Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However, this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19 diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial process. There are multiple criteria requires to evaluate and some of the criteria are conflicting with each other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS. The SVM (linear) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value. INDEX TERMS COVID19 diagnostic, machine learning, benchmarking methodology, chest X-rays images, entropy, TOPSIS, multi-criteria decision-making. The associate editor coordinating the review of this manuscript and approving it for publication was Zheng Xiao .

COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID‐19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID‐19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID‐19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID‐19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID‐19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID‐19 and inspire their works for future development.

Systems based on fog computing produce massive amounts of data; accordingly, an increasing number of fog computing apps and services are emerging. In addition, machine learning (ML), which is an essential area, has gained considerable progress in various research domains, including robotics, neuromorphic computing, computer graphics, natural language processing (NLP), decision-making, and speech recognition. Several researches have been proposed that study how to employ ML to settle fog computing problems. In recent years, an increasing trend has been observed in adopting ML to enhance fog computing applications and provide fog services, like efficient resource management, security, mitigating latency and energy consumption, and traffic modeling. Based on our understanding and knowledge, there is no study has yet investigated the role of ML in the fog computing paradigm. Accordingly, the current research shed light on presenting an overview of the ML functions in fog computing area. The ML application for fog computing become strong end-user and high layers services to gain profound analytics and more smart responses for needed tasks. We present a comprehensive review to underline the latest improvements in ML techniques that are associated with three aspects of fog computing: management of resource, accuracy, and security. The role of ML in edge computing is also highlighted. Moreover, other perspectives related to the ML domain, such as types of application support, technique, and dataset are provided. Lastly, research challenges and open issues are discussed.INDEX TERMS Fog computing, machine learning, Internet of Things (IoT), applications.

The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor (k-NN), Decision Tree (DT), and CN 2 rule inducer techniques) and deep learning models (e.g., MobileNets V2, ResNet50, GoogleNet, DarkNet and Xception). A large X-ray dataset has been created and developed, namely the COVID-19 vs. Normal (400 healthy cases, and 400 COVID cases). To the best of our knowledge, it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases. Based on the results obtained from the experiments, it can be concluded that all the models performed well, deep learning models had achieved the optimum accuracy of 98.8% in ResNet50 model. In comparison, in traditional machine learning techniques,

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The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.

This paper proposes a clustering approach to parameter estimation in distributed sensor networks. The proposed approach is an alternative to the conventional centralized and decentralized approaches. This is made possible by the unique adaptive estimation architecture, U-SHAPE, stemming from set-membership adaptive ltering. At the expense of a slightly degraded mean-square error performance (comparing to the least-squares approach), the proposed approach offers improved data processing exibility in a distributed sensor network, reduced signal processing hardware and reduced communication bandwidth and power requirements.

These days, the usage of machine-learning-enabled dynamic Internet of Medical Things (IoMT) systems with multiple technologies for digital healthcare applications has been growing progressively in practice. Machine learning plays a vital role in the IoMT system to balance the load between delay and energy. However, the traditional learning models fraud on the data in the distributed IoMT system for healthcare applications are still a critical research problem in practice. The study devises a federated learning-based blockchain-enabled task scheduling (FL-BETS) framework with different dynamic heuristics. The study considers the different healthcare applications that have both hard constraint (e.g., deadline) and resource energy consumption (e.g., soft constraint) during execution on the distributed fog and cloud nodes. The goal of FL-BETS is to identify and ensure the privacy preservation and fraud of data at various levels, such as local fog nodes and remote clouds, with minimum energy consumption and delay, and to satisfy the deadlines of healthcare workloads. The study introduces the mathematical model. In the performance evaluation, FL-BETS outperforms all existing machine learning and blockchain mechanisms in fraud analysis, data validation, energy and delay constraints for healthcare applications.