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Prof. Miltiadis D. Lytras
King Abdulaziz University

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0 Digital Transformation
0 Information Systems
0 Innovation Management
0 Knowledge Management
0 Semantic Web

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Knowledge Management
Smart Cities
Information Systems
Semantic Web
Technology Enhanced Learning
Cognitive Computing
Digital Transformation
Innovation Management

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Editorial
Published: 27 August 2021 in Sustainability
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The recent pandemic of COVID-19 challenges the delivery of training and education worldwide

ACS Style

Miltiadis Lytras; Basim Alsaywid; Abdulrahman Housawi. Training, Education, and Research in COVID-19 Times: Innovative Methodological Approaches, Best Practices, and Case Studies. Sustainability 2021, 13, 9650 .

AMA Style

Miltiadis Lytras, Basim Alsaywid, Abdulrahman Housawi. Training, Education, and Research in COVID-19 Times: Innovative Methodological Approaches, Best Practices, and Case Studies. Sustainability. 2021; 13 (17):9650.

Chicago/Turabian Style

Miltiadis Lytras; Basim Alsaywid; Abdulrahman Housawi. 2021. "Training, Education, and Research in COVID-19 Times: Innovative Methodological Approaches, Best Practices, and Case Studies." Sustainability 13, no. 17: 9650.

Journal article
Published: 11 August 2021 in Diagnostics
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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.

ACS Style

José-Luis Solorio-Ramírez; Magdalena Saldana-Perez; Miltiadis D. Lytras; Marco-Antonio Moreno-Ibarra; Cornelio Yáñez-Márquez. Brain Hemorrhage Classification in CT Scan Images Using Minimalist Machine Learning. Diagnostics 2021, 11, 1449 .

AMA Style

José-Luis Solorio-Ramírez, Magdalena Saldana-Perez, Miltiadis D. Lytras, Marco-Antonio Moreno-Ibarra, Cornelio Yáñez-Márquez. Brain Hemorrhage Classification in CT Scan Images Using Minimalist Machine Learning. Diagnostics. 2021; 11 (8):1449.

Chicago/Turabian Style

José-Luis Solorio-Ramírez; Magdalena Saldana-Perez; Miltiadis D. Lytras; Marco-Antonio Moreno-Ibarra; Cornelio Yáñez-Márquez. 2021. "Brain Hemorrhage Classification in CT Scan Images Using Minimalist Machine Learning." Diagnostics 11, no. 8: 1449.

Journal article
Published: 31 July 2021 in Mathematics
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Machine learning in the medical area has become a very important requirement. The healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are important to provide tools that can be useful to the health professional for this purpose. However, questions arise: which classifier to use? What metrics are appropriate to measure the performance of the classifier? How to determine a good distribution of the data so that the classifier does not bias the medical patterns to be classified in a particular class? Then most important question: does a classifier perform well for a particular disease? This paper will present some answers to the questions mentioned above, making use of classification algorithms widely used in machine learning research with datasets relating to medical illnesses under the supervised learning scheme. In addition to state-of-the-art algorithms in pattern classification, we introduce a novelty: the use of meta-learning to determine, a priori, which classifier would be the ideal for a specific dataset. The results obtained show numerically and statistically that there are reliable classifiers to suggest medical diagnoses. In addition, we provide some insights about the expected performance of classifiers for such a task.

ACS Style

Marco-Antonio Moreno-Ibarra; Yenny Villuendas-Rey; Miltiadis Lytras; Cornelio Yáñez-Márquez; Julio-César Salgado-Ramírez. Classification of Diseases Using Machine Learning Algorithms: A Comparative Study. Mathematics 2021, 9, 1817 .

AMA Style

Marco-Antonio Moreno-Ibarra, Yenny Villuendas-Rey, Miltiadis Lytras, Cornelio Yáñez-Márquez, Julio-César Salgado-Ramírez. Classification of Diseases Using Machine Learning Algorithms: A Comparative Study. Mathematics. 2021; 9 (15):1817.

Chicago/Turabian Style

Marco-Antonio Moreno-Ibarra; Yenny Villuendas-Rey; Miltiadis Lytras; Cornelio Yáñez-Márquez; Julio-César Salgado-Ramírez. 2021. "Classification of Diseases Using Machine Learning Algorithms: A Comparative Study." Mathematics 9, no. 15: 1817.

