples’ concerns about children’s cybersecurity and safety.

The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels. In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes. The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve. The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.

Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.

In this era of digital transformation, when the amount of scholarly literature is rapidly growing, hundreds of papers are published online daily with regard to different fields, especially in relation to academic subjects. Therefore, it difficult to find an expert/author to collaborate with from a specific research area. This is thought to be one of the most challenging activities in academia, and few people have considered authors’ multi-factors as an enhanced method to find potential collaborators or to identify the expert among them; consequently, this research aims to propose a novel model to improve the process of recommending authors. This is based on the authors’ similarity measurements by extracting their explicit and implicit topics of interest from their academic literature. The proposed model mainly consists of three factors: author-selected keywords, the extraction of a topic’s distribution from their publications, and their publication-based statistics. Furthermore, an enhanced approach for identifying expert authors by extracting evidence of expertise has been proposed based on the topic-modeling principle. Subsequently, an interactive network has been constructed that represents the predicted authors’ collaborative relationship, including the top-k potential collaborators for each individual. Three experiments have been conducted on the collected data; they demonstrated that the most influential factor for accurately recommending a collaborator was the topic’s distribution, which had an accuracy rate of 88.4%. Future work could involve building a heterogeneous co-collaboration network that includes both the authors with their affiliations and computing their similarities. In addition, the recommendations would be improved if potential and real collaborations were combined in a single network.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with unpredictable course and flares. The clinical manifestation can vary from mild to severe and life-threatening disease. Infection is the primary cause of mortality in hospitalized SLE patients. There is a paucity of evidence to support the co-management of SLE with major organ involvement and sepsis. We describe the clinical response of a 35-year-old male diagnosed with SLE; then, he developed severe sepsis and a flare of SLE with major organ involvement including lupus nephritis (LN), myocarditis, and neuropsychiatric systemic lupus erythematosus (NPSLE). Based on the patient’s condition, a treatment dilemma was encountered, and after a multidisciplinary meeting, the decision was made to use a combination of rituximab (RTX), intravenous immunoglobulin (IVIG), and pulse steroid. Shortly, the patient’s condition started to improve, and his symptoms were resolved. In conclusion, our clinical case suggests that combined RTX, IVIG, and pulse steroid seem to be effective and safe in achieving clinical response, thus representing a good choice for managing severe SLE flares in sepsis.

The entire world is transforming quickly under the present innovations. The Internet has become a basic requirement for everybody with the Web being utilized in every field. With the rapid increase in social network applications, people are using these platforms to voice them their opinions with regard to daily issues. Gathering and analyzing peoples’ reactions toward buying a product, public services, and so on are vital. Sentiment analysis (or opinion mining) is a common dialogue preparing task that aims to discover the sentiments behind opinions in texts on varying subjects. In recent years, researchers in the field of sentiment analysis have been concerned with analyzing opinions on different topics such as movies, commercial products, and daily societal issues. Twitter is an enormously popular microblog on which clients may voice their opinions. Opinion investigation of Twitter data is a field that has been given much attention over the last decade and involves dissecting “tweets” (comments) and the content of these expressions. As such, this paper explores the various sentiment analysis applied to Twitter data and their outcomes.