An augmented Snake Optimizer for diseases and COVID-19 diagnosis

Khurma, Ruba Abu; Albashish, Dheeb; Braik, Malik; Alzaqebah, Abdullah; Qasem, Ashwaq; Adwan, Omar

doi:10.1016/j.bspc.2023.104718

Cited by 18 publications

(9 citation statements)

References 37 publications

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“…In the specific context of FS, the k-NN classifier's ability to provide a clear and direct evaluation of the quality of a feature subset makes it a popular choice. This is evidenced in the works of Braik et al [62] and Khurma et al [63], among others. The selection of a base classifier for FS tasks largely depends on the specific requirements and characteristics of the application at hand.…”

Section: Objective Function Of the Proposed Mrimementioning

confidence: 81%

Advanced RIME architecture for global optimization and feature selection

Abu Khurma,

Braik,

Alzaqebah

et al. 2024

J Big Data

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The article introduces an innovative approach to global optimization and feature selection (FS) using the RIME algorithm, inspired by RIME-ice formation. The RIME algorithm employs a soft-RIME search strategy and a hard-RIME puncture mechanism, along with an improved positive greedy selection mechanism, to resist getting trapped in local optima and enhance its overall search capabilities. The article also introduces Binary modified RIME (mRIME), a binary adaptation of the RIME algorithm to address the unique challenges posed by FS problems, which typically involve binary search spaces. Four different types of transfer functions (TFs) were selected for FS issues, and their efficacy was investigated for global optimization using CEC2011 and CEC2017 and FS tasks related to disease diagnosis. The results of the proposed mRIME were tested on ten reliable optimization algorithms. The advanced RIME architecture demonstrated superior performance in global optimization and FS tasks, providing an effective solution to complex optimization problems in various domains.

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Section: Objective Function Of the Proposed Mrimementioning

confidence: 81%

Advanced RIME architecture for global optimization and feature selection

Abu Khurma,

Braik,

Alzaqebah

et al. 2024

J Big Data

Self Cite

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“…The SO algorithm flow chart is shown in Figure 11. It is suitable for solving various types of optimization problems, and has been applied to feature space search, 35 thermal error model optimization, 36 and other aspects. However, it has not yet been applied to the field of crashworthiness optimization of thin‐walled structures.…”

Section: Multi‐objective Optimizationmentioning

confidence: 99%

Crashworthiness analysis and multi‐objective optimization of Al/CFRP hybrid tube with initial damage under transverse impact

Cai,

Ma,

Wang

et al. 2023

Polymer Composites

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Damage can cause uncontrollable changes during the service of a structure, leading to a reduction in mechanical properties and ultimately to structural failure. This paper presents an experimental and numerical study of the crashworthiness of thin‐walled Al/CFRP hybrid tubes with pre‐existing transverse compression damage under transverse impact loading. Experiments shows that initial damage has a more limited effect on the energy absorption capacity of hybrid tubes under impact loading, but has a greater effect on their deformation pattern and the evolution of damage. After impact, cracks on the surface of the damaged tube propagate into multiple, discontinuous cracks with a total length similar to the length of the tube. Conversely, only short cracks are generated on the intact tube. Subsequent finite element simulations demonstrate the validity and accuracy of a coupled multi‐loads model and explore the effect of different structural parameters (winding angle and number of CFRP layers, and thickness of Al tube) on the crashworthiness of the hybrid tubes. Finally, a multi‐objective snake optimizer algorithm (MOSO) was used to obtain an optimal hybrid tube structure for multiple loading conditions in order to minimize the effect of initial damage on the crashworthiness of the hybrid tube. In comparison to the simulation outcomes for the original structure, the peak crushing force (PCF) decreased by 28.46%, whereas the specific energy absorption (SEA) increased by 44.59%.Highlight The effect of pre‐existing damage on the crashworthiness and deformation pattern of hybrid tube structures is investigated by means of experimental and numerical simulations. A multi‐step finite element model was developed using the restart method to realize the crashworthiness analysis of the hybrid tube under multiple load coupling conditions. The prediction accuracy of four surrogate models for the crashworthiness of hybrid tubes is compared and analyzed. The multi‐objective snake optimizer algorithm (MOSO) is proposed to obtain the optimum hybrid tube structure for multiple loading conditions.

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“…Further explanations and mathematical description of the ruling equation of the used algorithms can be sufficiently found in earlier literature (e.g., for AOA [60,61], BWO [62,63], FBI [64,65], SO [66,67], and TSO [68,69]).…”

Section: Plos Onementioning

confidence: 99%

Integrative soft computing approaches for optimizing thermal energy performance in residential buildings

Peng,

Chen

2023

PLoS ONE

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As is known, early prediction of thermal load in buildings can give valuable insight to engineers and energy experts in order to optimize the building design. Although different machine learning models have been promisingly employed for this problem, newer sophisticated techniques still require proper attention. This study aims at introducing novel hybrid algorithms for estimating building thermal load. The predictive models are artificial neural networks exposed to five optimizer algorithms, namely Archimedes optimization algorithm (AOA), Beluga whale optimization (BWO), forensic-based investigation (FBI), snake optimizer (SO), and transient search algorithm (TSO), for attaining optimal trainings. These five integrations aim at predicting the annual thermal energy demand. The accuracy of the models is broadly assessed using mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) indicators and a ranking system is accordingly developed. As the MAPE and R2 reported, all obtained relative errors were below 5% and correlations were above 92% which confirm the general acceptability of the results and all used models. While the models exhibited different performances in training and testing stages, referring to the overall results, the BWO emerged as the most accurate algorithm, followed by the AOA and SO simultaneously in the second position, the FBI as the third, and TSO as the fourth accurate model. Mean absolute error (MAPE) and Considering the wide variety of artificial intelligence techniques that are used nowadays, the findings of this research may shed light on the selection of proper techniques for reliable energy performance analysis in complex buildings.

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An augmented Snake Optimizer for diseases and COVID-19 diagnosis

Cited by 18 publications

References 37 publications

Advanced RIME architecture for global optimization and feature selection

Advanced RIME architecture for global optimization and feature selection

Crashworthiness analysis and multi‐objective optimization of Al/CFRP hybrid tube with initial damage under transverse impact

Integrative soft computing approaches for optimizing thermal energy performance in residential buildings

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