Journal article
Published: 06 July 2021 in Sustainability
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The evolution in knowledge management and crowdsourcing research provides new data-processing capabilities. The availability of both structured and unstructured open data formats offers unforeseen opportunities for analytics processing and advanced decision-making. However, social sciences research is facing advanced, complicated social challenges and problems. The focus of this study is to analyze the contribution of crowdsourcing techniques to the promotion of advanced social sciences research, exploiting open data available from the geographical positioning system (GPS) to analyze human behavior. In our study, we present the conceptual design of a device that, with the help of a global positioning system-data collection device (GPS-DCD), associates behavioral aspects of human life with place. The main contribution of this study is to integrate research in computer science and information systems with that in social science. The prototype system summarized in this work, proves the capacity of crowdsourcing and big data research to facilitate aggregation of microcontent related to human behavior toward improved quality of life and well-being in modern smart cities. Various ethical issues are also discussed to promote the scientific debate on this matter. Our study shows the capacity of emerging technologies to deal with social challenges. This kind of research will gain increased momentum in the future due to the availability of big data and new business models for social platforms.

ACS Style

Wadee Alhalabi; Miltiadis Lytras; Nada Aljohani. Crowdsourcing Research for Social Insights into Smart Cities Applications and Services. Sustainability 2021, 13, 7531 .

AMA Style

Wadee Alhalabi, Miltiadis Lytras, Nada Aljohani. Crowdsourcing Research for Social Insights into Smart Cities Applications and Services. Sustainability. 2021; 13 (14):7531.

Chicago/Turabian Style

Wadee Alhalabi; Miltiadis Lytras; Nada Aljohani. 2021. "Crowdsourcing Research for Social Insights into Smart Cities Applications and Services." Sustainability 13, no. 14: 7531.

Public health
Published: 07 June 2021 in Frontiers in Public Health
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Background: End-stage renal disease, as one of the most serious and major health problems, does not have many treatment options available. One of the best treatment modalities used to cure this debilitating disease is kidney transplantation. However, with the continuous increase in number of patients diagnosed with it, there is not enough supply of the organ. The aim of our study is to assess knowledge about, attitude toward, and willingness to donate kidney among health science students at King Saud bin Abdulaziz University in comparison to the general population in Jeddah and to investigate the factors that play a role on their willingness. Methods: This is an observational, analytical, cross-sectional study design conducted in 2019. Two target populations were included: King Saud bin Abdulaziz University for Health Sciences students and the general population in Jeddah. Data were collected via a self-administered, close-ended, structured, and previously validated questionnaire that contained 39 items divided into four sections. SPSS program version 22 was used in data analysis. Results: Out of 685 surveyed participants, 179 (26.1%) were willing to donate their kidney, with students showing a higher rate of willingness (N = 101; 32.3%) than the general population (N = 78; 21%). However, only 46 (6.7%) out of the total population hold an organ donor card. In bivariate analysis, it was found that knowledge significantly associated with a higher rate of willingness among the student population than the general population, while positive beliefs were associated with increased willingness in the general population than students. Positive attitude appeared to play a role in higher willingness among the general population and student population. Conclusion: There is a low perception of awareness regarding kidney donation in both populations of this study. The willingness rate of health science students at King Saud bin Abdulaziz University and the general population was low when compared with other studies conducted internationally.

ACS Style

Raghad Sharaan; Sara Alsulami; Raneem Arab; Ghida Alzeair; Nadia Elamin; Basim Alsaywid; Miltiadis Lytras. Knowledge, Attitude, and Willingness Toward Kidney Donation Among Health Sciences Students at King Saud Bin Abdulaziz University. Frontiers in Public Health 2021, 9, 1 .

AMA Style

Raghad Sharaan, Sara Alsulami, Raneem Arab, Ghida Alzeair, Nadia Elamin, Basim Alsaywid, Miltiadis Lytras. Knowledge, Attitude, and Willingness Toward Kidney Donation Among Health Sciences Students at King Saud Bin Abdulaziz University. Frontiers in Public Health. 2021; 9 ():1.

Chicago/Turabian Style

Raghad Sharaan; Sara Alsulami; Raneem Arab; Ghida Alzeair; Nadia Elamin; Basim Alsaywid; Miltiadis Lytras. 2021. "Knowledge, Attitude, and Willingness Toward Kidney Donation Among Health Sciences Students at King Saud Bin Abdulaziz University." Frontiers in Public Health 9, no. : 1.

Editorial
Published: 17 April 2021 in Journal of Ambient Intelligence and Humanized Computing
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ACS Style

Akila Sarirete; Zain Balfagih; Tayeb Brahimi; Miltiadis D. Lytras; Anna Visvizi. Artificial intelligence and machine learning research: towards digital transformation at a global scale. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -3.

AMA Style

Akila Sarirete, Zain Balfagih, Tayeb Brahimi, Miltiadis D. Lytras, Anna Visvizi. Artificial intelligence and machine learning research: towards digital transformation at a global scale. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-3.

