The Impact of COVID-19 Pandemic on Student’s E-Learning Experience in Jordan

Since the beginning of the COVID-19 pandemic Universities around the world are taking rapid actions to ensure students learning continuity and secure the well-being of their students. This study aims at exploring the student’s e-learning experience in Jordanian Universities as well as e-learning readiness during the pandemic. While each university is unique, we hope our assessment can provide some insights into how well the student’s e-learning experience was during the pandemic. A structural online questionnaire was distributed, followed by descriptive analysis. Students from remote and disadvantaged areas primarily faced enormous challenges such as technological accessibility, poor internet connectivity, and harsh study environments. This study also highlights the role of electronic commerce in transforming distance learning. Further investments and contingency plans are needed to develop a resilient education system that supports electronic and distance learning throughout Jordan

A New Stock Price Forecasting Method Using Active Deep Learning Approach

Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. A new set of features is used to enhance the possibility of giving more accurate results with fewer losses by creating a six-feature set (that includes High, Low, Volume, Open, HiLo, OpSe), rather than the traditional four-feature set (High, Low, Volume, Open). The study also investigates the effect of data size by using datasets (Apple, ExxonMobil, Tesla, Snapchat) of different sizes to boost open innovation dynamics. The effect of the business sector in terms of the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo, and OpSe) are evaluated according to the model’s outcome, showing fewer losses than the original approach, which utilizes the original feature set. The results show that LSTM-based models improved using the new approach, even though all models showed a comparative result wherein no model showed better results or continuously outperformed other models. Finally, the added new features positively affected the prediction models’ performance

Development of Lévy flight-based reptile search algorithm with local search ability for power systems engineering design problems

International audienceThe need for better-performing algorithms to solve real-world power systems engineering problems has always been a challenging topic. Due to their stochastic nature, metaheuristic algorithms can provide better results. Thus, they have a rising trend in terms of investigation. This paper is a further attempt to offer a better optimizing structure, therefore, aims to provide a better-performing algorithm both for designing an appropriate proportional–integral–derivative (PID) controller to effectively operate an automatic voltage regulator (AVR) system and extracting the optimum parameters of a power system stabilizer (PSS) employed in a single-machine infinite-bus (SMIB) power system. Therefore, the paper discusses the development of the Lévy flight-based reptile search algorithm with local search capability and evaluates its potential against challenging power systems engineering optimization problems. The Lévy flight concept is used for better exploration capability in the proposed algorithm, whereas the Nelder–Mead simplex search algorithm is integrated for further exploitation. The latter case is confirmed through 23 benchmark functions with different features using statistical and nonparametric tests. The superiority of the proposed Lévy flight-based reptile search and Nelder–Mead (L-RSANM) algorithm-based PID controller for the AVR system is demonstrated comparatively using convergence, statistical and nonparametric tests along with transient and frequency responses. Besides, it is also assessed against previously reported and different methods, showing further superiority for AVR system control. Furthermore, the extraordinary ability of the L-RSANM algorithm to design an efficient PSS employed in the SMIB power system is demonstrated, as well. In conclusion, the proposed L-RSANM algorithm is shown to be more capable to solve the challenging power systems engineering design problems

The Effects of Online Learning on Students’ Performance: A Comparison Between UK and Jordanian Universities

The global pandemic of Covid-19 has caused lockdowns across the globe, causing education institutions to shut down. As a result, classes have been held online. This study investigates the impact of online learning on student performance by comparing the impact on Jordan and the UK. Both countries have been reported to have high technological competency but are known to have varying sociodemographic structures. Surveys were conducted on undergraduate students from both countries (N = 780) to analyse students’ perception of online learning, self-perception of academic capabilities, and faculty performance during online learning. Semi-structured interviews were conducted on professors from both countries (N = 8). The findings indicate that both Jordan and the UK have been very similarly affected by in terms of student performance, with major challenges being in communication, technological competency, access to hardware for taking online classes, absenteeism, and drop-outs. Some benefits to student performance were identified as having access to recorded lectures, having more access to faculties through e-mail and extended office hours. Ethical implications were not commented on. Privacy concerns were largely voiced by faculties

