A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images

Abualigah, Laith; Diabat, Ali; Sumari, Putra; Gandomi, Amir H.

doi:10.3390/pr9071155

Cited by 126 publications

(56 citation statements)

References 57 publications

(68 reference statements)

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“…The machine learning approaches are employed not only for predictions in software engineering, but they are also utilized in various other fields such as text document clustering and COVID-19 predictions (Abualigah and Hanandeh 2015 ; Abualigah and Khader 2017 ; Abualigah et al. 2019 , 2021 )…”

Section: Introductionmentioning

confidence: 99%

Stacking regularization in analogy-based software effort estimation

et al. 2022

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Analogy-based estimation (ABE) estimates the effort of the current project based on the information of similar past projects. The solution function of ABE provides the final effort prediction of a new project. Many studies on ABE in the past have provided various solution functions, but its effectiveness can still be enhanced. The present study is an attempt to improve the effort prediction accuracy of ABE by proposing a solution function SABE: Stacking regularization in analogy-based software effort estimation. The core of SABE is stacking, which is a machine learning technique. Stacking is beneficial as it works on multiple models harnessing their capabilities and provides a better estimation accuracy as compared to a single model. The proposed method is validated on four software effort estimation datasets and compared with the already existing solution functions: closet analogy, mean, median and inverse distance weighted mean. The evaluation criteria used are mean magnitude of relative error (MMRE), median magnitude of relative error (MdMRE), prediction (PRED) and standard accuracy (SA). The results suggested that the SABE showed promising performance for almost all the evaluation criteria when compared with the results of the earlier studies.

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Section: Introductionmentioning

confidence: 99%

Stacking regularization in analogy-based software effort estimation

et al. 2022

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“…The trapping in a local minimum and the adjustable factors represent the main challenge against the implementation of the AI algorithms. Among AI algorithms, an algorithm termed arithmetic optimization algorithm (AOA) is developed as an effective optimization algorithm utilizing few adjustable factors and a global search strategy [43][44][45]. There are different versions of AOA utilized for various engineering problems [46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

et al. 2021

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This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

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“…In addition, different methods were employed to develop the discrete version of a continuous algorithm [35]. The metaheuristic algorithms are applied for solving complex problems in different applications such as parameter identification of solar cells [36], feature selection [37][38][39][40][41], scheduling and planning [42][43][44], disease diagnosis [45,46], clustering [47], medical applications [48][49][50], industrial applications [51][52][53][54][55], and engineering optimization [56,57].…”

Section: Introductionmentioning

confidence: 99%

DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection

et al. 2021

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In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.

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A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images

Cited by 126 publications

References 57 publications

Stacking regularization in analogy-based software effort estimation

Stacking regularization in analogy-based software effort estimation

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection

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