Chicago/Turabian Style

Akila Sarirete; Zain Balfagih; Tayeb Brahimi; Miltiadis D. Lytras; Anna Visvizi. 2021. "Artificial intelligence and machine learning research: towards digital transformation at a global scale." Journal of Ambient Intelligence and Humanized Computing , no. : 1-3.

Editorial
Published: 27 March 2021 in Journal of Ambient Intelligence and Humanized Computing
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ACS Style

Anna Visvizi; Miltiadis D. Lytras; Naif Aljohani. Big data research for politics: human centric big data research for policy making, politics, governance and democracy. Journal of Ambient Intelligence and Humanized Computing 2021, 12, 4303 -4304.

AMA Style

Anna Visvizi, Miltiadis D. Lytras, Naif Aljohani. Big data research for politics: human centric big data research for policy making, politics, governance and democracy. Journal of Ambient Intelligence and Humanized Computing. 2021; 12 (4):4303-4304.

Chicago/Turabian Style

Anna Visvizi; Miltiadis D. Lytras; Naif Aljohani. 2021. "Big data research for politics: human centric big data research for policy making, politics, governance and democracy." Journal of Ambient Intelligence and Humanized Computing 12, no. 4: 4303-4304.

Editorial
Published: 24 March 2021 in Sustainability
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Artificial intelligence (AI) and cognitive computing (CC) are subject of increased attention of both academia and industry today. The understanding is that AI- and CC-enhanced methods and techniques create a variety of opportunities relating to improving basic and advanced business functions, including production processes, logistics, financial management and others. AI-enhanced tools and methods tend to offer more precise results in the field of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this special issue was to bring together diverse communities of scholars and engage in a broad discussion on the role of AI and CC in today’s society, including the process of policy-making.

ACS Style

Miltiadis Lytras; Anna Visvizi. Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making. Sustainability 2021, 13, 3598 .

AMA Style

Miltiadis Lytras, Anna Visvizi. Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making. Sustainability. 2021; 13 (7):3598.

Chicago/Turabian Style

Miltiadis Lytras; Anna Visvizi. 2021. "Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making." Sustainability 13, no. 7: 3598.

Preprint content
Published: 04 March 2021
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Background: Under the urgent circumstances of 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.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 Saudi Commission of Health specialty. Main objective was to evaluate the quality of e-learning methods, provided by medical universities.Results: The study found that the frequency of digital education use increased by approximately 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 during adaptation process, according to our survey: lectures and training sessions were not conducted as per curriculum (56.33%); both students and instructors’ academic behavior and attitude changed (48.33%); engagement, satisfaction and motivation in class was rated low (5.93, 6.33 & 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. Conclusions: 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.

ACS Style

Basim Alsaywid; Miltiadis D. Lytras; Maha Abuzenada; Hara Lytra; Abdulrahman Housawi; Wesam Abuznadah; Sami A. Alhaidar; Areti Apostolaki; Lama Sultan; Hala Badawoud. Effectiveness and Preparedness of Institutions’ E-learning Method During COVID-19 Pandemic for residents’ medical training in Saudi Arabia: A pilot study. 2021, 1 .

AMA Style

Basim Alsaywid, Miltiadis D. Lytras, Maha Abuzenada, Hara Lytra, Abdulrahman Housawi, Wesam Abuznadah, Sami A. Alhaidar, Areti Apostolaki, Lama Sultan, Hala Badawoud. Effectiveness and Preparedness of Institutions’ E-learning Method During COVID-19 Pandemic for residents’ medical training in Saudi Arabia: A pilot study. . 2021; ():1.

Chicago/Turabian Style

Basim Alsaywid; Miltiadis D. Lytras; Maha Abuzenada; Hara Lytra; Abdulrahman Housawi; Wesam Abuznadah; Sami A. Alhaidar; Areti Apostolaki; Lama Sultan; Hala Badawoud. 2021. "Effectiveness and Preparedness of Institutions’ E-learning Method During COVID-19 Pandemic for residents’ medical training in Saudi Arabia: A pilot study." , no. : 1.

Journal article
Published: 17 February 2021 in IEEE Access
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The future generation of smart cities is a very timely and attractive topic for the research community worldwide. The evolution of this generation is oriented toward providing innovative services and policymaking to guarantee the well-being of citizens. Research on smart cities is maturing and the question of securing the well-being of cities’ inhabitants is increasingly attracting the attention of researchers, practitioners, and policymakers. Considering the challenges and opportunities cities/urban spaces generate, today the imperative is to examine how targeted research and cutting-edge innovation can be effectively communicated to all stakeholders. Thus, synergies emerging at the researchinnovation- policymaking nexus can be exploited and city dwellers’ well-being can be enhanced. Pervasive computing, big data analytics, crowdsourcing, and other timely technologies, including user behavior, brand popularity, recommender systems, and social media analytics, bear the promise and potential that viable solutions to key problems and challenges specific to the future generation of smart cities will be found. The objective of this Special Section in IEEE ACCESS is to examine this promise and potential from a variety of complementary interdisciplinary perspectives, including computing/ ICT, political economy, public policy, innovation, and entrepreneurship.