A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48

International audienceIn this study, the possibility of using and applying the capabilities of artificial intelligence (AI) and machine learning (ML) to increase the effectiveness of Internet of Things (IoT) and big data in developing a system that supports decision makers in the medical fields was studied. This was done by studying the performance of three well-known classification algorithms Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree-J48 (J48), to predict the probability of heart attack. The performance of the algorithms for accuracy was evaluated using the Healthcare (heart attack possibility) dataset, freely available on kagle. The data was divided into three categories consisting of (303, 909, 1808) instances which were analyzed on the WEKA platform. The results showed that the RFC was the best performer

Upshots of Intrinsic Traits on Social Entrepreneurship Intentions among Young Business Graduates: An Investigation through Moderated-Mediation Model

Social entrepreneurship has recently become a much-desired area of research for academia, practices, and policymaking. Natural or cognitive personal thoughtfulness like loving-kindness meditation (LKM) and compassion trigger individual intentions towards the social entrepreneurial venture. In this process of individual social entrepreneurial intention personality trait plays a very vital role, such as entrepreneurship resilience. For this study, a purposive sampling technique was incorporated and data was collected from 631 business and management sciences students. Data is analyzed by SPSS 23 and for the hypothesis testing, we used the bootstrap analysis of Hayes PROCESS v3.5. This study depicts that LKM has a positive significant impact on compassion and no significant impact on social entrepreneurship intentions while resilience strengthens the direct relationship of compassion with social entrepreneurship and the indirect relationship of LKM with social entrepreneurship via compassion. This study contributes to solving the economic and social problems over the globe especially by boosting the LKM and resilience traits so that the young graduate commence social entrepreneurship. This study helps the academician and policymakers to adopt strategies through which they can encourage youth to indulge in social entrepreneurial ventures solve the social problem and decrease unemployment

Arabic Text Classification Using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect

International audienceArab customers give their comments and opinions daily, and it increases dramatically through online reviews of products or services from companies, in both Arabic, and its dialects. This text describes the user’s condition or needs for satisfaction or dissatisfaction, and this evaluation is either negative or positive polarity. Based on the need to work on Arabic text sentiment analysis problem, the case of the Jordanian dialect. The main purpose of this paper is to classify text into two classes: negative or positive which may help the business to maintain a report about service or product. The first phase has tools used in natural language processing; the stemming, stop word removal, and tokenization to filtering the text. The second phase, modified the Artificial Bee Colony (ABC) Algorithm, with Upper Confidence Bound (UCB) Algorithm, to promote the exploitation ability for the minimum dimension, to get the minimum number of the optimal feature, then using forward feature selection strategy by four classifiers of machine learning algorithms: (K-Nearest Neighbors (KNN), Support vector machines (SVM), Naïve-Bayes (NB), and Polynomial Neural Networks (PNN). This proposed model has been applied to the Jordanian dialect database, which contains comments from Jordanian telecom company’s customers. Based on the results of sentiment analysis few suggestions can be provided to the products or services to discontinue or drop, or upgrades it. Moreover, the proposed model is applied to the database of the Algerian dialect, which contains long Arabic texts, in order to see the efficiency of the proposed model for short and long texts. Four performance evaluation criteria were used: precision, recall, f1-score, and accuracy. For a future step, in order to build on or use for the classification of Arabic dialects, the experimental results show that the proposed model gives height accuracy up to 99% by applying to the Jordanian dialect, and a 82% by applying to the Algerian dialect

Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications

The Moth flame optimization (MFO) algorithm belongs to the swarm intelligence family and is applied to solve complex real-world optimization problems in numerous domains. MFO and its variants are easy to understand and simple to operate. However, these algorithms have successfully solved optimization problems in different areas such as power and energy systems, engineering design, economic dispatch, image processing, and medical applications. A comprehensive review of MFO variants is presented in this context, including the classic version, binary types, modified versions, hybrid versions, multi-objective versions, and application part of the MFO algorithm in various sectors. Finally, the evaluation of the MFO algorithm is presented to measure its performance compared to other algorithms. The main focus of this literature is to present a survey and review the MFO and its applications. Also, the concluding remark section discusses some possible future research directions of the MFO algorithm and its variants

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research