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  • 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.
  • Chapter 1. Leadership and Innovation in Higher Education in 2035: The Open Research Agenda

    Baroudi, Sandra; Lytras, Miltiadis; External Collaboration; NA; 0; 0; Computer Science; 0; Baroudi, Sandra (Emerald Publishing Limited, 2024-06-24)
    This chapter highlights the key areas for the leadership and innovation research agenda in 2035. This agenda will direct researchers’ focus to the core transversal skills that individuals must have amidst the shift toward a greener and digitalized economy. Such skills include leadership, management, creativity, communication and adaptability. The role of macro governmental policies and micro organizational policies is of great significance to ensuring the implementation (if any) of these changes and of core to the research agenda. This chapter will also guide researchers to the challenges at the higher education level that need to be addressed to ensure the balance between the skills and knowledge acquired by workers through education and the needs of businesses in order to increase the productivity and innovation.
  • Promoting Sales of Knowledge Products on Knowledge Payment Platforms: A LargeScale Study with a Machine Learning Approach

    Zhang, Jacky; Jiang, Shan; Wang, Xuyan; Duan, Keran; Xiao, Yuting; Lytras, Miltiadis; Xu, Dongming; Zheng, Yunhao; Ordonez De Pablos, Patricia; External Collaboration; et al. (Elsevier, 2024-05-14)
    With the digital transformation of the global economy, a new mode of knowledge service has emerged on open innovation platforms, such as those for the sharing economy. This mode is the paid knowledge-sharing service, where knowledge providers share knowledge only with those who have paid for it. Since an individual customer’s purchases are influenced by others around them, we adopted social influence theory to explain sales of such services on paid knowledge-sharing platforms. A machine learning approach was applied to analyze 27,223 text reviews from the Zhihu Live platform, a well-known and large-scale open knowledge community in China. Hierarchical regression models were built to verify twelve proposed hypotheses about the knowledge providers, knowledge quality, interaction quality, and ratings. The results confirm the positive effect on sales of responsiveness, a dimension of interaction quality, and the negative effect on sales of free provider-driven knowledge contributions. In summary, this study provides a comprehensive framework for antecedent factors of sales of knowledge-sharing services. By introducing knowledge management notions from the field of e-commerce (e.g., price, quality), this study broadens the understanding of the free-to-paid phenomenon on knowledge-sharing platforms.
  • Transformative Leadership and Sustainable Innovation in Education: Interdisciplinary Perspectives

    Lytras, Miltiadis; Baroudi, Sandra; Baroudi, Sandra; External Collaboration; NA; 0; 0; Computer Science; 0; Baroudi, Sandra (Emerald Publishing Limited, 2024-06-24)
    t is essential to learn what innovative practices and leadership approaches are adopted in the education sector to solve challenges such as digital transformations, inefficiencies in higher education administration models, and the need for a connection between innovation and sustainability within the curriculum. Transformative Leadership and Sustainable Innovation in Education addresses these topics, discussing several possible transformations at the policy, classroom, and research levels. Adopting an interdisciplinary approach, scholars from three main disciplines of education, business, and IT consider both a leadership and management perspective and an educational perspective. This integration of research, academia and industry bridges the gap between theory and practice, tackling how to make schools a sustainable enterprise, how to sustain student learning through leadership practices, and exploring the disruptive impact of artificial intelligence and other technologies on higher education. Transformative Leadership and Sustainable Innovation in Education is a valuable resource to a diverse network of policy makers, school and university leaders, educators, practitioners, curriculum designers, innovators, and investors who want to collaborate to identify and implement innovations that transform education and research.
  • Digital transformation for enhanced learning impact and student experience.

    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)
    In this introductory chapter we collaborate on how Digital Transformation supports a value-driven educational approach, emphasizing the need for regular assessments of stakeholder needs, enhancing students' abilities to solve complex problems, applying learned knowledge effectively, nurturing creativity, and boosting employment prospects through skill development. Strategic considerations for implementing DT include creating a shared vision through collaborative strategy development, establishing clear objectives, designing a detailed action plan for DT initiatives, encouraging active participation from all educational community members, and maintaining the DT strategy through continuous evaluation and adaptation. By interweaving Digital Transformation with these strategic educational priorities, higher education institutions can not only improve the learning experience but also equip students to succeed in a rapidly evolving future.
  • Dynamic Pricing Mechanisms for Load Management in Smart Grids