ACS Style

Miltiadis D. Lytras; Anna Visvizi; Akila Sarirete; Miguel Torres-Ruiz; Tugrul U. Daim. IEEE Access Special Section Editorial: Future Generation Smart Cities Research—Part II: Services, Applications, Case Studies, and Policymaking Considerations For Well-Being. IEEE Access 2021, 9, 27298 -27303.

AMA Style

Miltiadis D. Lytras, Anna Visvizi, Akila Sarirete, Miguel Torres-Ruiz, Tugrul U. Daim. IEEE Access Special Section Editorial: Future Generation Smart Cities Research—Part II: Services, Applications, Case Studies, and Policymaking Considerations For Well-Being. IEEE Access. 2021; 9 ():27298-27303.

Chicago/Turabian Style

Miltiadis D. Lytras; Anna Visvizi; Akila Sarirete; Miguel Torres-Ruiz; Tugrul U. Daim. 2021. "IEEE Access Special Section Editorial: Future Generation Smart Cities Research—Part II: Services, Applications, Case Studies, and Policymaking Considerations For Well-Being." IEEE Access 9, no. : 27298-27303.

Journal article
Published: 16 December 2020 in Sustainability
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In late December of 2019, the outbreak of coronavirus disease (COVID-19) was first reported in the city of Wuhan, the capital of Hubei province in China, and was declared a pandemic by the World Health Organization in March 2020. Globally, as of 8 July 2020, there have been 11,669,259 confirmed cases of COVID-19, including 539,906 deaths. In Saudi Arabia, the confirmed cases have already reached 223,327, with 161,096 patients confirmed to have recovered, and 2100 deaths. This study aims to determine the effect of the COVID-19 pandemic on the training programs of the Saudi Commission for Health Specialties (SCFHS) and assess trainees’ mental health status (i.e., anxiety and depression). Trainee evaluations on training programs were also sought in order to obtain insights for strategic planning necessary for curricular modifications or improvements to address the clinical learning needs of trainees during this pandemic. The main contribution of our work is an investigation of the incidence of depression and anxiety regarding COVID-19 within the community of residents and fellows. Furthermore, we elaborate on key responsive actions towards the enhancement of the mental health of trainees. Last but not least, we propose the Saudi Commission for Health Specialties (SCFHS) Model for Residents’ Mental Health Enhancement during the COVID-19 Pandemic, which consists of five integrative value layers for medical education and training, namely: the knowledge creation process and innovation; technological capabilities for personalized medicine and patient-centric healthcare with a social impact; innovative applications of technology-enhanced learning and web-based active learning approaches for medical training and education; residents’ wellbeing and the impact of COVID-19 in strategic layers. In our future work, we intend to enhance the proposed framework with studies on trainee satisfaction and the efficiency of different technology-enhanced learning platforms for medical education.

ACS Style

Basim Alsaywid; Abdulrahman Housawi; Miltiadis Lytras; Huda Halabi; Maha Abuznadah; Sami A. Alhaidar; Wesam Abuznadah. Residents’ Training in COVID-19 Pandemic Times: An Integrated Survey of Educational Process, Institutional Support, Anxiety and Depression by the Saudi Commission for Health Specialties (SCFHS). Sustainability 2020, 12, 10530 .

AMA Style

Basim Alsaywid, Abdulrahman Housawi, Miltiadis Lytras, Huda Halabi, Maha Abuznadah, Sami A. Alhaidar, Wesam Abuznadah. Residents’ Training in COVID-19 Pandemic Times: An Integrated Survey of Educational Process, Institutional Support, Anxiety and Depression by the Saudi Commission for Health Specialties (SCFHS). Sustainability. 2020; 12 (24):10530.

Chicago/Turabian Style

Basim Alsaywid; Abdulrahman Housawi; Miltiadis Lytras; Huda Halabi; Maha Abuznadah; Sami A. Alhaidar; Wesam Abuznadah. 2020. "Residents’ Training in COVID-19 Pandemic Times: An Integrated Survey of Educational Process, Institutional Support, Anxiety and Depression by the Saudi Commission for Health Specialties (SCFHS)." Sustainability 12, no. 24: 10530.

