With the ubiquitous low-cost sensor devices and widespread use of IoT, the paradigm is shifted from urban areas towards a smart city. A smart city is an urban area that uses IoT technologies to collect data and manage resources efficiently. The vision is to improve the capabilities and to solve the citizens' problems (e.g., energy consumption, transportation, recycling, intelligent security, etc.) in an efficient way. A smart city is a multidimensional term including a smart economy, smart mobility, smart living, smart environment, smart people, and smart governance. Although the concept of a smart city is increasing and currently there exist many such cities in many developed countries, one of the key challenges faced by these cities is good governance. Smart cities need smart governance to run the city in a smarter way, and effective digital governance is a solution to this end. Digital governance refers to the use of digital technology in government practices.

Background/Objectives: The brain serves as the central command center for the nervous system in the human body and is made up of nerve cells known as neurons. When these nerve cells grow rapidly and abnormally, it can lead to the development of a brain tumor. Brain tumors are severe conditions that can significantly reduce a person’s lifespan. Failure to detect or delayed diagnosis of brain tumors can have fatal consequences. Accurately identifying and classifying brain tumors poses a considerable challenge for medical professionals, especially in terms of diagnosing and treating them using medical imaging analysis. Errors in diagnosing brain tumors can significantly impact a person’s life expectancy. Magnetic Resonance Imaging (MRI) is highly effective in early detection, diagnosis, and classification of brain cancers due to its advanced imaging abilities for soft tissues. However, manual examination of brain MRI scans is prone to errors and heavily depends on radiologists’ experience and fatigue levels. Swift detection of brain tumors is crucial for ensuring patient safety. Methods: In recent years, computer-aided diagnosis (CAD) systems incorporating deep learning (DL) and machine learning (ML) technologies have gained popularity as they offer precise predictive outcomes based on MRI images using advanced computer vision techniques. This article introduces a novel hybrid CAD approach named ViT-PCA-RF, which integrates Vision Transformer (ViT) and Principal Component Analysis (PCA) with Random Forest (RF) for brain tumor classification, providing a new method in the field. ViT was employed for feature extraction, PCA for feature dimension reduction, and RF for brain tumor classification. The proposed ViT-PCA-RF model helps detect early brain tumors, enabling timely intervention, better patient outcomes, and streamlining the diagnostic process, reducing patient time and costs. Our research trained and tested on the Brain Tumor MRI (BTM) dataset for multi-classification of brain tumors. The BTM dataset was preprocessed using resizing and normalization methods to ensure consistent input. Subsequently, our innovative model was compared against traditional classifiers, showcasing impressive performance metrics. Results: It exhibited outstanding accuracy, specificity, precision, recall, and F1 score with rates of 99%, 99.4%, 98.1%, 98.1%, and 98.1%, respectively. Conclusions: Our innovative classifier’s evaluation underlined our model’s potential, which leverages ViT, PCA, and RF techniques, showing promise in the precise and effective detection of brain tumors.

Embedded technologies combined with the Internet of Things (IoT), have transformed healthcare monitoring systems into automated and responsive platforms. In recent decades, many existing approaches have been based on edge computing to reduce response time in patient monitoring and provide a reliable method for interaction among the medical team and experts during disease diagnosis. Such approaches are the interconnection of battery-powered devices and physical objects to capture the physiological data streams for medical treatment and facilitate personalized healthcare systems. However, as wireless devices have limited resources for fulfilling end-user requests, this affects the accuracy of the medical system, especially in the presence of malicious devices on the communication infrastructure. Under diverse network conditions, such solutions lower the reliability level of the devices and increase the likelihood of suspicious processes. Therefore, to keep these significant concerns in IoT-based healthcare applications, trust and security should be adopted while collecting patients’ data over an insecure medium. In this research study, we propose a model referred to as Edge-Cloud Trusted Intelligence (ECTI), aiming to decrease the computing overhead on the devices. Additionally, multi-level security is implemented to ensure privacy preservation by adopting trusted behavior when communicating in a distributed environment. The edges utilize resources efficiently by employing task offloading strategies, enabling lightweight collaborative decision-making for routing in the healthcare domain. The performance results revealed notable improvement of the proposed model against related schemes in terms of various network metrics.

Education is the key to success for any society. The quality of education is directly linked with the curriculum it contains, the mode of delivery, how effective it is, as well as the volume of dispersion among the society. Digital curriculum (DC) changes the tradition of old teaching and bring higher results in different domains. This chapter examines how DC has played a main role in education in society at various levels and the importance of digital curriculum in education for teachers and students. The digital curriculum has been upgraded to enhance the status of education in a new era. Change provides a greater focus on building the era education. Moreover, the effect and the future of the DC will be illustrated. This research will further bring several ways of implementing DC in different domains, different benefits of it, and its impact on the economic growth as well as in digital governance.

Intrusion detection in Internet of Things (IoT) networks, particularly in healthcare settings, poses critical challenges due to latency constraints, limited resources, and the need for trustworthy auditing in distributed environments. Centralized detection models often fail to deliver timely or scalable responses under real-world IoT conditions. This study proposes a hybrid fog–cloud architecture tailored for healthcare-oriented IoT threat detection, incorporating blockchain-based auditability. The architecture utilizes fog- and cloud-level XGBoost classifiers trained on BoT-IoT and ToN-IoT datasets, with SMOTE applied to mitigate class imbalance. A lightweight blockchain module is integrated at the fog layer to log predictions in real-time for tamper-evident traceability. Simulations were performed using 50 fog-predicted events to evaluate performance, energy usage, and blockchain entropy. The system achieved an average block creation time of under 20 ms with minimal CPU and memory overhead. It also demonstrated robustness against tampering, preserving data integrity. The fog-level model achieved competitive metrics (AUC = 1, F1-score = 98.70%, Accuracy = 99.80%) compared to the cloud model, while outperforming it in terms of response latency and localized decision-making. The proposed blockchain-integrated fog–cloud framework enables secure, low-latency, and scalable threat detection for healthcare IoT systems, offering a promising foundation for privacy-aware edge intelligence.

Background: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients’ bodies promptly using biosensors. The dynamic nature of medical devices not only enhances the data analysis in medical services and the prediction of chronic diseases, but also improves remote diagnostics with the latency-aware healthcare system. However, due to scalability and reliability limitations in data processing, most existing healthcare systems pose research challenges in the timely detection of personalized diseases, leading to inconsistent diagnoses, particularly when continuous monitoring is crucial. Methods: This work propose an adaptive and secure framework for disability identification using the Internet of Medical Things (IoMT), integrating edge computing and artificial intelligence. To achieve the shortest response time for medical decisions, the proposed framework explores lightweight edge computing processes that collect physiological and behavioral data using biosensors. Furthermore, it offers a trusted mechanism using decentralized strategies to protect big data analytics from malicious activities and increase authentic access to sensitive medical data. Lastly, it provides personalized healthcare interventions while monitoring healthcare applications using realistic health records, thereby enhancing the system’s ability to identify diseases associated with chronic conditions. Results: The proposed framework is tested using simulations, and the results indicate the high accuracy of the healthcare system in detecting disabilities at the edges, while enhancing the prompt response of the cloud server and guaranteeing the security of medical data through lightweight encryption methods and federated learning techniques. Conclusions: The proposed framework offers a secure and efficient solution for identifying disabilities in healthcare systems by leveraging IoMT, edge computing, and AI. It addresses critical challenges in real-time disease monitoring, enhancing diagnostic accuracy and ensuring the protection of sensitive medical data.