Evaluation of E-learning Experience in the Light of the Covid-19 in Higher Education

Covid-19 has been stated as a worldwide outbreak of pandemic disease and crisis. The Covid-19 pandemic has dramatically affected the teaching and learning experience at universities and schools. In response, governments and higher education institutions around the world put significant efforts to ensure that students continue to obtain the best possible level of education and learning outcomes. As such effective evaluation of e-learning is essential in order to ensure that students get proper learning and education especially during the current circumstances of Covid-19. Our study was carried out to determine the main elements and factors related to students\u27 satisfaction and quality of e-learning during the Covid-19 pandemic era based on various aspects and dimensions of e-learning. The main findings of the study indicated that students satisfaction and evaluation of the e-learning experience during the pandemic were not promising. Therefore, higher education institutions should reconsider their efforts and approaches to improve the quality of e-learning and the learning outcomes achieved. For example, IT infrastructure, Internet access, and particularly network connectivity could be improved to support fully online courses. Such elements need to be addressed because of the prevalence of the current Covid-19 pandemic which perhaps will lead to e-learning occurring for a long time. With the move to e-learning, the size of the class (the number of students in each class) has been increased leading to other significant challenges related to communication and participation in the class and reducing the possible interactivity for each student. Furthermore, it has been also observed that new students need relevant training on IT and e-learning applications to ensure sufficient use and utilization of these applications in their e-learning journey

Assessment of Job Satisfaction among Faculty Members and its Relationship With Some Variables in Najran University

It is vital that colleges and universities monitor the satisfaction levels of their employees to secure high levels of their performance. The current study aimed to identify the impact of some variables (gender, Teaching experience and college type)on assessing the level of job satisfaction among faculty of Najran University. A survey was conducted in this study by a 23-item questionnaire, distributed to (262) male and female faculty members from various colleges. The questionnaire items distributed to four domains: Academic environment, salaries and financial support, psychological and social aspects, and interpersonal communication. The results showed a moderate degree of job satisfaction in general, and there are statistically significant differences due to (gender, teaching experience and college type), where the differences in favor of males, scientific colleges and more experienced. Keywords: job satisfaction, assessment, faculty and Najran Universit

ChatGPT and Beyond: The Generative AI Revolution in Education

The wide adoption and usage of generative artificial intelligence (AI) models, particularly ChatGPT, has sparked a surge in research exploring their potential applications in the educational landscape. This survey examines academic literature published between November, 2022, and July, 2023, specifically targeting high-impact research from Scopus-indexed Q1 and Q2 journals. This survey delves into the practical applications and implications of generative AI models across a diverse range of educational contexts. Through a comprehensive and rigorous evaluation of recent academic literature, this survey seeks to illuminate the evolving role of generative AI models, particularly ChatGPT, in education. By shedding light on the potential benefits, challenges, and emerging trends in this dynamic field, the survey endeavors to contribute to the understanding of the nexus between artificial intelligence and education. The findings of this review will empower educators, researchers, and policymakers to make informed decisions about the integration of AI technologies into learning environments

Credit Card Security System and Fraud Detection Algorithm

Credit card fraud is one of the most critical threats affecting individuals and companies worldwide, particularly with the growing number of financial transactions involving credit cards every day. The most common threats are likely to come from database breaches and identity theft. All these threats threat put the security of financial transactions at severe risk and require a fundamental solution. This dissertation aims to suggest a secure online payment system that significantly improves credit card security. Our system can be particularly resilient to potential cyber-attacks, unauthorized users, man-in-the-middle, and guessing attacks for credit card number generation or illegal financial activities by utilizing a secure communication channel between the cardholder and server. Our system uses a shared secret and a verification token that allow both sides to communicate through encrypted information. Furthermore, our system is designed to generate a one-time credit card number at the user’s machine that is verified by the server without sharing the credit card number over the network. Our approach combines the machine learning (ML) algorithms with unique temporary credit card numbers in one integrated system, which is the first approach in the online credit card protection system. The new security system generates a one-time-use credit card number for each transaction with a predetermined amount of money. Simultaneously, the system can detect potential fraud utilizing ML algorithm with new critical features such as the IMEI or I.P. address, the transaction’s location, and other features. The contribution of this research is two-fold: (1) a method is proposed to generate a unique, authenticatable one-time credit card number to effectively defend against the database breaches, and (2) a credit card fraud prevention system is proposed with multiple security layers that are achieved by the integration of authentication, ML-based fraud detection, and the one-time credit card number generation. The dissertation improves consumers’ trust and confidence in the credit card system’s security and enhances satisfaction with credit cards’ various financial transactions. Further, the system uses the current online credit card infrastructure; hence it can be implemented without tangible infrastructure cost

SemEval-2016 task 5 : aspect based sentiment analysis

International audienceThis paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams

SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans

COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which we include a global average pooling layer, flattening, and two dense layers that are fully connected. The model’s effectiveness is evaluated using balanced and unbalanced COVID-19 radiography datasets. After that, our model’s performance is analyzed using six evaluation measures: accuracy, sensitivity, specificity, precision, F1-score, and Matthew’s correlation coefficient (MCC). Experiments demonstrated that the proposed SEL-COVIDNET with tuned DenseNet121, InceptionResNetV2, and MobileNetV3Large models outperformed the results of comparative SOTA for multi-class classification (COVID-19 vs. No-finding vs. Pneumonia) in terms of accuracy (98.52%), specificity (98.5%), sensitivity (98.5%), precision (98.7%), F1-score (98.7%), and MCC (97.5%). For the COVID-19 vs. No-finding classification, our method had an accuracy of 99.77%, a specificity of 99.85%, a sensitivity of 99.85%, a precision of 99.55%, an F1-score of 99.7%, and an MCC of 99.4%. The proposed model offers an accurate approach for detecting COVID-19 patients, which aids in the containment of the COVID-19 pandemic

Flexible manufacturing system utilizing computer integrated control and modeling

In today\u27s fast-automated production, Flexible Manufacturing Systems (FMS) play a very important role by processing a variety of different types of workpieces simultaneously. This study provides valuable information about existing FMS workcells and brings to light a unique concept called Programmable Automation. Another integrated concept of programmable automation that is discussed is the use of two feasibility approaches towards modeling and controlling FMS operations; the most commonly used is programmable logic controllers (PLC), and the other one, which has not yet implemented in many industrial applications is Petri Net controllers (PN). This latter method is a unique powerful technique to study and analyze any production line or any facility, and it can be used in many other applications of automatic control. Programmable Automation uses a processor in conventional metal working machines to perform certain tasks through program instructions. Drilling, milling and chamfering machines are good examples for such automation. Keeping the above issues in concem; this research focuses on other core components that are used in the FMS workcell at New Jersey Institute of Technology, such as; industrial robots, material handling system and finally computer vision