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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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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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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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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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Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece.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.
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Designing robust and resilient data strategy in health clusters (HC): Use cases identification for efficiency and performance enhancement.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.
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Transformative leadership in Kuwait Direct Investment Promotion Authority: Investing in talent, innovation, and the next generationDigital 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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Chapter 1. Leadership and Innovation in Higher Education in 2035: The Open Research AgendaThis 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.
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Brain stroke detection from computed tomography images using deep learning algorithmsStroke is one of the common causes of death worldwide. Stroke is the inability of a focus to be fed in the brain due to clogged or bleeding of the vessels feeding the brain. Because early stroke treatment and diagnosis are related to a favorable patient outcome, time is a critical aspect of successful stroke treatment. In this chapter, we examine the stroke classification from Brain Stroke CT Dataset, with deep learning architectures. In the experimental study, a total of 2501 brain stroke computed tomography (CT) images were used for testing and training. For this purpose, numerus widely known pretrained convolutional neural networks (CNNs) such as GoogleNet, AlexNet, VGG-16, VGG-19, and Residual CNN were used to classify brain stroke CT images as normal and as stroke. Several performance metrics such as accuracy (ACC), specificity (SPE), sensitivity (SEN), and F-score are used to evaluate the performances of the classifier. The best classification results are achieved by VGG-19 with ACC 97.06%, SEN 97.41%, SPE 96.49%, and F-score 96.95%.
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Breast tumor detection in ultrasound images using artificial intelligenceLeveraging artificial intelligence (AI) for categorizing breast tumors as malignant or benign from breast ultrasound images can provide an effective and relatively low-cost method for the diagnosis of breast cancer. Presently, many machine learning (ML) and deep learning (DL) algorithms have been used for early-stage breast cancer detection. AI algorithms have shown promising results in breast cancer detection tasks. The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. Convolutional neural network (CNN) models analyze the image data in multiple layers and extract features which helps in better feature extractions and better performance in comparison to the conventional ML algorithms. Apart from conventional learning algorithms, we use the transfer learning technique which uses knowledge from its previous training in another related problem set. In this chapter, we have demonstrated the use of DL models through transfer learning, deep feature extraction, machine learning models, and comparison of their performances.
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Breast cancer detection from mammograms using artificial intelligenceBreast cancer is one of the fastest-growing forms of cancer in the world today. Breast cancer is primarily found in women, and its frequency has been gaining significantly in the last few years. The key to tackle the rising cases of breast cancer is early detection. Many studies have shown that early detection significantly reduces the mortality rate of those affected. Machine learning and deep learning techniques have been adopted in the present scenario to help detect breast cancer in an early stage. Deep learning models such as the convolutional neural networks (CNNs) are suited explicitly to image data and overcome the drawbacks of machine learning models. To improve upon conventional approaches, we apply deep CNNs for automatic feature extraction and classifier building. In this chapter, we have demonstrated thoroughly the use of deep learning models through transfer learning, deep feature extraction, and machine learning models. Computer-aided detection or diagnosis systems have recently been developed to help health-care professionals increase diagnosis accuracy. This chapter presents early breast cancer detection from mammograms using artificial intelligence (AI). Various models have been presented along with an in-depth comparative analysis of the different state-of-the-art architectures, custom CNN networks, and classifiers trained on features extracted from pretrained networks. Our findings have indicated that deep learning models can achieve training accuracies of up to 99%, while both validation and test accuracies up to 96%. We conclude by suggesting various improvements that could be made to existing architectures and how AI techniques could help further improve and help in the early detection of breast cancer.
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Artificial intelligence-based retinal disease classification using optical coherence tomography imagesOptical coherence tomography (OCT) is a noninvasive imaging technology used to obtain high-resolution cross-sectional images of the retina. The layers within the retina can be differentiated and retinal thickness can be measured to aid in the early detection and diagnosis of retinal diseases and conditions. Notwithstanding the proven utility of OCT images, diagnosing large datasets of OCT images using the manual method still remains a challenge. In this chapter, we propose a deep learning-based approach, namely, the use of convolutional neural networks (CNN) and some pretrained image classification models on top of CNNs to get a proper and faster diagnosis of the OCT images. We also experiment with the features extracted using pretrained image classification models. Mainly three diseases—drusen, diabetic macular edema, choroidal neovascularization are addressed in this study. Our technique achieves an accuracy score of 0.9948 and an F1 score of 0.9948 on the test set. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when retinal diseases are suspected.
