Salp swarm algorithm: a comprehensive survey

Abualigah, Laith; Shehab, Mohammad; Alabool, Hamzeh

doi:10.1007/s00521-019-04629-4

Cited by 296 publications

(115 citation statements)

References 72 publications

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“…give more attention to hybrid AI systems that combined new trends of AI techniques (e.g., metaheuristic algorithm) to bene t from the different characteristics of different algorithms. For example, but not limited to, salp swarm algorithm (Abualigah et al, 2019), moth-ame optimization (Shehab et al,2019), and cuckoo search algorithm (Shehab et al,2017) are a very promising and interesting algorithm that has already been successfully applied to several problems in medical research area due to their contain simplicity, speed in searching, and simple hybridization with other algorithms.…”

Section: Should Develop Hybrid Ai Systemsmentioning

confidence: 99%

Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Alabool

Alarabiat

Habib

et al. 2020

Preprint

Self Cite

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Due to the advantages offered by AI in containment the COVID-19 pandemic, the number of AI techniques has increased greatly. Although these techniques provide an acceptable start to COVID-19 pandemic control, they differ in terms of purpose, AI synthesis methods, datasets, validation approach. This increase and diversity in the numbers of proposed AI techniques can confuse decision makers and lead them to the dilemma of what is the appropriate technique under the specific conditions. Yet, studies that assess, analyze, and summarize the unresolved problems and shortcomings of current AI techniques for COVID-19 are limited. In the existing review studies, only individual parts of AI techniques, rarely the full solution, are reviewed and examined. Thus, this study aims to present a comprehensive systematic review on the application of AI techniques in containment the COVID-19 pandemic. The applied search strategy led to include 73 papers related to the Application of AI techniques for COVID-19 published from December 2019 to May 2020. Ten applications of AI for containment COVID-19 were identified. In addition, the analysis results of the systematic review revealed five deficiencies so that future research should take them into consideration.

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Section: Should Develop Hybrid Ai Systemsmentioning

confidence: 99%

Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Alabool

Alarabiat

Habib

et al. 2020

Preprint

Self Cite

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“…Moreover, SSA is also an optimization algorithm proposed by Mirjalili et al [48] inspired by the behavior of salp chains. In recent years, the SSA was utilized to solve different optimization problems, such as feature selection [49,50], data classification [51], image segmentation [52], and others [53,54].…”

Section: Introductionmentioning

confidence: 99%

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al-qaness

Ewees

Fan

et al. 2020

JCM

375

316

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In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

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“…Maximal pattern mining finds the set of patterns whose super-patterns are infrequent. Closed SPM has become a research hotspot because of its impressive compression performance [46,47] and has been widely used in many essential fields, such as recommendation systems [48], clustering analysis [49][50][51], genetic engineering [52], disease diagnosis [53], and software engineering [54,55]. However, these studies ignored the repetitions that may contain more relevant information in long sequences.…”

Section: Related Workmentioning

confidence: 99%

NetNCSP: Nonoverlapping closed sequential pattern mining

Zhu

et al. 2020

Knowledge-Based Systems

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Salp swarm algorithm: a comprehensive survey

Cited by 296 publications

References 72 publications

Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

NetNCSP: Nonoverlapping closed sequential pattern mining

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