Journal article
Published: 06 December 2020 in Sustainability
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The latest developments in Sustainable Health focus on the provision of high quality medical training to health specialists, with a special focus on human factors. The need to promote effective Training Programs also reflects the job satisfaction needs of trainees. The objective of this study is to evaluate the trainees’ satisfaction with the quality of Training Programs and assess the degree of achievement based on the defined parameters to provide baseline data based on which strategies for improvement can be formulated. Our study was conducted in Saudi Arabia and our targeted population was residents in medical programs supervised by the Saudi Commission for the Health Specialties (SCFHS). The trainees’ response rate to the online survey was 27% (3696/13,688) and the key aspects of job satisfaction investigated include: Satisfaction with Academic Activities in the Center; Satisfaction with the Residents and Colleagues in the Center; Satisfaction with the Administrative Components in the Center; Satisfaction with the Training Programs; Satisfaction with the Specialty; Satisfaction with the Training Center; Satisfaction with the SCFHS. The main contribution of our work is a benchmark model for job satisfaction that can be used as a managerial tool for the enhancement of medical education with reference to the satisfaction of trainees. We analyze the key aspects and components of training satisfaction and we introduce our progressive model for Trainees’ Satisfaction in Medical Training. In future work, we intend to enhance the proposed framework with a set of key performance indicators as well as with a focused cause and effect focused survey on factors related to the key benchmark of this study.

ACS Style

Abdulrahman Housawi; Amal Al Amoudi; Basim Alsaywid; Miltiadis Lytras; Yara H. Bin Μoreba; Wesam Abuznadah; Fadi Munshi; Sami Al Haider; Abrar W. Tolah. A Progressive Model for Quality Benchmarks of Trainees’ Satisfaction in Medical Education: Towards Strategic Enhancement of Residency Training Programs at Saudi Commission for Health Specialties (SCFHS). Sustainability 2020, 12, 10186 .

AMA Style

Abdulrahman Housawi, Amal Al Amoudi, Basim Alsaywid, Miltiadis Lytras, Yara H. Bin Μoreba, Wesam Abuznadah, Fadi Munshi, Sami Al Haider, Abrar W. Tolah. A Progressive Model for Quality Benchmarks of Trainees’ Satisfaction in Medical Education: Towards Strategic Enhancement of Residency Training Programs at Saudi Commission for Health Specialties (SCFHS). Sustainability. 2020; 12 (23):10186.

Chicago/Turabian Style

Abdulrahman Housawi; Amal Al Amoudi; Basim Alsaywid; Miltiadis Lytras; Yara H. Bin Μoreba; Wesam Abuznadah; Fadi Munshi; Sami Al Haider; Abrar W. Tolah. 2020. "A Progressive Model for Quality Benchmarks of Trainees’ Satisfaction in Medical Education: Towards Strategic Enhancement of Residency Training Programs at Saudi Commission for Health Specialties (SCFHS)." Sustainability 12, no. 23: 10186.

Editorial
Published: 04 December 2020 in Sensors
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One of the key smart city visions is to bring smarter transport networks, specifically intelligent/smart transportation

ACS Style

Miltiadis D. Lytras; Kwok Tai Chui; Ryan Wen Liu. Moving Towards Intelligent Transportation via Artificial Intelligence and Internet-of-Things. Sensors 2020, 20, 6945 .

AMA Style

Miltiadis D. Lytras, Kwok Tai Chui, Ryan Wen Liu. Moving Towards Intelligent Transportation via Artificial Intelligence and Internet-of-Things. Sensors. 2020; 20 (23):6945.

Chicago/Turabian Style

Miltiadis D. Lytras; Kwok Tai Chui; Ryan Wen Liu. 2020. "Moving Towards Intelligent Transportation via Artificial Intelligence and Internet-of-Things." Sensors 20, no. 23: 6945.

Journal article
Published: 12 October 2020 in Applied Sciences
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Massive heterogeneous big data residing at different sites with various types and formats need to be integrated into a single unified view before starting data mining processes. Furthermore, in most of applications and research, a single big data source is not enough to complete the analysis and achieve goals. Unfortunately, there is no general or standardized integration process; the nature of an integration process depends on the data type, domain, and integration purpose. Based on these parameters, we proposed, implemented, and tested a big data integration framework that integrates big data in the biology domain, based on the domain ontology and using distributed processing. The integration resulted in the same result as that obtained from the local integration. The results are equivalent in terms of the ontology size before the integration; in the number of added items, skipped items, and overlapped items; in the ontology size after the integration; and in the number of edges, vertices, and roots. The results also do not violate any logical consistency rules, passing all the logical consistency tests, such as Jena Ontology API, HermiT, and Pellet reasoners. The integration result is a new big data source that combines big data from several critical sources in the biology domain and transforms it into one unified format to help researchers and specialists use it for further research and analysis.

