RSS 1.0Faculty Research and Publications
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Guest editorial: Active and transformative learning in higher education in times of artificial intelligence and ChatGPT1. Introduction to the theme In the green and digital transition, new skills are required for the new labor markets. National education and training systems need to adapt to the new demands of companies and governments, providing workers, students and citizens with the valuable digital skills needed today. And at the same time, this digital transformation must ensure the transition toward a greener and more inclusive economy and society (Alkhaldi et al., 2024, Alsaywid et al., 2023a, 2023b, Ordóñez de Pablos et al., 2020, 2022, 2023, 2024). In the case of the European Union, the Digital Education Action Plan (2021–2027) “aims to support the adaptation of the education and training systems of Member States to the digital age.” It contributes to “the goals of the European Skills Agenda, the European Social Pillar Action Plan and the 2030 Digital Compass: the European way for the Digital Decade” (European Commission, 2024a, 2024b). This plan has two key priorities: “Priority 1: Fostering the development of a high-performing digital education ecosystem” and “Priority 2: Enhancing digital skills and competences for the digital transformation”. In the case of Priority 2, Action 8 focuses on AI and data-related skills (European Commission, 2024a, 2024b).
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Bibliometric analysis of Arabic Rhetoric in the translation and transcreation of literary textsIn recent years, ‘transcreation’ has emerged in translation, integrating linguistic, cultural, and creative reinterpretation. With the rise of bibliometric analysis tools, mapping and assessing scientific activities across various fields have become possible, yet few studies apply these methods to translation. This study explores the developments and trends in transcreation within literary texts using quantitative and qualitative bibliometric analysis, particularly through VOSviewer. By analyzing 558 documents from the Scopus database focused on Arabic, rhetoric, transcreation, and translation, the study identifies the United States, United Kingdom, Jordan, Saudi Arabia, and Spain as the most active countries. The USA leads in total citations, followed by Saudi Arabia and the UK. King Saud University is highlighted as a leading institution. These findings provide a roadmap for future research and policy in applying Arabic rhetoric to the translation and transcreation of literary texts
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ViTDroid: Vision Transformers for Efficient, Explainable Attention to Malicious Behavior in Android BinariesSmartphones are intricately connected to the modern society. The two widely used mobile phone operating systems, iOS and Android, profoundly affect the lives of millions of people. Android presently holds a market share of close to 71% among these two. As a result, if personal information is not securely protected, it is at tremendous risk. On the other hand, mobile malware has seen a year-on-year increase of more than 42% globally in 2022 mid-year. Any group of human professionals would have a very tough time detecting and removing all of this malware. For this reason, deep learning in particular has been used recently to overcome this problem. Deep learning models, however, were primarily created for picture analysis. Despite the fact that these models have shown promising findings in the field of vision, it has been challenging to fully comprehend what the characteristics recovered by deep learning models are in the area of malware. Furthermore, the actual potential of deep learning for malware analysis has not yet been fully realized due to the translation invariance trait of well-known models based on CNN. In this paper, we present ViTDroid, a novel model based on vision transformers for the deep learning-based analysis of opcode sequences of Android malware samples from large real-world datasets. We have been able to achieve a false positive rate of 0.0019 as compared to the previous best of 0.0021. However, this incremental improvement is not the major contribution of our work. Our model aims to make explainable predictions, i.e., it not only performs the classification of malware with high accuracy, but it also provides insights into the reasons for this classification. The model is able to pinpoint the malicious behavior-causing instructions in the malware samples. This means that our model can actually aid in the field of malware analysis itself by providing insights to human experts, thus leading to further improvements in this field.
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Innovation, leadership, and education: How Effat University is paving the way for Vision 2030Higher education institutions like Effat University play a critical role in realizing the ambitious goals of Saudi Arabia’s Vision 2030, which emphasizes adaptability, agility, and sustainability in the educational sector. With the higher education landscape undergoing rapid transformation globally, institutions are compelled to rethink curricula and align more closely with the changing needs of students, industry, and the government. Effat University, situated at the confluence of Saudi Arabia’s Vision 2030 and the global Sustainable Development Goals (SDGs), shows adaptability, agility, and sustainability in the higher education sector. At the heart of Effat’s mission lies the IQRA core values, anchoring its academic and administrative endeavors and fostering graduates who are holistic, ethically grounded, and attuned to global challenges. This book chapter investigates Effat University’s distinctive approach, fortified by innovative initiatives, transformative leadership, expansive research activities, and the unwavering IQRA principles, as it positions itself to further the goals of Vision 2030 and the SDGs. Effat’s research, diverse in its scope and impact, ranges from cutting-edge technological advancements to interdisciplinary collaborations that address both local and global challenges. Using a mixed-method approach and drawing from internal data and insights from the university’s archives the chapter underscores Effat University’s commitment to innovation, interdisciplinary education, research excellence, international collaborations, and sustainable practices, all harmonized by the guiding IQRA values. These concerted efforts resonate deeply with Vision 2030 and the SDGs, setting the stage for sustained academic excellence and solid foundation for future academic and societal progress.
