Translating a value-based framework for resilient e-learning impact in post COVID-19 times: Research-based Evidence from Higher Education in Kuwait.

The covid-19 pandemic has changed people's daily lives and behaviors all across the world and has impacted practically every element of human existence. The introduction of remote education systems and the move toward online learning have had some of the most significant effects. The on-site operations of educational institutions, such as schools, colleges, and universities, have had to be suspended in order to stop the virus' spread. In order to effectively disseminate instructional material and guarantee the unbroken progression of students' academic endeavors, educators have been forced to look for novel approaches. The study used the Value-Based Adoption Model (VAM) as a conceptual framework to look into the factors that affected Kuwait's e-learning outcomes in the midst of the covid-19 pandemic. 382 students at Kuwaiti universities and colleges were the source of quantitative data collection. The findings revealed that peer interaction emerged as the most influential factor in shaping outcomes within the educational context of Kuwait, while instructors and course design factors were not significant. Using the VAM, this study investigated the impact of several factors on students' e-learning results during times of crisis. The research expands the existing knowledge base in the field on this subject and suggests developing a well-organized online learning crisis approach. The main contribution of this work is summarized on (i) An integrated framework for the quality of the e-learning experience in universities in post-covid-19 times and (ii) A resilient higher education institutional learning strategy model in post-covid-19 times. The findings of this paper can be generalizable to other Gulf Corporation Council (GCC) countries such as Kingdom of Saudi Arabia, Qatar, United Arab Emirates (UAE), Bahrain and Oman. This is due to the shared cultural traditions and values, along with similar educational systems among these nations.

Attitudes and Perceptions of Health Leaders for the Quality Enhancement of Workforce in Saudi Arabia.

BACKGROUND AND AIM: Besides the unique exposure and experience of health leaders in facing challenges and overcoming them, and the relatively fewer articles relating to the perception of health leaders in workforce quality enhancement, health leadership plays a crucial role in redirecting the workforce, increasing job satisfaction, professional development, and burnout prevention. Thus, this study aimed to understand the current healthcare workforce quality and future expectations from the attitudes and perceptions of health leaders. METHODS: A qualitative research was carried out using semi-structured interviews consisting of 24 different questions. Participants of the study were healthcare leaders from different backgrounds and governmental institutions. All interviews were recorded, transcribed, and then analyzed using thematic analysis via the N-Vivo program. RESULTS: Eleven participants were involved in the study, with one female and ten males. A thematic analysis and N-Vivo program yielded 5 main themes: (1) workforce competency, (2) health transformation, (3) leadership, (4) workforce planning, and (5) healthcare quality, with 22 emerging sub-themes. Moreover, participants responded with different attitudes and perceptions. CONCLUSION: Health leaders are satisfied with the current direction of workforce competency and planning, yet fragmentation of the system and poor accessibility may need further enhancement. Furthermore, misutilization of services and the uncertainty of the future and talent pool are potential barriers for capability building. Moreover, with the existing gap in the workforce, health leaders believe that privatization and corporatization may have a positive effect. Aside from that, Saudization with the current plan of having a minimum standard of accepting non-Saudis in certain areas might benefit in maintaining competition and enriching experience. However, catching up with further research in healthcare quality in Saudi Arabia is needed because of the ongoing health transformation.

Effectiveness and Preparedness of Institutions' E-Learning Methods During the COVID-19 Pandemic for Residents' Medical Training in Saudi Arabia: A Pilot Study.