ACS Style

Ameera Almasoud; Hend Al-Khalifa; Abdulmalik Al-Salman; Miltiadis Lytras. A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing. Applied Sciences 2020, 10, 7092 .

AMA Style

Ameera Almasoud, Hend Al-Khalifa, Abdulmalik Al-Salman, Miltiadis Lytras. A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing. Applied Sciences. 2020; 10 (20):7092.

Chicago/Turabian Style

Ameera Almasoud; Hend Al-Khalifa; Abdulmalik Al-Salman; Miltiadis Lytras. 2020. "A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing." Applied Sciences 10, no. 20: 7092.

Journal article
Published: 29 September 2020 in Sustainability
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The Kingdom of Saudi Arabia is undergoing a major transformation in response to a revolutionary vision of 2030, given that healthcare reform is one of the top priorities. With the objective of improving healthcare and allied professional performance in the Kingdom to meet the international standards, the Saudi Commission for Health Specialties (SCFHS) has recently developed a strategic plan that focuses on expanding training programs’ capacity to align with the increasing demand for the country’s healthcare workforce, providing comprehensive quality assurance and control to ensure training programs uphold high quality standards, and providing advanced training programs benchmarked against international standards. In this research paper, we describe our attempt for developing a general framework for key performance indicators (KPIs) and the related metrics, with the aim of contributing to developing new strategies for better medical training compatible with the future. We present the results of a survey conducted in the Kingdom of Saudi Arabia (KSA), for the enhancement of quality of postgraduate medical training. The recent developments in the field of learning analytics present an opportunity for utilizing big data and artificial intelligence in the design and implementation of socio-technical systems with significant potential social impact. We summarize the key aspects of the Training Quality Assurance Initiative and suggest a new approach for designing a new data and services ecosystem for personalized health professionals training in the KSA. The study also contributes to the theoretical knowledge on the integration of sustainability and medical training and education by proposing a framework that can enhance future initiatives from various health organizations.

ACS Style

Abdulrahman Housawi; Amal Al Amoudi; Basim Alsaywid; Miltiadis Lytras; Yara Bin Μoreba; Wesam Abuznadah; Sami Alhaidar. Evaluation of Key Performance Indicators (KPIs) for Sustainable Postgraduate Medical Training: An Opportunity for Implementing an Innovative Approach to Advance the Quality of Training Programs at the Saudi Commission for Health Specialties (SCFHS). Sustainability 2020, 12, 8030 .

AMA Style

Abdulrahman Housawi, Amal Al Amoudi, Basim Alsaywid, Miltiadis Lytras, Yara Bin Μoreba, Wesam Abuznadah, Sami Alhaidar. Evaluation of Key Performance Indicators (KPIs) for Sustainable Postgraduate Medical Training: An Opportunity for Implementing an Innovative Approach to Advance the Quality of Training Programs at the Saudi Commission for Health Specialties (SCFHS). Sustainability. 2020; 12 (19):8030.

Chicago/Turabian Style

Abdulrahman Housawi; Amal Al Amoudi; Basim Alsaywid; Miltiadis Lytras; Yara Bin Μoreba; Wesam Abuznadah; Sami Alhaidar. 2020. "Evaluation of Key Performance Indicators (KPIs) for Sustainable Postgraduate Medical Training: An Opportunity for Implementing an Innovative Approach to Advance the Quality of Training Programs at the Saudi Commission for Health Specialties (SCFHS)." Sustainability 12, no. 19: 8030.

Journal article
Published: 01 July 2020 in Applied Sciences
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The world has witnessed the success of artificial intelligence deployment for smart healthcare applications. Various studies have suggested that the prevalence of voice disorders in the general population is greater than 10%. An automatic diagnosis for voice disorders via machine learning algorithms is desired to reduce the cost and time needed for examination by doctors and speech‑language pathologists. In this paper, a conditional generative adversarial network (CGAN) and improved fuzzy c‑means clustering (IFCM) algorithm called CGAN‑IFCM is proposed for the multi‑class voice disorder detection of three common types of voice disorders. Existing benchmark datasets for voice disorders, the Saarbruecken Voice Database (SVD) and the Voice ICar fEDerico II Database (VOICED), use imbalanced classes. A generative adversarial network offers synthetic data to reduce bias in the detection model. Improved fuzzy c‑means clustering considers the relationship between adjacent data points in the fuzzy membership function. To explain the necessity of CGAN and IFCM, a comparison is made between the algorithm with CGAN and that without CGAN. Moreover, the performance is compared between IFCM and traditional fuzzy c‑means clustering. Lastly, the proposed CGAN‑IFCM outperforms existing models in its true negative rate and true positive rate by 9.9–12.9% and 9.1–44.8%, respectively.

