Care4U: Integrated healthcare systems based on blockchain

During the COVID-19 crisis, the need to stay at home has increased dramatically. In addition, the number of sick people, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of patients can ensure remote healthcare at home using advanced technologies such as the Internet of Medical Things (IoMT). The IoMT can monitor and transmit sensitive health data; however, it may be vulnerable to various attacks. In this paper, an efficient healthcare security system is proposed for IoMT applications. In the proposed system, the medical sensors can transmit sensed encrypted health data via a mobile application to the doctor for privacy. Then, three consortium blockchains are constructed for load balancing of transactions and reducing transaction latency. They store the credentials of system entities, doctors' prescriptions and recommendations according to the data transmitted via mobile applications, and the medical treatment process. Besides, cancelable biometrics are used for providing authentication and increasing the security of the proposed medical system. The investigational results show that the proposed system outperforms existing work where the proposed model consumed less processing time by values of 18%, 22%, and 40%, and less energy for processing a 200 ​KB file by values of 9%, 13%, and 17%. Finally, the proposed model consumed less memory usage by values of 7%, 7%, and 18.75%. From these results, it is clear that the proposed system gives a very reliable and secure performance for efficiently securing medical applications

An Efficient Fault Diagnosis Framework for Digital Twins Using Optimized Machine Learning Models in Smart Industrial Control Systems

Abstract In recent times, digital twins (DT) is becoming an emerging and key technology for smart industrial control systems and Industrial Internet of things (IIoT) applications. The DT presently supports a significant tool that can generate a huge dataset for fault prediction and diagnosis in a real-time scenario for critical industrial applications with the support of powerful artificial intelligence (AI). The physical assets of DT can produce system performance data that is close to reality, which delivers remarkable opportunities for machine fault diagnosis for effective measured fault conditions. Therefore, this study presents an intelligent and efficient AI-based fault diagnosis framework using new hybrid optimization and machine learning models for industrial DT systems, namely, the triplex pump model and transmission system. The proposed hybrid framework utilizes a combination of optimization techniques (OT) such as the flower pollination algorithm (FPA), particle swarm algorithm (PSO), Harris hawk optimization (HHO), Jaya algorithm (JA), gray wolf optimizer (GWO), and Salp swarm algorithm (SSA), and machine learning (ML) such as K-nearest neighbors (KNN), decision tree (CART), and random forest (RF). The proposed hybrid OT–ML framework is validated using two different simulated datasets which are generated from both the mechanized triplex pump and transmission system models, respectively. From the experimental results, the hybrid FPA–CART and FPA–RF models within the proposed framework give acceptable results in detecting the most relevant subset of features from the two employed datasets while maintaining fault detection accuracy rates exemplified by the original set of features with 96.8% and 85.7%, respectively. Therefore, the results achieve good and acceptable performance compared to the other existing models for fault diagnosis in real time based on critical IIoT fields

Simultaneous Super-Resolution and Classification of Lung Disease Scans

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support