The widespread use of computer technologies in education has reshaped the roles of instructors, who are encouraged to innovate interactive forms of technology‐supported instruction that promote participation and collaboration. This in turn engenders new experiences of teaching that need to be gathered and capitalized as teaching assets to be shared among communities of instructors. Among these experiences, best teaching practices (BTPs) are instructional practices accumulated in teaching that have been proven to work well, give good results, and can therefore be recommended as a model. Identifying and sharing best practices means duplicating successes which help instructors learn from each other and deliver better quality teaching. This paper presents a knowledge management framework for acquiring, coding, sharing, and reusing BTPs. To encourage instructors’ participation, the framework is based on peer scoring of BTPs, which stimulates contribution and interaction. The framework has been implemented as a knowledge portal that allows instructors to create, store, search, and share BTPs and to receive feedback and comments from other users, providing many useful functionalities and services to users as individuals and communities. The paper presents also a real‐life case study, lessons learned from using the system within a community of instructors, and a system evaluation of the effectiveness of reusing BTPs using the reuse effort and impact metrics. © 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:163–178, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21776

No abstract

Prediction of financial and economic markets is very challenging but valuable for economists, business owners, and traders. Forecasting stock market prices depends on many factors, such as other markets' performance, economic state of a country, and others. In behavioral finance, people's emotions and opinions influence their transactional decisions and therefore the financial markets. The focus of this research is to predict the Saudi Stock Market Index by utilizing its previous values and the impact of people's sentiments on their financial decisions. Human emotions and opinions are directly influenced by media and news, which we incorporated by utilizing the Global Data on Events, Location, and Tone (GDELT) dataset by Google. GDELT is a collection of news from all over the world from different types of media such as TV, broad-casts, radio, newspapers, and websites. We extracted two time series from GDELT, filtered for Saudi Arabian news. The two time series rep-resent daily values of tone and social media attention. We studied the characteristics of the generated multivariate time series, then deployed and compared multiple multivariate models to predict the daily index of the Saudi stock market.

The latest COVID-19 pandemic is a specific and unusual event. It forced universities to close their doors and move fully to distance education. The sudden shift from traditional education to full distance education created many challenges and difficulties for universities, faculty members, and students. This study aims to investigate the challenges and obstacles faced by undergraduate women in Saudi Arabia universities while using online-only learning during the COVID-19 pandemic outbreak. Moreover, this study provides some recommendations to address these challenges from undergraduate women’s perspectives. The study used a qualitative research methodology to investigate the challenges and difficulties. The participants were undergraduate women selected using random purposive sampling technique from the population of College of Computer and Information Sciences (CCIS) at Princess Nourah Bint Abdulrahman University (PNU), Riyadh, Saudi Arabia. The final sample consisted of 68 undergraduate women who responded to a predesigned open-ended questionnaire that was sent via e-mail to targeted respondents. The data gathered from the questionnaire were analyzed using qualitative content analysis. Results of the research revealed that the most obvious challenges identified by the participants were technical issues, lack of in-person interaction, distractions and time management, lack of a systematic schedule, stress and psychological pressure, missing the traditional university environment, limited availability of digital devices, and lack of access to external learning resources.

Increased traffic density, combined with global population development, has resulted in increasingly congested roads, increased air pollution, and increased accidents. Globally, the overall number of automobiles has expanded dramatically during the last decade. Traffic monitoring in this environment is undoubtedly a significant difficulty in various developing countries. This work introduced a novel vehicle detection and classification system for smart traffic monitoring that uses a convolutional neural network (CNN) to segment aerial imagery. These segmented images are examined to further detect the vehicles by incorporating novel customized pyramid pooling. Then, these detected vehicles are classified into various subcategories. Finally, these vehicles are tracked via Kalman filter (KF) and kernelized filter-based techniques to cope with and manage massive traffic flows with minimal human intervention. During the experimental evaluation, our proposed system illustrated a remarkable vehicle detection rate of 95.78% over the Vehicle Aerial Imagery from a Drone (VAID), 95.18% over the Vehicle Detection in Aerial Imagery (VEDAI), and 93.13% over the German Aerospace Center (DLR) DLR3K datasets, respectively. The proposed system has a variety of applications, including identifying vehicles in traffic, sensing traffic congestion on a road, traffic density at intersections, detecting various types of vehicles, and providing a path for pedestrians.

The novel coronavirus 2019 (COVID-19) spread rapidly around the world and its outbreak has become a pandemic. Due to an increase in afflicted cases, the quantity of COVID-19 tests kits available in hospitals has decreased. Therefore, an autonomous detection system is an essential tool for reducing infection risks and spreading of the virus. In the literature, various models based on machine learning (ML) and deep learning (DL) are introduced to detect many pneumonias using chest X-ray images. The cornerstone in this paper is the use of pretrained deep learning CNN architectures to construct an automated system for COVID-19 detection and diagnosis. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pre-trained CNN models, AlexNet and Xception. Hence, we propose COVID-AleXception: a neural network that is a concatenation of the AlexNet and Xception models for the overall improvement of the prediction capability of this pandemic. To evaluate the proposed model and build a dataset of large-scale X-ray images, there was a careful selection of multiple X-ray images from several sources. The COVID-AleXception model can achieve a classification accuracy of 98.68%, which shows the superiority of the proposed model over AlexNet and Xception that achieved a classification accuracy of 94.86% and 95.63%, respectively. The performance results of this proposed model demonstrate its pertinence to help radiologists diagnose COVID-19 more quickly.

Obesity is one of the world’s most serious health issues. Therefore, therapists have looked for methods to fight obesity. Currently, technology-based intervention options in medical settings are very common. One such technology is virtual reality (VR) which has been used in the treatment of obesity since the late 1990s. The main objective of this study is to review the literature on the use of VR in the treatment of obesity and overweight to better understand the role of VR-based interventions in this field. To this end, four databases (PubMed, Medline, Scopus, and Web of Science) were searched for related publications from 2000 to 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From the 645 articles identified, 24 were selected. The main strength of this study is that it is the first systematic review to focus completely on the use of VR in the treatment of obesity. It includes most research in which VR was utilized to carry out the intervention. Although several limitations were detected in the reviewed studies, the findings of this review suggest that employing VR for self-monitoring of diet, physical activity, and/or weight is effective in supporting weight loss as well as improving satisfaction of body image and promoting health self-efficacy in overweight or obese persons.

Big data streams started becoming ubiquitous in recent years, thanks to rapid generation of massive volumes of data by different applications. It is challenging to apply existing data mining tools and techniques directly in these big data streams. At the same time, streaming data from several applications results in two major problems such as class imbalance and concept drift. The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection (MOMBD-CDD) method on High-Dimensional Streaming Data. The presented MOMBD-CDD model has different operational stages such as pre-processing, CDD, and classification. MOMBD-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique (SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE, Glowworm Swarm Optimization (GSO) algorithm is employed. Besides, Statistical Test of Equal Proportions (STEPD), a CDD technique is also utilized. Finally, Bidirectional Long Short-Term Memory (Bi-LSTM) model is applied for classification. In order to improve classification performance and to compute the optimum parameters for Bi-LSTM model, GSO-based hyperparameter tuning process is carried out. The performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection (NSL KDDCup) dataset and ECUE spam dataset. An extensive experimental validation process confirmed the effective outcome of MOMBD-CDD model. The proposed model attained high accuracy of 97.45% and 94.23% on the applied KDDCup99 Dataset and ECUE Spam datasets respectively.