ACS Style

Kwok Tai Chui; Miltiadis D. Lytras; Pandian Vasant. Combined Generative Adversarial Network and Fuzzy C‑Means Clustering for Multi‑Class Voice Disorder Detection with an Imbalanced Dataset. Applied Sciences 2020, 10, 4571 .

AMA Style

Kwok Tai Chui, Miltiadis D. Lytras, Pandian Vasant. Combined Generative Adversarial Network and Fuzzy C‑Means Clustering for Multi‑Class Voice Disorder Detection with an Imbalanced Dataset. Applied Sciences. 2020; 10 (13):4571.

Chicago/Turabian Style

Kwok Tai Chui; Miltiadis D. Lytras; Pandian Vasant. 2020. "Combined Generative Adversarial Network and Fuzzy C‑Means Clustering for Multi‑Class Voice Disorder Detection with an Imbalanced Dataset." Applied Sciences 10, no. 13: 4571.

Journal article
Published: 18 June 2020 in International Journal of Information Management
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In the current socio-economic environment, to face challenges such as the emergence of new technologies, globalisation and increasing demands from their clients it is inevitable that enterprises will collaborate with others and progressively shift their boundaries. In this context, interoperability has become a prerequisite in the jigsaw of such collaboration. By definition, it is entities’ ability to work together as an organisation. This ability spans a wide range of aspects, embracing both technical and business issues. Over the past decade, both the concept and the context of interoperability have been extended from a largely IT-focused domain to a business-focused domain and the evaluation of interoperability has become a rising concern. An increasing number of studies have concentrated on not just digital but business aspects of human behaviour in the social environment. In general, the wider application domain is the assessment of the interoperability of information systems and processes in any organisation (especially medium and large) that needs multiple processes to interact effectively. To deal with such concerns and pave the way to achievement of more effective collaborative goals in business, the concept of interoperability has been adopted to measure the efficiency and productivity of information systems’ integration. More than twenty approaches have so far been adopted to evaluate this interoperability, however most are unable to assess it at the higher levels, such as at the pragmatic, process and social levels. Hence, we have conducted a three-phase study. Phase 1 reviewed existing interoperability evaluation approaches. To prove the concept, phase 2 proposed the concept of semiotic interoperability and its application to healthcare information systems. This article reports on the third phase of the study, a proposed framework with a group of metrics to measure interoperability from a new perspective – a semiotics perspective. The framework is named the Semiotic Interoperability Evaluation Framework (the SIEF) and has the ability to analyse, measure and assess the interoperability among business processes. The metrics derive from a feasibility study to investigate several interoperability barriers at a hospital. Next, the SIEF was applied in a case study and a detailed interoperability evaluation was conducted.

ACS Style

Leo Liu; Weizi Li; Naif R. Aljohani; Miltiadis D. Lytras; Saeed-Ul Hassan; Raheel Nawaz. A framework to evaluate the interoperability of information systems – Measuring the maturity of the business process alignment. International Journal of Information Management 2020, 54, 102153 .

AMA Style

Leo Liu, Weizi Li, Naif R. Aljohani, Miltiadis D. Lytras, Saeed-Ul Hassan, Raheel Nawaz. A framework to evaluate the interoperability of information systems – Measuring the maturity of the business process alignment. International Journal of Information Management. 2020; 54 ():102153.

Chicago/Turabian Style

Leo Liu; Weizi Li; Naif R. Aljohani; Miltiadis D. Lytras; Saeed-Ul Hassan; Raheel Nawaz. 2020. "A framework to evaluate the interoperability of information systems – Measuring the maturity of the business process alignment." International Journal of Information Management 54, no. : 102153.

Editorial
Published: 08 June 2020 in Soft Computing
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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.

ACS Style

Miltiadis D. Lytras; Anna Visvizi; Jari Jussila. Social media mining for smart cities and smart villages research. Soft Computing 2020, 24, 10983 -10987.

AMA Style

Miltiadis D. Lytras, Anna Visvizi, Jari Jussila. Social media mining for smart cities and smart villages research. Soft Computing. 2020; 24 (15):10983-10987.

Chicago/Turabian Style

Miltiadis D. Lytras; Anna Visvizi; Jari Jussila. 2020. "Social media mining for smart cities and smart villages research." Soft Computing 24, no. 15: 10983-10987.

