A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy

Abdel‐Basset, Mohamed; Mohamed, Reda; Elhoseny, Mohamed; Chakrabortty, Ripon K.; Ryan, Michael J.

doi:10.1109/access.2020.2990893

Cited by 151 publications

(78 citation statements)

References 59 publications

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“…The prominence of NIC across several application domains shows its potential in solving many problems. The NIC models have been applied to the diagnosis and prediction of COVID-19 [59], and they have been used for X-ray image segmentation to identify features of COVID-19 [91]. As early detection and treatment of COVID-19 is very needful, the review of the current literature suggests that nature-inspired computing models have not been explored in the contact tracing algorithms for the COVID-19 pandemic.…”

Section: Nic For Contact Tracing and Case Detectionmentioning

confidence: 99%

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Agbehadji

Awuzie

Ngowi

et al. 2020

IJERPH

199

112

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The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19’s cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.

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Section: Nic For Contact Tracing and Case Detectionmentioning

confidence: 99%

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Agbehadji

Awuzie

Ngowi

et al. 2020

IJERPH

199

112

View full text Add to dashboard Cite

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“… 12 Tang [53] This paper integrated the PSO with BFA to provide the global search capability and promote the premature convergence toward the optimal threshold values 13 Abdel-Basset [54] A novel equilibrium optimizer (EO) has been proposed for finding the optimal threshold values of an image by maximizing the Kapur’s entropy. 14 Abdel-Basset [55] A novel marine predators algorithm (IMPA) improved using the Ranking-based diversity reduction strategy has been suggested to segment the chest X-ray image 15 Chouksey [56] In this paper, the antlion optimization (ALO) and the multiverse optimization (MVO) have been developed for tackling the ISP by maximizing the Kapur’s entropy and Otsu method. After investigating the performance of ALO, and MVO, the author notified that MVO is better 16 Erik Cuevas [44] In this paper, the locust search algorithm (LSA) was applied for solving the multi-level thresholding image segmentation under a new objective function in a gaussian mixture model.…”

Section: Related Workmentioning

confidence: 99%

HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images

Abdel‐Basset

Chang

Mohamed

2020

Applied Soft Computing

Self Cite

202

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Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 — sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur’s entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur’s entropy for all the metrics used and the standard SMA could perform better than the other algorithms in the comparison under all the metrics.

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“…The MPA has been implemented to imitate the strategy of foraging optimally for the Marine Predators (MP) to detect their Prey as the following: In the case of a low concentration of prey, the MP follow the Lévy behavior. In the case of abundant Prey, the MP follow the Brownian movements' behavior [24,30]. According to the environmental effects, the velocity-ratio from the Prey to MPA can be changed based on the behaviors of Lévy and Brownian as follows: -In the low velocity-ratio < 0.1, the most suitable behavior for MP is Lévy.…”

Section: Mpamentioning

confidence: 99%

Fuel Cell Parameters Estimation via Marine Predators and Political Optimizers

et al. 2020

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Proton Exchange Membrane Fuel Cells (PEMFC) is considered a propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and an in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, recent meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Marine Predators Algorithm (MPA) and Political Optimizer (PO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W, SR-12PEM 500 W, and 250 W stack under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with those obtained in the literature. Moreover, the I/V curves obtained by the application of MPA and PO showed a clear matching with datasheet curves for all the studied cases. Statistical analysis has been performed to evaluate the robustness of the MPA and PO techniques. Finally, the PEMFC model based on the MPA technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed. The obtained results confirmed the superiority and reliability of the applied approach of the MPA algorithm. The results prove that the MPA algorithm has a superior performance based on its reliability. INDEX TERMS Fuel cell modelling, Parameter estimation, metaheuristic algorithms.

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A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy

Cited by 151 publications

References 59 publications

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images

Fuel Cell Parameters Estimation via Marine Predators and Political Optimizers

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