Opposition-based Laplacian Equilibrium Optimizer with application in Image Segmentation using Multilevel Thresholding

Dinkar, Shail Kumar; Deep, Kusum; Mirjalili, Seyedali; Thapliyal, S.

doi:10.1016/j.eswa.2021.114766

Cited by 60 publications

(24 citation statements)

References 65 publications

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“…The exponential term F demonstrates that it assists EO with attaining the appropriate balance amongst diversification as well as intensification [ 24 ]. The term λ has been an arbitrary value from the intervals 0 and 1 for controlling the turnover rate from real control volume.…”

Section: Methodsmentioning

confidence: 99%

Design of Metaheuristic Optimization-Based Vascular Segmentation Techniques for Photoacoustic Images

Vaiyapuri

Dutta²,

Sikkandar

et al. 2022

Contrast Media & Molecular Imaging

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Biomedical imaging technologies are designed to offer functional, anatomical, and molecular details related to the internal organs. Photoacoustic imaging (PAI) is becoming familiar among researchers and industrialists. The PAI is found useful in several applications of brain and cancer imaging such as prostate cancer, breast cancer, and ovarian cancer. At the same time, the vessel images hold important medical details which offer strategies for a qualified diagnosis. Recently developed image processing techniques can be employed to segment vessels. Since vessel segmentation on PAI is a difficult process, this paper employs metaheuristic optimization-based vascular segmentation techniques for PAI. The proposed model involves two distinct kinds of vessel segmentation approaches such as Shannon’s entropy function (SEF) and multilevel Otsu thresholding (MLOT). Moreover, the threshold value and entropy function in the segmentation process are optimized using three metaheuristics such as the cuckoo search (CS), equilibrium optimizer (EO), and harmony search (HS) algorithms. A detailed experimental analysis is made on benchmark PAI dataset, and the results are inspected under varying aspects. The obtained results pointed out the supremacy of the presented model with a higher accuracy of 98.71%.

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Section: Methodsmentioning

confidence: 99%

Design of Metaheuristic Optimization-Based Vascular Segmentation Techniques for Photoacoustic Images

Vaiyapuri

Dutta²,

Sikkandar

et al. 2022

Contrast Media & Molecular Imaging

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“…Recently, EOA has been successfully applied across different fields such as network reconfiguration of the power system [53], image segmentation [54], dynamic model of the fuel cell [55], parameter estimation of PV cell [56,57], and automatic voltage regulator system [58]. Despite the benefits offered, EO has the drawbacks of lacking attention on fitness assignment, and it is not able to satisfy the contradictory goals brought by the multiobjective functions simultaneously due to its high tendency of reaching equilibrium in one objective and failing in the remaining objectives.…”

Section: Optimal Feature Selectionmentioning

confidence: 99%

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

et al. 2022

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Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks and fire in photovoltaic (PV) systems. However, the issues of interest in fault detection and classification for PV systems remain an open-ended challenge due to manual and time-consuming processes that require the relevant domain knowledge and experience of fault diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures as the novel DL approaches to achieve the real-time automatic fault detection and classification of a PV system. This research employed the wavelet packet transform (WPT) as a data preprocessing technique to handle the PV voltage signal collected and feeding them as the inputs for combined DL architectures that consist of the equilibrium optimizer algorithm (EOA) and long short-term memory (LSTM-SAE) approaches. The combined DL architectures are able to extract the fault features automatically from the preprocessed data without requiring any previous knowledge, therefore can override the traditional shortages of manual feature extraction and manual selection of optimal features from the extracted fault features. These desirable features are anticipated to speed up the fault detection and classification capability of the proposed DL model with higher accuracy. In order to determine the performance of the proposed fault model, we carried out a comprehensive evaluation study on a 250-kW grid-connected PV system. In this paper, symmetrical and asymmetrical faults have been studied involving all the phases and ground faults such as single phase to ground, phases to phase, phase to phase to ground, and three-phase to ground. The simulation results validate the efficacy of the proposed model in terms of computation time, accuracy of fault detection, and noise robustness. Comprehensive comparisons between the simulation results and previous studies demonstrate the multidisciplinary applications of the present study.INDEX TERMS Deep distributed energy, equilibrium optimizer algorithm (EOA), fault detection and classification, grid-connected photovoltaic systems, optimal feature selection, wavelet packet transform (WPT).

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“…In which I t e r = c u r r e n t i t e r a t i o n , M a x _ i t e r = maximum iteration and parameters a 2 is used for controlling exploitation capability of EO [ 24 ]. For ensuring the convergence when improving local and global search capability of the method: …”

Section: The Proposed Obcsa-osae Modelmentioning

confidence: 99%

A novel oppositional binary crow search algorithm with optimal machine learning based postpartum hemorrhage prediction model

Krishnamoorthy

Liu

2022

BMC Pregnancy Childbirth

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Postpartum hemorrhage (PPH) is an obstetric emergency instigated by excessive blood loss which occurs frequently after the delivery. The PPH can result in volume depletion, hypovolemic shock, and anemia. This is particular condition is considered a major cause of maternal deaths around the globe. Presently, physicians utilize visual examination for calculating blood and fluid loss during delivery. Since the classical methods depend on expert knowledge and are inaccurate, automated machine learning based PPH diagnosis models are essential. In regard to this aspect, this study introduces an efficient oppositional binary crow search algorithm (OBCSA) with an optimal stacked auto encoder (OSAE) model, called OBCSA-OSAE for PPH prediction. The goal of the proposed OBCSA-OSAE technique is to detect and classify the presence or absence of PPH. The OBCSA-OSAE technique involves the design of OBCSA based feature selection (FS) methods to elect an optimum feature subset. Additionally, the OSAE based classification model is developed to include an effective parameter adjustment process utilizing Equilibrium Optimizer (EO). The performance validation of the OBCSA-OSAE technique is performed using the benchmark dataset. The experimental values pointed out the benefits of the OBCSA-OSAE approach in recent methods.

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Opposition-based Laplacian Equilibrium Optimizer with application in Image Segmentation using Multilevel Thresholding

Cited by 60 publications

References 65 publications

Design of Metaheuristic Optimization-Based Vascular Segmentation Techniques for Photoacoustic Images

Design of Metaheuristic Optimization-Based Vascular Segmentation Techniques for Photoacoustic Images

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

A novel oppositional binary crow search algorithm with optimal machine learning based postpartum hemorrhage prediction model

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