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Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early detection of COVID-19 is important for the prevention and treatment of pandemic. Numerous sources of medical images (e.g., Chest X-Rays (CXR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI)) are regarded as a desirable technique for diagnosing COVID-19 cases. Medical images of coronavirus patients show that the lungs are filled with sticky mucus that prevents them from inhaling. Today, Artificial Intelligence (AI) based algorithms have made a significant shift in the computer aided diagnosis due to their effective feature extraction capabilities. In this survey, a complete and systematic review of the application of Machine Learning (ML) methods for the detection of COVID-19 is presented, focused on works that used medical images. We aimed to evaluate various ML-based techniques in detecting COVID-19 using medical imaging. A total of 26 papers were extracted from ACM, ScienceDirect, Springerlink, Tech Science Press, and IEEExplore. Five different ML categories to review these mechanisms are considered, which are supervised learning-based, deep learning-based, active learning-based, transfer learning-based, and evolutionary learning-based mechanisms. A number of articles are investigated in each group. Also, some directions for further research are discussed to improve the detection of COVID-19 using ML techniques in the future. In most articles, deep learning is used as the ML method. Also, most of the researchers used CXR images to diagnose COVID-19. Most articles reported accuracy of the models to evaluate model performance. The accuracy of the studied models ranged from 0.84 to 0.99. The studies demonstrated the current status of AI techniques in using AI potentials in the fight against COVID-19.

The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is perhaps the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters' stochastic tuning of ELM's supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine-cosine algorithm was utilized to tune the ELM's parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network's training time is only 0.9421 milliseconds and the overall detection test time for 3100 images is 2.721 seconds.

The rapid developments in the mobile application context illuminate the demand for more resources and processing power at Smart Mobile Devices (SMDs). Mobile Cloud Computing (MCC) enables the SMDs to offload their workloads on the remote cloud servers and benefit from the MCC’s extensive resources to deal with this issue. To this end, numerous offloading schemes are provided in the literature to enhance the SMD's efficiency by offloading their workloads on the nearby cloudlets or remote cloud computing resources. This article puts forward a comprehensive survey and taxonomy of the offloading approaches designed and proposed for MCCs. It first classifies them based on the algorithms which have been used for making the offloading decisions. Then, in each category, it illuminates how the offloading decisions are made to improve application performance and mobile devices' energy efficiency, regarding offloading factors such as deadlines, costs, etc. The evaluation metrics, simulator, offloading type, and architecture of the studied schemes are compared and illuminated in each category. Furthermore, regarding the various properties of the studied offloading methods, the offloading domain's leading issues and challenges are discussed. Lastly, the concluding points are provided, and directions for the subsequent studies in the offloading context are specified.

Mobile edge computing (MEC) is an interesting technology aimed at providing various processing and storage resources at the edge of mobile devices (MDs). However, MECs contain limited resources, and they should be appropriately managed to prevent resource wastage. Workflow scheduling is a process that tries to map tasks to the most proper set of resources based on some objectives. This paper presents DBOA, a discrete version of the Butterfly Optimization Algorithm (BOA) that applies the Levy flight method to improve its convergence speed and prevent local optima problems. We also employed a task prioritization method to find the task execution order in the scientific workflows. Then, we use DBOA for Dynamic Voltage and Frequency Scaling or DVFS-based data-intensive workflow scheduling and data placement in MEC environments. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on various well-known scientific workflows with different sizes. The obtained experimental results indicate that our method can outperform other algorithms in terms of energy consumption, data access overheads, and so on.