Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics.

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Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images.

Swarm intelligence techniques have been created to respond to theoretical and practical global optimization problems. This paper puts forward an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method, by an explicit exploration mechanism and a chaotic local search strategy. The resulting augmented approach was theoretically tested on two sets of bound-constrained benchmark functions from the CEC suites and practically validated for automatically selecting the optimal dropout rate for the regularization of deep neural networks. Despite their successful applications in a wide spectrum of different fields, one important problem that deep learning algorithms face is overfitting. The traditional way of preventing overfitting is to apply regularization; the first option in this sense is the choice of an adequate value for the dropout parameter. In order to demonstrate its ability in finding an optimal dropout rate, the boosted version of the firefly algorithm has been validated for the deep learning subfield of convolutional neural networks, with respect to five standard benchmark datasets for image processing: MNIST, Fashion-MNIST, Semeion, USPS and CIFAR-10. The performance of the proposed approach in both types of experiments was compared with other recent state-of-the-art methods. To prove that there are significant improvements in results, statistical tests were conducted. Based on the experimental data, it can be concluded that the proposed algorithm clearly outperforms other approaches.

Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization

Gliomas belong to the group of the most frequent types of brain tumors. For this specific type of brain tumors, in its beginning stages, it is extremely complex to get the exact diagnosis. Even with the works from the most experienced doctors, it will not be possible without magnetic resonance imaging, which aids to make the diagnosis of brain tumors. In order to create classification of the images, to where the class of glioma belongs to, for achieving superior performance, convolutional neural networks can be used. For achieving high-level accuracy on the image classification, the convolutional network hyperparameters’ calibrations must reach a very accurate response of high accuracy results and this task proves to take up a lot of computational time and energy. Proceeding with the proposed solution, in this scientific research paper a metaheuristic method has been proposed to automatically search and target the near-optimal values of convolutional neural network hyperparameters based on hybridized version of elephant herding optimization swarm intelligence metaheuristics. The hybridized elephant herding optimization has been incorporated for convolutional neural network hyperparameters’ tuning to develop a system for automatic and instantaneous image classification of glioma brain tumors grades from the magnetic resonance imaging. Comparative analysis was performed with other methods tested on the same problem instance an results proved superiority of approach proposed in this paper.

Optimizing Convolutional Neural Network by Hybridized Elephant Herding Optimization Algorithm for Magnetic Resonance Image Classification of Glioma Brain Tumor Grade

Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users-to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives-cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results' quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches. 

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Modified firefly algorithm for workflow scheduling in cloud-edge environment

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

A novel type of coronavirus, now known under the acronym COVID-19, was initially discovered in the city of Wuhan, China. Since then, it has spread across the globe and now it is affecting over 210 countries worldwide. The number of confirmed cases is rapidly increasing and has recently reached over 14 million on July 18, 2020, with over 600,000 confirmed deaths. In the research presented within this paper, a new forecasting model to predict the number of confirmed cases of COVID-19 disease is proposed. The model proposed in this paper is a hybrid between machine learning adaptive neuro-fuzzy inference system and enhanced genetic algorithm metaheuristics. The enhanced genetic algorithm is applied to determine the parameters of the adaptive neuro-fuzzy inference system and to enhance the overall quality and performances of the prediction model. Proposed hybrid method was tested by using realistic official dataset on the COVID-19 outbreak in the state of China. In this paper, proposed approach was compared against multiple existing state-of-the-art techniques that were tested in the same environment, on the same datasets. Based on the simulation results and conducted comparative analysis, it is observed that the proposed hybrid approach has outperformed other sophisticated approaches and that it can be used as a tool for other time-series prediction.

Aleksandar Djordjevic

Papers

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Hybrid Genetic Algorithm and Machine Learning Method for COVID-19 Cases Prediction