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Sudden Fall Detection and Prediction Using AI TechniquesFall prediction is a critical process in ensuring the safety and well-being of individuals, particularly the elderly population. This paper focuses on the development of a fall detection and prediction system using wearable sensors and machine learning algorithms. The system issues an alarm upon predicting the occurrence of falling and sends alerts to a monitoring centre for timely assistance. Wearable sensor devices, including Inertial Measurement Units (IMUs) equipped with accelerometers, gyroscopes, and magnetometers are utilized for data collection. UPFALL, a comprehensive online freely available dataset, had been utilized for training and verifying the proposed system. Several machine learning algorithms, such as K-Nearest Neighbours (KNN), Random Forest, Support Vector Machine (SVM), and Gradient Boosting, are implemented and evaluated. Among these algorithms, KNN demonstrates the highest effectiveness for fall detection having an accuracy of 93.5%. Furthermore, deep learning models, including Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN), are employed. The GRU model exhibits superior performance among the deep learning approaches by having the least train and test loss of 0.219 and 0.267 respectively. An early fall prediction function is incorporated by establishing a threshold selection process based on logical analysis. The maximum voting concept is employed to determine the optimal threshold.
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Machine Unlearning: An Overview of the Paradigm Shift in the Evolution of AIThe rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledge for smart systems. However, the concept of machine unlearning has emerged as a transformative paradigm shift in the field of AI, due to the amount of false information that have been learned over the past. Machine unlearning refers to the ability of AI systems to reverse or discard previously acquired knowledge or patterns, enabling them to adapt and refine their understanding in response to changing circumstances or new insights. This paper explores the concept of machine unlearning, its implications, methods, challenges, and potential applications. The paper begins by providing an overview of the traditional learning-based approaches in AI and the limitations they impose on system adaptability and agility. It then delves into the concept of machine unlearning, discussing various techniques and algorithms employed to remove or modify learned knowledge from AI models or datasets.
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Metabolomic profiling reveals altered phenylalanine metabolism in Parkinson’s disease in an Egyptian cohortIntroduction: Parkinson’s disease (PD) is the most common motor neurodegenerative disease worldwide. Given the complexity of PD etiology and the different metabolic derangements correlated to the disease, metabolomics profiling of patients is a helpful tool to identify patho-mechanistic pathways for the disease development. Dopamine metabolism has been the target of several previous studies, of which some have reported lower phenylalanine and tyrosine levels in PD patients compared to controls.Methods: In this study, we have collected plasma from 27 PD patients, 18 reference controls, and 8 high-risk controls to perform a metabolomic study using liquid chromatography-electrospray ionization–tandem mass spectrometry (LC-ESI-MS/MS).Results: Our findings revealed higher intensities of trans-cinnamate, a phenylalanine metabolite, in patients compared to reference controls. Thus, we hypothesize that phenylalanine metabolism has been shifted to produce trans-cinnamate via L-phenylalanine ammonia lyase (PAL), instead of producing tyrosine, a dopamine precursor, via phenylalanine hydroxylase (PAH).Discussion: Given that these metabolites are precursors to several other metabolic pathways, the intensities of many metabolites such as dopamine, norepinephrine, and 3-hydroxyanthranilic acid, which connects phenylalanine metabolism to that of tryptophan, have been altered. Consequently, and in respect to Metabolic Control Analysis (MCA) theory, the levels of tryptophan metabolites have also been altered. Some of these metabolites are tryptamine, melatonin, and nicotinamide. Thus, we assume that these alterations could contribute to the dopaminergic, adrenergic, and serotonergic neurodegeneration that happen in the disease.
