In the context of healthcare, big data refers to a complex compilation of digital medical data collected from many sources that are difficult to manage with normal technology and software due to its size and complexity. These big data are useful in various aspects of healthcare, such as disease diagnosis, early prevention of diseases, and predicting epidemics. Even though medical big data has many advantages and a lot of potential for revolutionizing healthcare, it also has a lot of drawbacks and problems, of which security and privacy are of the utmost concern, owing to the severity of the complications once the medical data is compromised. On the other hand, it is evident that existing security and privacy safeguards in healthcare organizations are insufficient to protect their massive, big data repositories and ubiquitous environment. Thus, motivated by the synthesizing of the current knowledge pertaining to the security and privacy of medical big data, including the countermeasures, in the study, firstly, we provide a comprehensive review of the security and privacy of medical big data, including countermeasures. Secondly, we propose a novel cloud-enabled hybrid access control framework for securing the medical big data in healthcare organizations, and the result of this research indicates that the proposed access control model can withstand most cyber-attacks, and it is also proven that the proposed framework can be utilized as a primary base to build secure and safe medical big data solutions. Thus, we believe this research would be useful for future researchers to comprehend the knowledge on the security and privacy of medical big data and the development of countermeasures.

In Africa, significant changes are occurring through technological advancements in agriculture and healthcare, offering the potential for a better and wealthier future. This paper investigates the societal and ethical consequences of technology-enhanced agriculture and healthcare in Africa. The study illuminates the distinctive factors that differentiate the African circumstance from other regions. Advancements in eHealth and data-driven agriculture are transforming African food production, and healthcare services are explored in detail. Furthermore, these advancements tackle existing problems, including food security, public health, and development. Nevertheless, the identified and presented study also raises ethical and societal concerns. It surveys the societal and ethical consequences of technology-enhanced agriculture and healthcare in Africa, highlighting the distinct factors that set the African context apart from other continents. Finally, it summarizes critical insights attained during the study that will be fundamental to researchers, students, and other key stakeholders in making Africa smarter.

In Islamic education, the infusion of digital tools is reshaping conventional paradigms, demanding the cultivation of digital competence (DC) as a pivotal skill. Balancing technology with cultural and religious sensitivities requires finesse from scholars and learners. This chapter underscores DC's enduring importance in lifelong learning, validating its applicability in Islamic education through a study involving 122 participants from Indonesia, Uganda, and Malaysia. Using quantitative surveys, the research affirms DigComp 2.1's validity and reliability in this context. Notably, learners exhibit a moderate level of DC across its five domains without discernible gender-based differences. These findings advocate for the seamless integration of DC and contemporary skills into Islamic education, urging educators and policymakers to recognise the compatibility of digital tools with Islamic ideals. This study serves as a foundational pillar for shaping educational strategies that harmonise lifelong learning at the intersection of Islamic principles and technological progress.

The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (AI) have sparked a revolution in the agricultural industry, with applications ranging from crop and livestock monitoring to yield optimization, crop grading and sorting, pest and disease identification, and pesticide spraying among others. By leveraging these innovative techniques, sustainable farming practices are being adopted to ensure future food security. With the help of CV, AI, and related methods, such as Machine Learning (ML) together with Deep Learning (DL), key stakeholders can gain invaluable insights into the performance of agricultural and farm initiatives, enabling them to make data-driven decisions without the need for direct interaction. This chapter presents a comprehensive overview of the requirements, techniques, applications, and future directions for smart farming and agriculture. Different vital stakeholders, researchers, and students who have a keen interest in this field would find the discussions in this chapter insightful.