Various resources are provided by cloud computing over the Internet, which enable plenty of applications to be employed to offer different services for industries. However, cloud computing due to the relying on a central server/datacenter has limitations such as high latency and response time, which are so crucial in real time applications like healthcare systems. To solve this, edge computing paradigm paves the way and provides pioneering solutions by moving the computational and storage resources closer to the end users. Edge computing by facilitating the real-time applications becomes more suitable for healthcare systems. This paper uses edge technology for detecting heart disease in patients utilizing a hybrid machine learning method. Although there exist some works in this area, there is still a need for improving the prediction accuracy. To this end, this paper proposes a meta-heuristic-based feature selection method using Black Widow Optimization (BWO) algorithm, and then, applies different classifiers on the selected features. The experimental results show that AdaBoost classifier along with BWO-based feature selection by 90.11 % accuracy outperforms other experimental methods, such as KNN, SVM, DT, and RF.

Abstract In recent years, cloud manufacturing (CMfg) has been developed as an intelligent manufacturing system, in which geographically distributed manufacturing resources are available as services in the cloud platform. Choosing and integrating single services into a combined service to fulfill the client's requests requires higher emphasis. However, by increasing customers' trend to utilize CMfg, service providers are encouraged to publish services with various functional and non‐functional characteristics. Thus, service composition and optimal selection has become one of the most challenging topics in CMfg. Hence, an inclusive review of current studies on this NP‐hard issue is extremely desirable. This article first, selects the recent studies in the field of single‐objective service composition in CMfg and classifies and surveys them comprehensively in terms of QoS parameters, energy consumption, user constraint, and so forth. This article aims to provide a useful roadmap for future researchers who are intended to explore novel work in this field. The search for articles was conducted in November 2020.

With the rapid growth of edge-assisted solutions in Internet of Things (IoT) networks, connected healthcare progressively relies on such solutions. This refers to systems in which all the healthcare stakeholders are connected to each other. These systems employ novel technologies, such as IoT, edge computing, and artificial intelligence (AI) to convert conventional health systems to more effective, appropriate, and customized intelligent systems. However, such systems encounter many restrictions and require new policies. By moving the computation and processing closer to the data sources and end-users, fog becomes edge computing which can reduce latency, bandwidth usage, and energy consumption. To the best of our knowledge, there is no systematic and methodological research in this scope that investigates the existing studies considering various vital and relevant factors. Thus, this survey aims to examine the state-of-the-art research in this area. We have reviewed a significant number of papers in this area and divided them into two main taxonomies, patient-centric and process-centric techniques. Furthermore, essential factors, such as available data sets and parameters like accuracy, mobility, and data rates are described and examined. Our aim is to bridge the gap between edge computing and connected healthcare solutions by discussing the challenges and highlighting future trends.