The rise of the Internet of Things (IoT) has driven significant advancements across sectors such as urbanization, manufacturing, and healthcare, all of which are focused on enhancing quality of life and stimulating the global economy. This survey offers an in-depth analysis of the integration of blockchain technology with IoT, addressing aspects such as architectural alignment, applications, security, limitations, scalability, and latency. Moreover, this survey focuses on security, integration techniques, and future research directions. The primary contributions of this review include a taxonomy of security concerns specific to IoT, an analysis of integration methods, and insights into consensus mechanisms suitable for resource-constrained environments. These findings highlight the unique challenges and opportunities in IoT–blockchain integration, providing a foundation for advancing secure and scalable IoT applications. By exploring consensus mechanisms and resource-constrained deployments, this paper provides a framework for developing secure and efficient IoT applications utilizing blockchain technology and providing a basis for future research and practical applications. In addition, this survey investigates innovative trends, including AI-driven blockchain for IoT.

As AI advances, new applications and services are being developed to enhance current services. With the emergence of digital twin technology (DT), an exact digital replica of any natural system or physical object/state can be created. DT incorporates several machines and deep learning-based approaches to efficiently analyze and process the collected data in order to understand the system pattern better. Consequently, Health 4.0 applications can get advantages from these cutting-edge technologies. In this article, we focus on Intracranial haemorrhage (ICH) which is a life-threatening emergency that needs immediate diagnosis and treatment. ICH is caused by the bleeding inside the skull or brain. It is classified in five main subtypes; a) subdural hemorrhage (SH), b) epidural haemorrhage (EH), c) intraparenchymal haemorrhage (IH), d) subarachnoid haemorrhage (SAH) and e) intraventricular hemorrhage (IVH). Radiologists typically examine computed tomography (CT) scan of the patients to determine the ICH and its subtype. However, manual assessment of the CT scan is a complex and time-consuming task. Pre-trained convolutional neural networks (CNNs) are state-of-the-art for the ICH classification. But they suffer from the curse of dimensionality and use redundant and noisy features which causes memory and computation overhead. The problem of imbalanced data poses the challenge for achieving model generalization. This paper proposes a hybrid attention-based ResNet architecture for ICH detection and classification. Principal component analysis (PCA) is used for dimensionality reduction and redundant feature removal whereas xtreme gradient boosting (XGBoost) is used for ICH classfication. The class imbalance problem is resolved using deep convolutional generative adversarial network (DCGAN). The proposed system is evaluated using the dataset assembled during Radiologist Society of North America (RSNA) ICH detection challenge 2019. The findings show that our proposed model outperforms existing state-of-the-art models in terms of accuracy and F1-score. ICH classification achieved accuracies of 99.3%, 97.1%, 96.7%, and 96.1%, for detecting EH, IH, IVH, SAH, and SH subtypes respectively, while F1-score is 96.1%, which is also best when compared with the benchmark studies.

The emergence of the Internet of Things (loT) has led to remarkable advancements in various sectors such as urbanization, manufacturing, and healthcare, intending to enhance the quality of life and contribute to the world economy. However, by the conclusion of 2024, ti is projected that the global network of the Internet of Things (IoT) will encompass over 207billion interconnected devices. The immense data collected and exchanged are often stored in centralized servers, leading to vulnerabilities such as data breaches. Blockchain technology, originally developed for cryptocurrency transactions, provides a possible solution for these challenges with its decentralized, immutable, and encrypted features. This survey provides a comprehensive discussion regarding the fusion of Blockchain technology and the loT, covering architectural alignment, application, security, and limitations. The contributions of this survey include an overview of Blockchain technology and its secure applications, the integration of Blockchain into IoT , an examination of suitable methods for Blockchain integration,and a taxonomy or security and limitations of loT and blockchain integration.