    Abed, Fidaa; No Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Computer Science; 0; Abed, Fidaa (IEEE, 2024-01-15)
    In this research study, we introduce an innovative approach to managing load distribution in smart grid infrastructures. The primary objective is to regulate power demand during peak hours, utilizing dynamic pricing mechanisms. Unlike traditional centralized systems, our proposed solution operates on a distributed paradigm, deriving its foundation from the rational actions of grid users. To effectively analyze the prevailing scenario, we employ a game-theoretic model to understand the behaviors of users, who, in our model, act primarily out of self-interest. An initial observation indicates that if left to their own devices, these selfish users might make decisions that could be detrimentally arbitrary to the system's efficient functioning.In response to this challenge, we introduce a pricing mechanism designed to enhance the allocation quality influenced by these self-centered users. We begin by establishing the validity of our approach through a proof that the game, even with the existence of selfish users, will reach a Nash equilibrium. This equilibrium ensures that no player can benefit by deviating unilaterally from their chosen strategy after considering an opponent's choice. Following this, we demonstrate that by incorporating our dynamic pricing strategy, the resulting allocation's peak demand is effectively managed. Specifically, the peak of this allocation, even in the worst-case scenario, will not exceed double the value of an ideal or optimal peak. This result underscores the efficiency and efficacy of our proposed mechanism in maintaining a balance between user behavior and systemic demand, ensuring a more stable and sustainable smart grid infrastructure.
  • Transformative learning for future higher education: The AI-enabled learning revolution 2035.

    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 delves into the pivotal role of Digital Transformation (DT) strategies in fostering educational innovation, particularly through the lens of Transformative Learning (TL). By outlining a five-stage TL model, we explore how DT strategies can not only support but significantly enhance educational reforms. Actions and Multipliers, constituting the core elements of this model, interact dynamically to advance TL within academic institutions. Actions, such as strategic initiatives and the development of learning environments, account for the tangible steps towards transformation. Meanwhile, Multipliers amplify these efforts, emphasizing the importance of strategy, commitment, and the sustainable impact of educational transformations. We also highlight the emerging influence of Artificial Intelligence (AI) in reshaping learning contexts, demonstrating its capacity to personalize learning experiences and foster problem-solving skills. Additionally, we envision the future trajectory of Higher Education (HE) towards 2035, emphasizing the integration of AI and DT in creating a responsive and adaptive educational ecosystem. This chapter argues that DT is not just a tool but a catalyst for active and transformative learning, proposing a holistic approach to integrating technology in education that addresses current challenges and anticipates future needs.
  • An Ensemble Voting Approach With Innovative Multi-Domain Feature Fusion for Neonatal Sleep Stratification

    Subasi, Abdulhamit; Siddiqa, Hafza Ayeshaa; Nahliis, Abdelwahed; Chen, Chen; Xu, Yan; Wang, Laishuan; Nawaz, Anum; Westerlund, Tomi; Chen, Wei; External Collaboration; et al. (IEEE, 2023-12)
    A limited number of electroencephalography (EEG) channels are useful for neonatal sleep classification, particularly in the Internet of Medical Things (IoMT) field, where compact and lightweight devices are essential to monitoring health effectively. A streamlined and cost-effective IoMT solution can be achieved by utilizing fewer EEG channels, thereby reducing data transmission and device processing requirements. Using only two channels of an EEG device, this study presents a binary and multistage classification of neonatal sleep. The binary classification (sleep vs awake) achieved an accuracy of 87.56%, and a Cohen’s kappa of 74.13%. The quiet sleep ( QS ) detection accuracy was 95.63%, with a Cohen’s kappa of 83.87%. For the three-stage classification, accuracy was 83.72%, and Cohen’s kappa was 69.73%. With only two channels, these are the highest performance parameters. The focus is on the fusion of features extracted through flexible analytical wavelet transform (FAWT) & discrete wavelet transform (DWT), ensemble-based voting models, and fewer channels. To feed crucial features into the ensemble-based voting model, feature importance, feature selection, and validation mechanisms were used. To design the voting classifier, several machine learning models were used, compared, and optimized. With SelectKBest feature selection, the proposed methodology was found to be the most effective. By using only two channels, this study shows the practicality of classifying neonatal sleep stages.

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