Jaya‐tunicate swarm algorithm based generative adversarial network for COVID‐19 prediction with chest computed tomography images

Rajan, D. Palanivel; Velliangiri, S.; S, Iwin Thanakumar Joseph; Sorna, Sona Chandra Devadass

doi:10.1002/cpe.7211

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…Chouhan et al fused TSA with the GWO algorithm and successfully solved the multi-path routing protocol problem [65]. Doraiswami et al fused TSA with the Jaya algorithm and used it for the GAN network, proposed Jaya-TSA based GAN prediction method, and used it successfully for patient prediction of COVID-19 [66]. In addition, the fusion of TSA with the BOA algorithm [67] and TSA with the FPA algorithm [68] can also effectively enhance the superior performance of each other, indicating that the combination of the respective search advantages of different algorithms can effectively balance the global exploration and local exploitation capabilities of the algorithms.…”

Section: Plos One (1) Population Diversity Enhancement Mechanismmentioning

confidence: 99%

MSHHOTSA: A variant of tunicate swarm algorithm combining multi-strategy mechanism and hybrid Harris optimization

Liu,

Guo,

Liu

et al. 2023

PLoS ONE

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This paper proposes a novel hybrid algorithm, named Multi-Strategy Hybrid Harris Hawks Tunicate Swarm Optimization Algorithm (MSHHOTSA). The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. Firstly, inspired by the idea of the neighborhood and thermal distribution map, the hyperbolic tangent domain is introduced to modify the position of new tunicate individuals, which can not only effectively enhance the convergence performance of the algorithm but also ensure that the data generated between the unknown parameters and the old parameters have a similar distribution. Secondly, the nonlinear convergence factor is constructed to replace the original random factor c1 to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. Finally, the swarm update mechanism of the HHO algorithm is introduced into the position update of the TSA algorithm, which further balances the local exploitation and global exploration performance of the MSHHOTSA. The proposed algorithm was evaluated on eight standard benchmark functions, CEC2019 benchmark functions, four engineering design problems, and a PID parameter optimization problem. It was compared with seven recently proposed metaheuristic algorithms, including HHO and TSA. The results were analyzed and discussed using statistical indicators such as mean, standard deviation, Wilcoxon’s rank sum test, and average running time. Experimental results demonstrate that the improved algorithm (MSHHOTSA) exhibits higher local convergence, global exploration, robustness, and universality than BOA, GWO, MVO, HHO, TSA, ASO, and WOA algorithms under the same experimental conditions.

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Section: Plos One (1) Population Diversity Enhancement Mechanismmentioning

confidence: 99%

MSHHOTSA: A variant of tunicate swarm algorithm combining multi-strategy mechanism and hybrid Harris optimization

Liu,

Guo,

Liu

et al. 2023

PLoS ONE

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Jaya‐tunicate swarm algorithm based generative adversarial network for COVID‐19 prediction with chest computed tomography images

Rajan¹,

Velliangiri

et al. 2022

Concurrency and Computation

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Summary A novel corona virus (COVID‐19) has materialized as the respiratory syndrome in recent decades. Chest computed tomography scanning is the significant technology for monitoring and predicting COVID‐19. To predict the patients of COVID‐19 at early stage poses an open challenge in the research community. Therefore, an effective prediction mechanism named Jaya‐tunicate swarm algorithm driven generative adversarial network (Jaya‐TSA with GAN) is proposed in this research to find patients of COVID‐19 infections. The developed Jaya‐TSA is the incorporation of Jaya algorithm with tunicate swarm algorithm (TSA). However, lungs lobs are segmented using Bayesian fuzzy clustering, which effectively find the boundary regions of lung lobes. Based on the extracted features, the process of COVID‐19 prediction is accomplished using GAN. The optimal solution is obtained by training GAN using proposed Jaya‐TSA with respect to fitness measure. The dimensionality of features is reduced by extracting the optimal features, which enable to increase the speed of training process. Moreover, the developed Jaya‐TSA based GAN attained outstanding effectiveness by considering the factors, like, specificity, accuracy, and sensitivity that captured the importance as 0.8857, 0.8727, and 0.85 by varying training data.

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An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model

Yousefpanah,

Ebadi,

Sabzekar

et al. 2024

Acta Tropica

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Jaya‐tunicate swarm algorithm based generative adversarial network for COVID‐19 prediction with chest computed tomography images

Cited by 5 publications

References 35 publications

MSHHOTSA: A variant of tunicate swarm algorithm combining multi-strategy mechanism and hybrid Harris optimization

MSHHOTSA: A variant of tunicate swarm algorithm combining multi-strategy mechanism and hybrid Harris optimization

Jaya‐tunicate swarm algorithm based generative adversarial network for COVID‐19 prediction with chest computed tomography images

An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model

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