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Statistical Analysis for Evaluation and Improvement of Computer Science EducationDeveloping well-prepared and competent graduates is one of the main goals of all university programs globally. Recently, Computer Science (CS) has achieved tremendous success among all career fields driven by the strong competence in the market and the rapid changes in technology. Our goal is to develop an automated framework that provides efficient management, evaluation and improvement of the CS students education, as well as a profound establishment of a successful study tree for CS university programs. Such a challenging goal comprises major factors that should be inclusively considered. High school (HS) students are expected to join CS university programs with different educational backgrounds and learning capabilities. The strength of association among several performance-related factors including the academic performance of students in HS is evaluated to gain insights and infer indicators in CS programs. The automatic correlational analysis of the prerequisites for each course is also investigated to assess the program structure and dependencies among several CS courses. In this comprehensive study, all these factors are efficiently analyzed in order to investigate the valid causes of low and high performance of both CS university students and programs. Experimental results have concluded several major findings with validated associations that assure and prioritize the importance of evaluation and improvement of CS education.
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From Analysis to Implementation: A Comprehensive Review for Advancing Arabic-English Machine TranslationIn an increasingly interconnected world, the demand for accurate Arabic-English translation has surged, highlighting the complexities in handling Arabic's intricate morphology and diverse linguistic structures. This research delves into various translation models, including Convolutional Neural Networks (CNNs), LSTM, Neural Machine Translation (NMT), BERT, and innovative fusion architectures like the Transformer-CNN. Each model's strengths and limitations are scrutinized through comprehensive evaluations and comparisons, unveiling their potential to address translation challenges. The research then builds two models, the first based on LSTM and the second on BERT, and tests their performance in translating English to Arabic. The paper then conducts an in-depth analysis of the results. The comparative analysis provides insights into the landscape of Arabic-English translation models, guiding future research toward refining models, leveraging diverse datasets, and establishing standardized evaluation benchmarks to bridge existing gaps.
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The challenges for the next generation digital health: The disruptive character of artificial intelligence and machine learningArtificial Intelligence is not a technology. Artificial Intelligence is a robust strategy for the digital transformation of anything. This bold characteristic of AI is the focus of this introductory chapter. We elaborate on the unique value proposition of AI and its contribution to the formulation of the emerging, next-generation value-based ecosystem for Digital Health. Then main discussion of the chapter is dedicated on disruptive scenarios and use case for AI-Enabled next-generation Generation Digital Health Services and Solutions. We aim by this short coverage and discussion to highlight bold directions for the utilization of AI in the eHealth context. We are also commenting on various technological, business, strategic and ethical aspects of AI. Last but not least, we convey the main message of the chapter: A robust, resilient strategy for the training and upskilling of stakeholders in healthcare in the foundations, models and strategy of AI is required. We are somehow carriers of a bold change in our societies and in our healthcare ecosystem. It is up to us, as humans with intuition and decision-making capability, to envision the way that the disruptive technology of AI will add value to healthcare, patients' health status and people' wellbeing in the next few years.
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Chapter 14. Transformative leadership and sustainable innovation in higher education: Setting the context.Transformative leadership is a holistic and bold approach for the next generation of higher education. In this chapter, we provide an introductory, definitive discussion of the phenomenon and integrate it with the concept of sustainable innovation. In Section 1, we introduce a high-level integrated approach to transformative leadership in higher education institutions. We define and discuss diverse pillars. In Section 2, we propose a contextual framework for transformative leadership as a value space. In an effort to provide guidelines and principles for the crafting of a transformative leadership strategy, we propose indicative actions and initiatives for the deployment of transformative leadership in higher education. Finally, in Section 4, we summarized some simple designs for tools and instruments to support the documentation of the transformative leadership strategy, including the transformative leadership scorecard and the systematic overview of the portfolio of transformative educational programs. We also comment on the significance of social impact and research, innovation, and sustainability aspects of the strategy. The contribution of this chapter is multi-fold. It can be used as a reference document for administrators interested in the design and execution of transformative leadership in universities and colleges. It also provided guiding principles for researchers interested in further contributions in the domain.
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Automated Recognition of Human Emotions from EEG Signals Using Signal Processing and Machine Learning TechniquesOne of the most difficult challenges in pattern recognition, machine learning, and artificial intelligence is emotion recognition. For automatic emotion recognition, voices, images, and electroencephalography (EEG) signals have been employed. Emotion recognition systems based on brain activity are extremely useful in a variety of sectors. In today’s computer age, providing reliable information on emotion recognition is a critical task. Because emotional activity is complicated, it is critical to apply cutting-edge technology and profit from signal processing and machine learning methods while learning about it. Although individuals have been interested in documenting emotional activities over the past decade, there are still fundamental issues that must be addressed in order to take advantage of technology in the understanding of emotion activity. In this chapter, we will go over the most recent signal processing and machine learning algorithms for detecting emotion activity information in the system. We will also cover the difficulties and significant considerations associated with emotion recognition. Several open concepts will be presented for future research to use in understanding the challenges with emotion recognition. Finally, we present some specific examples of emotion recognition using EEG signals employing various AI and signal processing techniques.
