The Internet of Things (IoT) has become a popular computing technology paradigm. It is increasingly being utilized to facilitate human life processes through a variety of applications, including smart healthcare, smart grids, smart finance, and smart cities. Scalability, interoperability, security, and privacy, as well as trustworthiness, are all issues that IoT applications face. Blockchain solutions have recently been created to help overcome these difficulties. The purpose of this paper is to provide a survey and tutorial on the use of blockchain in IoT systems. The importance of blockchain technology in terms of features and benefits for constituents of IoT applications is discussed. We propose a blockchain taxonomy for IoT applications based on the most significant factors. In addition, we examine the most widely used blockchain platforms for IoT applications. Furthermore, we discuss how blockchain technology can be used to broaden the spectrum of IoT applications. Besides, we discuss the recent advances and solutions offered for IoT environments. Finally, we discuss the challenges and future research directions of the use of blockchain for the IoT.

The exponential growth in the number of smart devices connected to the Internet of Things (IoT), and associated with various IoT-based smart applications and services, raises interoperability challenges which could affect the sustainability of IoT services. IoT software applications are built using different software platforms and embedded in diverse types of terminals and sensing devices. Aiming to offer smart services over a range of network technologies that use different communication protocols. The concept of Web service with service-oriented solutions was introduced to cope with the heterogeneity of hardware and software, and to tackle issues of interoperability, flexibility and scalability. The main step of this solution was the integration of Web of Things technologies into smart device networks, with the utilization of IoT gateways. Service management is a crucial factor in sustaining service-oriented solutions in dynamic and highly scalable IoT systems, and is concerned with several issues associated with service provisioning, orchestration, composition and adaption. This work was motivated by the need for robust and flexible service management systems that can meet the requirements for the rapid scalability and heterogeneity associated with the exponential growth of IoT systems. In the literature there is no survey of service management issues and associated research efforts in the field of IoT. In this article, we identify the key requirements for managing IoT services as well as common service management platforms for IoT. We provide a thematic taxonomy based on the important factors, and investigate recent advances in service management for IoT systems. Finally, the major challenges that remain open are presented as a guide for future research directions.

Phishing is a social engineering attack and a type of cybercrime that is dangerously and constantly on the rise. Phishing attacks can impact various sectors, including governmental, social, financial, and individual businesses. Traditional methods of identifying phishing websites, such as blacklist and heuristic approaches, often fail to provide sufficient protection. Moreover, traditional techniques that combine URLs, webpage content, and external features are time-consuming, require substantial computing power, and are unsuitable for devices with limited resources. Moreover, previous research has often overlooked the critical role of identifying which features are important for detection and their impact on outcomes. Traditional methods might not fully capture the significance of individual features. To overcome this issue, this research applies feature selection techniques, specifically shapley additive explanations, with each model based primarily on the URL to improve the detection process. A dataset with over 11000+ URLs and 30 varied features of the ”Phishing Website Detection” was applied from the Kaggle repository. Then, the models, namely support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression(LR), and K-nearest neighbor, were trained and tested. Each model used shapely additive explanations (SHAP) to improve precision and interpretability by highlighting the most important features. It was tested using some key performance metrics such as accuracy, precision, recall, and F1 score. Compared to all the models that were tested, this random forest model indicates 97% accuracy. The proposed system offers an overall and interpretable solution for phishing detection that contributes to a safer digital environment.

