Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies

Ding, Hongwei; Cao, Xingguo; Wang, Zongshan; Dhiman, Gaurav; Hou, Peng; Wang, Jie; Li, Aishan; Hu, Xiang

doi:10.3934/mbe.2022364

Cited by 17 publications

(12 citation statements)

References 81 publications

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“…All the process has described below: In texture features, initially, by the help of image processing technique, the images are combined to find the best outcome. Finally, the GLCM technique calculates the gray image and some texture features have been removed [ 38 ]. In coronal plane, the MRI images have been divided into front and back where the images are clearly visible and easy for removal [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

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Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

Mohapatra

Sahu

Almotiri

et al. 2022

Computational Intelligence and Neuroscience

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Nowadays, so many people are living in world. If so many people are living, then the diseases are also increasing day by day due to adulterated and chemical content food. The people may suffer either from a small disease such as cold and cough or from a big disease such as cancer. In this work, we have discussed on the encephalon tumor or cancer which is a big problem nowadays. If we will consider about the whole world, then there are deficiency of clinical experts or doctors as compared to the encephalon tumor affected person. So, here, we have used an automatic classification of tumor by the help of particle swarm optimization (PSO)-based extreme learning machine (ELM) technique with the segmentation process by the help of improved fast and robust fuzzy C mean (IFRFCM) algorithm and most commonly feature reduction method used gray level co-occurrence matrix (GLCM) that may helpful to the clinical experts. Here, we have used the BraTs (“Multimodal Brain Tumor Segmentation Challenge 2020”) dataset for both the training and testing purpose. It has been monitored that our system has given better classification accuracy as an approximation of 99.47% which can be observed as a good outcome.

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Section: Methodsmentioning

confidence: 99%

“…In texture features, initially, by the help of image processing technique, the images are combined to find the best outcome. Finally, the GLCM technique calculates the gray image and some texture features have been removed [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

Mohapatra

Sahu

Almotiri

et al. 2022

Computational Intelligence and Neuroscience

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“…In [35], a novel MRPP method based on metaheuristic algorithms was proposed. Firstly, they defined the concept of velocity based on particle movement.…”

Section: Related Workmentioning

confidence: 99%

Opposition-Based Learning Equilibrium Optimizer with Application in Mobile Robot Path Planning

Wang,

Ding

2023

Int. J. Robot. Autom. Technol.

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The objective of mobile robot path planning (MRPP) is to devise the shortest obstacle-free path for autonomous mobile robots based on a given terrain. Numerous MRPP methods have been extensively researched. This paper presents a novel approach called Opposition-based Learning Equilibrium Optimizer (OEO) for generating smooth paths for mobile robots. The fundamental idea behind OEO is to introduce an opposition-based learning mechanism while maintaining the overall framework of the basic EO algorithm. This modification alleviates the susceptibility of the basic EO algorithm to local optima. The OEO algorithm is employed to provide smooth paths for autonomous mobile robots, and the results are compared with several classical metaheuristic algorithms. Comparative analysis across different environments demonstrates that the proposed OEO-based path planning method consistently yields the shortest and most collision-free paths with superior stability.

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“…ere are 2 categories of breast cancer: intrusive and non-intrusive. Ductal carcinoma in situ is a non-intrusive variety, whereas the rest are intrusive [10][11][12][13].…”

Section: Categories Of Breast Cancermentioning

confidence: 99%

A Supervised Learning Identification System for Prognosis of Breast Cancer

Rawat

Gulati

Kaur

et al. 2022

Mathematical Problems in Engineering

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Breast cancer is one of the most dangerous cancers, accounting for a large number of fatalities each year. It is the leading cause of mortality among women globally. It is getting a lot of interest in the scientific community because of its possible life-threatening danger. As a consequence, many machine learning methods (MLMs) have been modified to provide the best results for early diagnosis of this malignancy. Machine learning methods (MLMs) offer several beneficial implications in breast cancer, including early prognosis, detection, and diagnosis. Compared to traditional statistical analysis, machine learning methods (MLMs) have the capacity to improve the analysis of various health data, such as unstructured, complicated, and noisy data. With the demanding prevalence of breast cancer and the arrival of “data reformation,” it is thus imperative to mention the ethical consequences of machine learning (ML) on society and cancer care. It offers conclusively strong tools, smart methods, and efficient algorithms that can help in the prognosis of breast cancer. The focus of this review is on supervised techniques such as classification and regression that may be implemented and used for breast cancer data analysis. Some supervised learning methods like Naive Bayes, AdaBoost, and support vector machine are presented in this work in the early identification of breast cancer. These algorithms have been analyzed for their accuracy and efficiency using various assessment metrics and methods.

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Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies

Cited by 17 publications

References 81 publications

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

Opposition-Based Learning Equilibrium Optimizer with Application in Mobile Robot Path Planning

A Supervised Learning Identification System for Prognosis of Breast Cancer

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