Journal article
Published: 08 June 2020 in International Journal of Information Management
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Smart-cities research has established itself as one of the most dynamic fields in research today, accommodating scholars from diverse disciplines, including engineering and computer science as well as social sciences. Even if only tacitly, the resultant debate increasingly oscillates around how the effective use of information and communication technology (ICT) might render cities and urban space better places. This article responds to this imperative by suggesting how to capture users’ views and perceptions of smart city services and applications and in this way enrich the decision- and policy-making processes. It is argued that by developing appropriate scales these otherwise subjective views and perceptions may be objectivized and hence made of great use to managers and policymakers. Accordingly, in this research, a process of scale development is conducted in four phases of both inductive and deductive methods. Following initial rounds of focus groups and assessment by experts, an international survey was conducted with 295 participants from Europe, Asia, Latin America, the Arab Peninsula, and other regions. The data were analyzed using IBM SPSS 24 and AMOS 20 tools. The study proposes a 20-item scale in five distinct dimensions: Technology anxiety; Work–life interface; Engagement orientation; Support orientation; and Quality of life. The significant theoretical and managerial implications are discussed to demonstrate how to manage information for the benefit of all stakeholders involved in the making of a smart city.

ACS Style

Miltiadis D. Lytras; Anna Visvizi; Prasanta Kr Chopdar; Akila Sarirete; Wadee Alhalabi. Information Management in Smart Cities: Turning end users’ views into multi-item scale development, validation, and policy-making recommendations. International Journal of Information Management 2020, 56, 102146 .

AMA Style

Miltiadis D. Lytras, Anna Visvizi, Prasanta Kr Chopdar, Akila Sarirete, Wadee Alhalabi. Information Management in Smart Cities: Turning end users’ views into multi-item scale development, validation, and policy-making recommendations. International Journal of Information Management. 2020; 56 ():102146.

Chicago/Turabian Style

Miltiadis D. Lytras; Anna Visvizi; Prasanta Kr Chopdar; Akila Sarirete; Wadee Alhalabi. 2020. "Information Management in Smart Cities: Turning end users’ views into multi-item scale development, validation, and policy-making recommendations." International Journal of Information Management 56, no. : 102146.

Journal article
Published: 26 May 2020 in Sustainability
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The recent pandemic has raised significant challenges worldwide. In higher education, the necessity to adopt efficient strategies to sustain education during the crisis is mobilizing diverse, complementary, and integrative action in response. In this research article, we rise to the challenge of designing and implementing a transparent strategy for social media awareness at King Abdulaziz University (KAU). We introduce a framework for social media impact, termed the KAU Pandemic Framework. This includes the factors with the most important role in enhancing the deployment of social media in crisis in order to minimize the negative impact on education’s sustainability. We used a mixed-methods approach, integrating quantitative statistical analyses of social media data and online surveys and qualitative interviews in such a way as to construct a comprehensive framework. The results show that a methodological framework can be justified and that Twitter contributes significantly to six areas: administrative resilience; education sustainability; community responsibility; positive sentiment; community bonds; and delivery of promised value. The components of our proposed methodological framework integrate five pillars of the strategic adoption of social media: social media governance; social media resilience; social media utilization; decision-making capability; and institutional strategy. Finally, we show that the KAU Pandemic Framework can be used as strategic decision-making tool for the analysis of the gaps and inefficiencies in any social media plan that is deployed and the management challenges arising from the pandemic.

ACS Style

Abdulrahman Obaid Ai-Youbi; Abdulmonem Al-Hayani; Hisham J. Bardesi; Mohammed Basheri; Miltiadis D. Lytras; Naif Radi Aljohani. The King Abdulaziz University (KAU) Pandemic Framework: A Methodological Approach to Leverage Social Media for the Sustainable Management of Higher Education in Crisis. Sustainability 2020, 12, 4367 .

AMA Style

Abdulrahman Obaid Ai-Youbi, Abdulmonem Al-Hayani, Hisham J. Bardesi, Mohammed Basheri, Miltiadis D. Lytras, Naif Radi Aljohani. The King Abdulaziz University (KAU) Pandemic Framework: A Methodological Approach to Leverage Social Media for the Sustainable Management of Higher Education in Crisis. Sustainability. 2020; 12 (11):4367.

Chicago/Turabian Style

Abdulrahman Obaid Ai-Youbi; Abdulmonem Al-Hayani; Hisham J. Bardesi; Mohammed Basheri; Miltiadis D. Lytras; Naif Radi Aljohani. 2020. "The King Abdulaziz University (KAU) Pandemic Framework: A Methodological Approach to Leverage Social Media for the Sustainable Management of Higher Education in Crisis." Sustainability 12, no. 11: 4367.