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Artificial Intelligence-Enabled EEG Signal Processing-Based Detection of Epileptic SeizuresEpilepsy affects numerous people worldwide. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Real-time seizure onset detection is critical for accurate evaluation, presurgical assessment, seizure prevention, and emergency warnings and overall improving patients’ quality of life, but manually examining EEG signals is tedious and time-consuming. To assist neurologists, many automatic systems have been proposed to support neurologists utilizing conventional techniques, and these have performed well in detecting epilepsy. Big data applications, particularly biomedical signals, are becoming more appealing in this era as data collection and storage have expanded in recent years. Because data mining approaches are not adaptable to the new needs, big data processing to extract knowledge is difficult. In this chapter, we review AI-enabled signal processing-based approaches for detecting epileptic seizures using EEG signals including with examples.
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Research and Education Skills as a core part of Digital Transformation in Healthcare in Saudi ArabiaThe adoption of digitalization technologies in healthcare systems is crucial for improving the delivery of medical services, facilitating patient access, and enhancing the overall patient experience. However, the experience and challenges of the Saudi healthcare system in adopting digital transformation have received little attention in the literature. Thus, this chapter aims to provide an overview of the digital transformation process in the Saudi healthcare system. The authors also shed light on the major challenges and suggested technology-based solutions. This chapter was written in light of the available global and local literature discussing the digitization of the health sector. Moreover, the Saudi governmental documents and official reports were utilized to include the latest updates and future plans for the local digital transformation strategy. In paving the way toward digital transformation, many challenges have been faced in areas such as medical records unification, technical infrastructure, and workforce capability. Solutions such as telemedicine applications, artificial intelligence, cybersecurity, and blockchain have been gradually applied to achieve the goal of transformation by 2030. Saudi Arabia's digital transformation began in 2018, with the implementation of the Vision 2030’s rapid digital change. With the emergence of the COVID-19 pandemic, a leap in utilizing digital health solutions has been noticed locally. Nevertheless, more action plans are needed to address the challenges and implement suitable solutions accordingly.
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Chapter1. Digital transformation in higher education in times of artificial intelligence: Setting the emerging landscapeDigital transformation has become a top priority for Higher Education, driven by technological advances like: Artificial Intelligence (AI), Artificial General Intelligence (AGI), and Generative Open AI. It serves as a catalyst for the reshaping of mainstream processes in academic institutions, emphasising teamwork, collaborative projects, and critical thinking in research, learning, and assessment strategies. In this chapter, we contextualise the use of this digital transformation, highlighting its potential to improve learning experiences, business efficiency, and upskill students and faculty. Our holistic approach to Digital Transformation as an enabler of excellence in Higher Education is based on four pillars of excellence and impact: Business Process Reengineering, Learning Excellence & Skills Building, Research Capacity and Innovation, and Partnership & Outlook. Digital transformation needs the development of efficient, resilient, flexible and adaptable strategies and a strong collaboration between all the actors involved in the process, to ensure the coherence, the sustainability, and alignment of the objectives, means, and targets with the real needs of the learners, tutors, labour market, and society as whole. Our bold proposition consists of a model for the strategy design of digital transformation in universities and colleges organised in three dimensions: Understand, Strategize, Deploy & Exploit. Each dimension emphasises different stages of the process: understanding emerging technologies and their impact on higher education, collaboration between stakeholders, strategy and priorities formulation, roadmap of implementation, deployment and exploitation of digital technologies, etc. The ongoing digital transformation in Higher Education will continue to create an extensive shift in educational processes - learning, teaching, research, and management. Institutions around the world are taking bold initiatives to adapt to this rapidly changing environment, emphasizing the importance of readiness for technological changes, system development, inclusive, and sustainable transformation.
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Digital health as a bold contribution to sustainable and social inclusive development.In this volume we tried to provide indicative complementary aspects of the Next Generation eHealth. In the concluding chapter of the book we elaborate with the capacity of Digital Health Ecosystem to serve as a bold enabler of the Sustainable Development Goals. We elaborate on the enabling technologies, the stakeholders and the emerging marketplace of digital health services as key determinants of the so-called Sustainable Health Ecosystem. We also discuss the Digital health as a pivotal pillar of Social Inclusive Development and we provide some directions. This chapter can serve as a reference for a contextual framework of Digital Health as a Sustainable Development enabler.