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Artificial intelligence-based skin cancer diagnosisThe first melanoma tumor has affected millions of people across the globe and taken many human lives. It can be diagnosed in its early stage, therefore it becomes very important to detect it before it becomes lethal. The melanoma skin cancer can be detected from the images of tumor by applying various techniques of deep learning. Medical science has progressed to a large extent in recent times. Its progress can be catalyzed further with the help of technology such as artificial intelligence or deep learning. In the first stage of our study, we used CNN (convolutional neural network) and transfer learning for differentiating between normal and melanoma tumors. In the next stage, features of the image were extracted from different pretrained models and then these features were passed through global average pooling layer and a classifier was put on top of it. In the first stage, that is, end-to-end learning, MobileNet architecture achieved the highest F1 score of 0.8014. In feature extraction technique, the model in which features were extracted from MobileNet architecture and XGBoost was used as a classifier achieved the highest F1 score of 0.818.
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Detection and classification of Diabetic Retinopathy Lesions using deep learningDiabetic retinopathy (DR) is a frequent consequence of diabetes mellitus that induces retinal lesions, which affect vision. DR can lead to poor vision and blindness if not treated quickly. Unfortunately, DR is not reversible, and therapy just prolongs vision. As a result, tools are needed that initially identify and prevent poor vision in diabetics at an early stage. Early identification and treatment of DR can decrease the risk of vision loss considerably. Unlike computer-aided diagnosis systems, the manual diagnosis of DR retina fundus images by ophthalmologists is time-consuming and is prone to misdiagnosis. Recent technological advances have brought optical imaging systems to the market in relation to smartphones, which allows for low power, DR viewing in a variety of settings. On the other hand, deep learning (DL) has recently emerged as one of the most widely used approaches for improving performance in a variety of fields, including medical image analysis and classification. The purpose of this chapter is to use DL models to create an automated DR detection for the modern eye model. Moreover, DL models are implemented with the color fundus retina images. Transfer learning models such as InceptionResNet, VGG, and DenseNet architectures are also utilized for the color fundus retina image analysis. F1 scores, accuracy, area under the receiver operating characteristic curve (AUC - Area under the ROC Curve), and Kappa score are utilized to measure the performance of DL models for DR detection. It contributes significantly to improve DR identification by using different artificial intelligence (AI) methods with a variety of the color fundus retina public datasets.
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Diagnosis of breast cancer from histopathological images with deep learning architecturesBreast cancer is one of the most common cancer types among women worldwide. If not treated in earlier stages, it may be fatal. Therefore early diagnosis of breast cancer can minimize the human life risk. Mammograms and ultrasound imaging technologies play a crucial role to detect intraductal papillomas. However, the determination process of intraductal papillomas requires histopathological image analysis which may be mostly time-consuming, subjective, and tedious if carried out manually by the experts. To cover issue, computer-aided diagnosis (CAD) systems came into consideration. However, earlier CAD systems could not achieve significant improvement in the diagnosis process and their usage of them did not become widespread for more than a decade. Since deep learning has made so many significant advances in a wide variety of image applications, CAD systems that use its principles perform as well as the experts in stand-alone mode, and even perform better when used in support mode. In this chapter, we utilized various deep learning architectures for the detection process of breast cancer on the invasive ductal carcinoma (IDC) dataset which is one of the most popular and remarkable datasets in this field. According to the results, the pretrained VGG16 and MobileNet architectures obtain the best detection performance, reaching nearly 92% classification accuracy.
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Magnetic resonance imagining-based automated brain tumor detection using deep learning techniquesA brain tumor refers to an accumulation or growth of anomalous cells in the brain. The mass can either be benign (noncancerous) or malignant (cancerous). Cancerous brain tumors are the source of morbidity for which diagnosis and treatment require extensive resource allocation, experienced and skilled radiologists and doctors, and sophisticated diagnostic and therapeutic technology. Early detection of brain tumors is the basic requirement for the treatment of the patient. Manual detection of the brain tumor is an invasive process and hence is extremely risky. So, advancements in medical imaging techniques, such as magnetic resonance imagining (MRI), have proved to be an important tool in the early detection of brain tumors. Even with the advancements in medical imaging, it remains a very exigent task for radiologists. In many scenarios, the unavailability of a skilled radiologist or doctor can lead to improper diagnosis of the patient. Artificial intelligence and computer vision have successfully been used to achieve human-like accuracy in various image classification tasks. In this chapter, we propose various algorithms to detect the presence of brain tumors in MRI scans of the brain. We use various state-of-the-art convolutional neural networks and apply transfer learning to achieve this goal. We have also used machine learning algorithms that were trained on the embedding of the MRI scans, which were acquired through deep feature extraction, to detect the presence of abnormalities. Moreover, this chapter compares the performance of various models and techniques for automatic brain tumor detection using deep learning.