Background: Under the urgent circumstances of the COVID-19 pandemic, higher education institutions of an international scale have resorted to online education methods, exclusive or not. Among those, medical institutions are under double pressure, fighting the pandemic's effects and, at the same time providing efficient clinical training to their residents. The main aim of the study is to evaluate the preparedness of the educational institutions for the e-learning platform transition for the delivery of medical training and also to evaluate the overall satisfaction level of the participants with their e-learning experience. Methods: This is an observational cross-sectional study design. The survey's sample included 300 medical students and residents of multiple training levels and specialties, coming from more than 15 different cities of Saudi Arabia. Filling the questionnaire required specific inclusion criteria and all obtained data were secured by the Saudi Commission of Health specialty. The main objective was to evaluate the quality of e-learning methods provided by medical universities. For the collection of the data, Survey Monkey software was used and the analysis was conducted with SPSS. Results: The study found that the frequency of digital education use increased by ~61% during the coronavirus crisis, while almost 9 out of 10 residents have used some e-learning platform. It was reported that before the pandemic, participants' online training was deemed to be rather ineffective, given the rate of 3.65 out of 10. However, despite the increase in e-learning use after COVID-19, many obstacles arose duringcthe adaptation process. According to our survey: lectures and training sessions were not conducted as per the curriculum (56.33%); both students and instructors' academic behavior and attitude changed (48.33%); engagement, satisfaction, and motivation in class were rated low (5.93, 6.33, and 6.54 out of 10 accordingly), compared to the desired ones. Still, participants accredited e-learning as a potential mandatory tool (77.67%) and pinpointed the qualifications that in their opinion will maximize educational impact. Conclusion: The study concluded that innovative restructuring of online education should be based on defined critical success factors (technical support, content enhancement, pedagogy etc.) and if possible, set priority levels, so that a more permanent e-learning practice is achievable. Also our study confirmed that students were overall satisfied with the e-learning support of the training method.

Brain Hemorrhage Classification in CT Scan Images Using Minimalist Machine Learning.

Over time, a myriad of applications have been generated for pattern classification algorithms. Several case studies include parametric classifiers such as the Multi-Layer Perceptron (MLP) classifier, which is one of the most widely used today. Others use non-parametric classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naïve Bayes (NB), Adaboost, and Random Forest (RF). However, there is still little work directed toward a new trend in Artificial Intelligence (AI), which is known as eXplainable Artificial Intelligence (X-AI). This new trend seeks to make Machine Learning (ML) algorithms increasingly simple and easy to understand for users. Therefore, following this new wave of knowledge, in this work, the authors develop a new pattern classification methodology, based on the implementation of the novel Minimalist Machine Learning (MML) paradigm and a higher relevance attribute selection algorithm, which we call dMeans. We examine and compare the performance of this methodology with MLP, NB, KNN, SVM, Adaboost, and RF classifiers to perform the task of classification of Computed Tomography (CT) brain images. These grayscale images have an area of 128 × 128 pixels, and there are two classes available in the dataset: CT without Hemorrhage and CT with Intra-Ventricular Hemorrhage (IVH), which were classified using the Leave-One-Out Cross-Validation method. Most of the models tested by Leave-One-Out Cross-Validation performed between 50% and 75% accuracy, while sensitivity and sensitivity ranged between 58% and 86%. The experiments performed using our methodology matched the best classifier observed with 86.50% accuracy, and they outperformed all state-of-the-art algorithms in specificity with 91.60%. This performance is achieved hand in hand with simple and practical methods, which go hand in hand with this trend of generating easily explainable algorithms.

Moving Towards Intelligent Transportation via Artificial Intelligence and Internet-of-Things.

One of the key smart city visions is to bring smarter transport networks, specifically intelligent/smart transportation [...].

Social media mining for smart cities and smart villages research.

The imperative of well-being and improved quality of life in smart cities context can only be attained if the smart services, so central to the concept of smart cities, correspond with the needs, expectations and skills of cities' inhabitants. Considering that social media generate and/or open real-time entry points to vast amounts of data pertinent to well-being and quality of life, such as citizens' expectations, opinions, as well as to recent developments related to regulatory frameworks, debates, political decisions and policymaking, the big question is how to exploit the potential inherent in social media and use it to enhance the value added smart cities generate. Social mining is traditionally understood as the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. In the context of smart cities, this special issue focuses on how social media data, also potentially combined with other data, can be used to optimize the efficiency of city operations and services, and thereby contribute more efficiently to citizens' well-being and quality of life.

A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM.

Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. Multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals.