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  • Federated Learning for Intrusion Detection in Internet of Vehicles

    Marir, Naila; Sarirete, Akila; naila; Alam, Leena; AlFarra, Joud; Computer Science
    The Internet of Vehicles (IoV) is rapidly evolving, bringing vehicles into a unified network and revolutionizing connectivity and convenience. By enabling seamless communication between vehicles, infrastructure, and cloud services, IoV holds the potential to improve traffic management, road safety, and passenger experiences. However, this increased connectivity also exposes the IoV to new security challenges. Protecting against cyber-attacks on Vehicle-to-Everything (V2X) network communications is now more crucial than ever before. The objective of this senior project is to create an Intrusion Detection System (IDS) tailored for charging stations for electric vehicles, using federated learning (FL) and machine/deep learning methods to improve the detection of abnormal activities and cyber threats. To address IoV challenges, we require complex network traffic analysis and efficient machine learning algorithms for real-time cyber attack prediction. Scaling out a distributed IDS adds further complexity. Our project proposes an FL approach, that decentralizes training and preserves privacy. IoV components can update the intrusion detection model using local data without sharing sensitive information. The project aims to achieve two outcomes: first, developing an IDS capable of detecting and mitigating common vehicular network attacks effectively, with results including performance metrics; second, contributing significantly to advancing cybersecurity in the IoV domain by demonstrating the feasibility of FL for intrusion detection. The privacy-preserving nature of our approach aligns with emerging trends in decentralized cybersecurity solutions, ensuring data security and integrity in IoV environments. The project evaluated 20 machine learning algorithms that led to the selection of ExtraTreesClassifier as the optimal ML algorithm for our global model, which achieved an accuracy of 83.38%. While the specialized scenario-specific submodels attained very high accuracies ranging from 99.91% to 99.95%, the FL approach allowed for a comprehensive and adaptable solution capable of handling the complex and evolving IoV environment.
  • Machine Unlearning: An Overview of the Paradigm Shift in the Evolution of AI

    Jaman, Layan; Alsharabi, Reem; ElKafrawy, Passent; Department Collaboration; Virtual Reality Lab; 2; 0; Computer Science; 0; Jaman, Layan (IEEE, 2024-01-16)
    The 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.
  • Metabolomic profiling reveals altered phenylalanine metabolism in Parkinson’s disease in an Egyptian cohort

    Sheb, Nourhan; El-Jaafary, Shaimaa; A. Saeed, Ayman; ElKafrawy, Passent; El-Sayed, Amr; Shamma, Samir; Elnem, Rasha; Mekky, Jaidaa; Mohamed, Lobna A.; Kittaneh, Omar; et al. (Frontiers, 2024-03-07)
    Introduction: 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.
  • Statistical Analysis for Evaluation and Improvement of Computer Science Education

    ElKafrawy, Passent; Kamal, Ahmed; Medhat, Walaa; El-Hadidi, Mohamed; Yousef, Ahmed Hassan; External Collaboration; Virtual Reality Lab; 0; 0; Computer Science; et al. (IEEE, 2024-01-15)
    Developing 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.
  • From Analysis to Implementation: A Comprehensive Review for Advancing Arabic-English Machine Translation

    Sidiya, Aichetou Mohamed; Alzaher, Hanin; Almahdi, Razan; ElKafrawy, Passent; Department Collaboration; Virtual Reality Lab; 3; 0; Computer Science; 0; et al. (IEEE, 2024-01-16)
    In 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.
  • End to End Precision Password Cracking

    Khan, Sohail; Ghryani, Layal; Alsubaie, Rayanah; Aldabbagh, Shaymaa; Computer Science
    The study of password-cracking techniques remains a fundamental pursuit within the realm of information security, primarily due to the ever-present threat posed by weak passwords. This project introduces cutting-edge approaches and methodologies aimed at improving password-cracking precision. It explores various techniques, including brute force, dictionary attacks, machine learning, and contextual word lists, with the primary objective of enhancing the e - ciency and precision of password bypassing. Specific goals include evaluating the performance of di↵erent cracking methods and technologies. The project outlines novel methodologies and approaches for password ex- ploitation, discusses their implementation, and provides insights from experi- mental results. The research addresses the pressing need for improved password security measures and contributes to digital forensics and criminal investiga- tions by enhancing password-cracking techniques. The findings of this study have practical implications for strengthening password security across various applications and platforms. By synthesizing insights from existing research and proposing innovative methods, this project aims to advance the state of the art in password cracking, ultimately leading to more robust security measures and better protection of sensitive information.
  • The challenges for the next generation digital health: The disruptive character of artificial intelligence and machine learning

    Lytras, Miltiadis; Housawi, Abdulrahman; Alsaywid, Basim; Aljohani, Naif; External Collaboration; NA; 0; 0; Computer Science; 0; et al. (Academic Press (Elsevier Imprint), 2024-08-01)
    Artificial 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.
  • Chapter 14. Transformative leadership and sustainable innovation in higher education: Setting the context.

    Lytras, Miltiadis; Alkhaldi, Afnan; Malik, Sawsan; External Collaboration; NA; 0; 0; Computer Science; 0; Lytras, Miltiadis (Emerald Publishing Limited, 2024-06-24)
    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.
  • Automated Recognition of Human Emotions from EEG Signals Using Signal Processing and Machine Learning Techniques

    Subasi, Abdulhamit; Tuba Nur Subasi; Oznur Ozaltin; External Collaboration; NA; NA; NA; Computer Science; NA; Abdulhamit Subasi (CRC Press, 2024-06-06)
    One 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.
  • Artificial Intelligence-Enabled EEG Signal Processing-Based Detection of Epileptic Seizures

    Subasi, Abdulhamit; Muhammed Enes Subasi; Emrah Hancer; External Collaboration; NA; NA; NA; Computer Science; NA; Subasi, Abdulhamit (CRC Press, 2024-04)
    Epilepsy 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.
  • Research and Education Skills as a core part of Digital Transformation in Healthcare in Saudi Arabia

    Alsaywid, Basim; Qedair, Jumanah; Alkhalifah, Yara; Miltiades Lytras; External Collaboration; NA; 0; 0; Computer Science; 0; et al. (Academic Press, 2023-05-12)
    The 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.
  • Chapter1. Digital transformation in higher education in times of artificial intelligence: Setting the emerging landscape

    Lytras, Miltiadis; Serban, Andreea Claudia; Alkhaldi, Afnan; Aldosemani, Tahani; Malik, Sawsan; External Collaboration; NA; 0; 0; Computer Science; et al. (Emerald Publishing Limited, 2024-08-31)
    Digital 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.
  • Digital health as a bold contribution to sustainable and social inclusive development.

    Lytras, Miltiadis; Housawi, Abdulrahman; Alsaywid, Basim; Aljohani, Naif; External Collaboration; NA; 0; 0; Computer Science; 0; et al. (Academic Press (Elsevier Imprint), 2024-08-01)
    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.
  • Digital transformation in higher education: PART A. Challenges and best practices

    Lytras, Miltiadis; Serban, Andreea Claudia; Alkhaldi, Afnan; Aldosemani, Tahani; Malik, Sawsan; External Collaboration; NA; 0; 0; Computer Science; et al. (Emerald Publishing Limited, 2024-08-31)
    As 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.
  • Data Governance in Healthcare Organizations

    Housawi, Abdulrahman; Lytras, Miltiadis; External Collaboration; NA; 0; 0; Computer Science; 0; Lytras, Miltiadis (Academic Press (Elsevier Imprint), 2024-08-01)
    The 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.
  • What's next in higher education: The AI revolution 2030

    Lytras, Miltiadis; Serban, Andreea Claudia; Alkhaldi, Afnan; Aldosemani, Tahani; Malik, Sawsan; External Collaboration; NA; 0; 0; Computer Science; et al. (Emerald Publishing Limited, 2024-08-31)
    This 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.
  • Digital transformation in higher education: PART B. Cases, examples, and good practices

    Lytras, Miltiadis; Serban, Andreea Claudia; Alkhaldi, Afnan; Aldosemani, Tahani; Malik, Sawsan; External Collaboration; NA; 0; 0; Computer Science; et al. (Emerald Publishing Limited, 2024-08-31)
    Digital 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.
  • Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece.

    Papadopoulou, Paraskevi; Lytras, Miltiadis; External Collaboration; NA; 0; 0; Computer Science; 0; Papadopoulou, Paraskevi (2024-08-01)
    The rapid advancement of Artificial Intelligence (AI) in healthcare presents challenges and opportunities for patient welfare. New policies, governance, and interoperability standards have been introduced necessitating patient and stakeholder engagement. The Organization for Economic Cooperation and Development (OECD) countries have prioritized health transformation through the use of health data, digital tools, and AI-driven healthcare innovation. AI has enormous potential to improve activities from research to treatment, administration, patient welfare, and value for money. However, responsible development is crucial to avoid misuse. This chapter explores the ethical considerations of AI-generated healthcare innovation, emphasizing the need for a balance between AI-driven progress and patient welfare. It examines progress in OECD countries, with a specific focus on enhancing patient welfare in Greece. Challenges related to treatment, data privacy, transparency, fairness, algorithmic bias, informed consent, and impact on healthcare professionals are discussed. Regulatory frameworks are considered as well as professional guidelines in the context of international collaboration. The chapter provides insights and recommendations for a responsible and patient-centric approach, prioritizing patient welfare while navigating ethical dimensions.
  • Designing robust and resilient data strategy in health clusters (HC): Use cases identification for efficiency and performance enhancement.

    Housawi, Abdulrahman; Lytras, Miltiadis; External Collaboration; NA; 0; 0; Computer Science; 0; Housawi, Abdulrahman (Academic Press (Elsevier Imprint), 2024-08-01)
    The design, implementation of execution of Data Strategy within a health cluster is a sophisticated process. Diverse actors, stakeholders and business functions are involved. The need for designing roadmaps for the implementation of the strategy, also incorporates the identification of use cases for novel initiatives, services and systems. In this chapter we communicate our experience in designing use cases as carriers of enhanced efficiency and performance that is data driven. This is a high value intellectual effort aiming to strategize the allocation of resources and the design of new robust and resilient systems to promote efficiency and performance. We exploit the outcomes of maturity assessment for Data Governance and Data Strategy and we provide a systematic methodology for the implementation of a resilient strategy in the health cluster.
  • Transformative leadership in Kuwait Direct Investment Promotion Authority: Investing in talent, innovation, and the next generation

    Alkhaldi, Afnan; Malik, Sawsan; Alsadeeqi, Ahmad; Lytras, Miltiadis; External Collaboration; NA; 0; 0; Computer Science; 0; et al. (Emerald Publishing Limited, 2024-06-24)
    Digital transformation is becoming a necessity for all organizations all over the world. The importance of digital transformation is not only applicable to the private sector but also extends to the public sector. Kuwait boasts the Kuwait Direct Investment Promotion Authority (KDIPA), a pivotal entity entrusted with the mission of spearheading investment promotion across diverse sectors. More importantly, the focus has been recently on investing on digital transformation technologies where their statistics shows that 33% of their investment are in the emerging technologies. However, the success of KDIPA was not a mere chance or coincidence where it is really attributed to the transformative leadership that it has. It started to invest in projects that develop the talents and skills of Kuwaitis to create sustainable development and bring innovative technologies to the state of Kuwait. This chapter provides an overview of digital transformation and the role of KDIPA and its transformative leadership in attaining the Strategic Development Goals (SDGs) for a new Kuwait Vision of 2035.

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