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Digital transformation in higher education: PART A. Challenges and best practicesAs the twenty-first century unfolds, the debate on digital transformation is impacting all sectors of society. Higher education, a beacon in the knowledge dissemination and creation, is challenged by the entire digital transformation strategy and ecosystem. At this pivotal moment in time, higher education institutions find themselves facing diverse transformative forces, with artificial intelligence (AI) at the forefront, promising to reshape educational landscapes. The acceleration of digital transformation in higher education brings with it a unique set of challenges. One of the greatest is the task of seamlessly integrating advanced technologies, such as AI, into longstanding pedagogical frameworks while preserving the invaluable human element of teaching. Institutions are tasked with fostering environments that promote active and transformative learning, aiming to bridge digital divides and create inclusive progress. The rapid evolution of technology demands a corresponding evolution in our approaches to teaching and assessment, calling for a paradigm shift in educational philosophy. It seems that in the current evolution and development of higher education, there is a crucial need to re-invent the unique value proposition of procedures and strategies towards enhanced student experience and learning outcomes. The underlying philosophy of the first volume of our edition, centers on the symbiotic relationship between technology and human-centric pedagogy with social foot print. Each chapter explores a distinct facet of this digital transformation journey in higher education, guiding readers through the ways in which higher education can embrace digital transformation while remaining substantial in its commitment to student-centric learning and teaching.
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Data Governance in Healthcare OrganizationsThe Next Generation eHealth has a critical data goverance component. In order also to have an effective design and implementation of a robust Data Strategy, a holistic approach to Data governance is requires. In this context, this chapter introduces the readers to the concept of Data Governance as it is applied in Health Organizations. We, elaborate on the state of the art and also, we summarize in a compact way a sophisticated approach for the collection of data and evidence for drafting resilient Data Governance Strategies in health organizations. The main contribution of the chapter is the communication of best practices for the design and implementation of Data Governance and Data Strategy policies. The approach can be re-used by various stakeholders, and contributes to the body of knowledge of Data Governance strategy for next generation eHealth services and applications.
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What's next in higher education: The AI revolution 2030This chapter explores the transformative impact of artificial intelligence (AI) on higher education, particularly in the context of accelerating technological and societal changes. As higher education institutions face the need to offer more flexible, adapted, and relevant academic programmes, AI presents significant opportunities and challenges. In the first part of this chapter, we elaborated on the characteristics of the evolution of AI, including the emerging AI landscape. One of our contributions in this concluding chapter is to conceptualise the next areas of deployment of AI in higher education, considering the novel, innovative services that will disrupt the entire market in the next few years. Our strategic proposition for the deployment of AI in higher education highlighted six pillars, namely: large language models and research. AI is content creation. AI, or personalized learning. AI, skills-building assistants. AI is education out of the box. AI. We presented opportunities to harness AI to enhance teaching, learning, and research under each pillar, along with a detailed list of potential application areas and services. Universities are exploring innovative ways to use AI-driven solutions to improve research, teaching, and learning experiences, and we also developed indicative scenarios for the use of AI in higher education based on the six pillars. One of our bold contributions in this chapter is the structured framework for understanding the evolution and use of AI in higher education, utilising a matrix to map the intersection of market penetration and product development. Finally, we discuss future directions and strategies for higher education in 2030 in light of advances in AI technology.
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Digital transformation in higher education: PART B. Cases, examples, and good practicesDigital Transformation in Higher Education examines the various aspects of technology enhanced learning and proposes a roadmap that can be utilized for strategic planning to implement resilient digital transformation strategies within higher education institutions effectively. Chapter authors present a comprehensive examination of the many obstacles and potential advantages associated with the digital transformation process within the context of higher education and provide valuable insights into the necessary training of academics and leaders that will enable them to develop the digital intelligence that is essential for addressing complex challenges and achieving success. Featuring case studies from around the world, chapters explore topics such as the digital transformation of doctoral programs in Romania, e-learning effectiveness in Slovakian universities and how education in Kuwait has been transformed through emerging technologies. The key contribution of this study lies in its provision of practical insights, best practices, and case studies that will enable institutions to traverse the ever-changing process of digital transformation effectively.