Cardiovascular disorders cause approximately 18 million deaths annually worldwide, underscoring the urgent need for precise and rapid diagnosis. Conventional machine learning does not automate feature extraction and does not capture complex non-linear relationships in high-dimensional medical data during cardiodiagnostic predictions, not to mention current techniques also lack interpretability and transparency and thus are not useful for building clinically trusted AI-powered prediction tools for personalized biomedicine and healthcare. To fill this gap, we propose an interpretable Convolutional Neural Network (1D CNN) model for cardiodiagnostic predictions that integrates automated feature extraction and explains AI. Our approach uses a 1D CNN model composed of two convolutional layers (with 64 and 128 filters). The CNN will be trained on Cleveland Heart Disease Dataset (Kaggle) (303 instances, 14 attributes) and will undergo evaluation using accuracy, precision, recall , F1 score, and LIME-SHAP interpretability analyses. The results given attest to the remarkable results, where the model achieved 98.05% percent accuracy, 100% percent precision, 96.12% percent recall, 98.02% percent F1 score, 0.963 MCC, and 0.961 Kappa coefficient. Our results exceed several of the more modern techniques offered in the literature. LIME and SHAP analyses reveal how specific features (sex, number of major vessels, thalassemia status) drive model predictions, enhancing interpretability and aligning with clinical understanding of cardiac risk factors essential for precision medicine. This research demonstrates the potential for interpretable deep learning to transform cardiovascular diagnostics through enhanced clinical decision support systems and trustworthy AI implementation in precision medicine.

The exponential growth in the number of smart devices connected to the Internet of Things (IoT) that are associated with various IoT-based smart applications and services, raises interoperability challenges. Service-oriented architecture for IoT (SOA-IoT) solutions has been introduced to deal with these interoperability challenges by integrating web services into sensor networks via IoT-optimized gateways to fill the gap between devices, networks, and access terminals. The main aim of service composition is to transform user requirements into a composite service execution. Different methods have been used to perform service composition, which has been classified as trust-based and non-trust-based. The existing studies in this field have reported that trust-based approaches outperform non-trust-based ones. Trust-based service composition approaches use the trust and reputation system as a brain to select appropriate service providers (SPs) for the service composition plan. The trust and reputation system computes each candidate SP’s trust value and selects the SP with the highest trust value for the service composition plan. The trust system computes the trust value from the self-observation of the service requestor (SR) and other service consumers’ (SCs) recommendations. Several experimental solutions have been proposed to deal with trust-based service composition in the IoT; however, a formal method for trust-based service composition in the IoT is lacking. In this study, we used the formal method for representing the components of trust-based service management in the IoT, by using higher-order logic (HOL) and verifying the different behaviors in the trust system and the trust value computation processes. Our findings showed that the presence of malicious nodes performing trust attacks leads to biased trust value computation, which results in inappropriate SP selection during the service composition. The formal analysis has given us a clear insight and complete understanding, which will assist in the development of a robust trust system.

Early detection of gastroenterological diseases significantly improves patient outcomes and reduces late-stage diagnostic burden, yet traditional CNN models show limitations in capturing complex patterns within medical imaging datasets, prompting investigation into transformer architectures like Vision Transformer (ViT). Application of the ViT technology in detecting gastroenterological diseases with the help of medical imaging has not been fully explored, despite the promising capabilities. In this paper, the effectiveness of the ViT-B16 structure for the identification of gastrointestinal abnormalities is considered using a combined dataset of Curated Colon Dataset and HyperKvasir Dataset (10,000 images across four classes), and compared with established methodologies. Our experimental results showed that ViT-B16 performed better when compared to alternative approaches; it achieved 99.5% classification accuracy compared to 99.1% by EfficientNetB5 and 97.1% by EfficientNetB2, with other supportive performance metrics including precision (99.4%), recall (99.4%), and F1-score (99.4%), AUC values ranged from 0.99 to 1.00 across all classes, reflecting very strong discriminatory power regarding disease classification tasks. These suggest that ViT-B16 has great potential for medical diagnosis applications, especially classification tasks in healthcare, where evidence-based decision-making and model interpretability are key considerations. The model also supports sustainable healthcare through computational efficiency and reduced diagnostic burden. However, there are several challenges that have not been addressed, including addressing ethical concerns about diagnostics, improving diagnostic accuracy for underrepresented disease classes, and validating the model across diverse clinical settings, which are essential directions for future research to continue developing gastroenterological disease-detecting techniques.