Mutation based test generation using search based social group optimization approach

2021 ◽  
Author(s):  
Shweta Rani ◽  
Bharti Suri
Keyword(s):  
Test Generation ◽  
Social Group ◽  
Optimization Approach ◽  
Social Group Optimization

Stroke Lesion Segmentation and Analysis using Entropy/Otsu’s Function – A Study with Social Group Optimization

2019 ◽  
Vol 14 (4) ◽  
pp. 305-313 ◽  
Author(s):  
Suresh Chandra Satapathy ◽  
Steven Lawrence Fernandes ◽  
Hong Lin
Keyword(s):  
Social Group ◽  
Research Work ◽  
Lesion Segmentation ◽  
Permanent Disability ◽  
Brain Abnormality ◽  
Inspection Process ◽  
Stroke Lesion ◽  
Social Group Optimization ◽  
The Brain ◽  
Otsu’S Function

Background: Stroke is one of the major causes for the momentary/permanent disability in the human community. Usually, stroke will originate in the brain section because of the neurological deficit and this kind of brain abnormality can be predicted by scrutinizing the periphery of brain region. Magnetic Resonance Image (MRI) is the extensively considered imaging procedure to record the interior sections of the brain to support visual inspection process. Objective: In the proposed work, a semi-automated examination procedure is proposed to inspect the province and the severity of the stroke lesion using the MRI. associations while known disease-lncRNA associations are required only. Method: Recently discovered heuristic approach called the Social Group Optimization (SGO) algorithm is considered to pre-process the test image based on a chosen image multi-thresholding procedure. Later, a chosen segmentation procedure is considered in the post-processing section to mine the stroke lesion from the pre-processed image. Results: In this paper, the pre-processing work is executed with the well known thresholding approaches, such as Shannon’s entropy, Kapur’s entropy and Otsu’s function. Similarly, the postprocessing task is executed using most successful procedures, such as level set, active contour and watershed algorithm. Conclusion: The proposed procedure is experimentally inspected using the benchmark brain stroke database known as Ischemic Stroke Lesion Segmentation (ISLES 2015) challenge database. The results of this experimental work authenticates that, Shannon’s approach along with the LS segmentation offers superior average values compared with the other approaches considered in this research work.</P>

scholarly journals Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

2020 ◽  
Vol 12 (5) ◽  
pp. 1011-1023 ◽  
Author(s):  
Nilanjan Dey ◽  
V. Rajinikanth ◽  
Simon James Fong ◽  
M. Shamim Kaiser ◽  
Mufti Mahmud
Keyword(s):  
Computed Tomography ◽  
Social Group ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Computed Tomography Images ◽  
Kapur’S Entropy ◽  
Benchmark Datasets ◽  
Social Group Optimization ◽  
Novel Coronavirus ◽  
Segmentation Task

Abstract The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19–affected CTI using social group optimization–based Kapur’s entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis–based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.

scholarly journals Social-Group-Optimization Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

2020 ◽  
Author(s):  
Nilanjan Dey ◽  
V. Rajinikant ◽  
Simon James Fong ◽  
M. Shamim Kaiser ◽  
Mufti Mahmud
Keyword(s):  
Computed Tomography ◽  
Social Group ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Computed Tomography Images ◽  
Global Pandemic ◽  
Benchmark Datasets ◽  
Social Group Optimization ◽  
Novel Coronavirus ◽  
Segmentation Task

The Coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared as a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a Machine Learning based pipeline to detect the COVID-19 infection using the lung Computed Tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19 affected CTI using Social-Group-Optimization and Kapur&rsquo;s Entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection and fusion to classify the infection. PCA based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test and validate four different classifiers namely Random Forest, k-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (&gt; 91%) for the morphology-based segmentation task and for the classification task the KNN offers the highest accuracy among the compared classifiers (&gt; 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose the ongoing COVID-19 infection.

scholarly journals Constrained Optimization of Linear Antenna Arrays using Novel Social Group Optimization Algorithm

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1184-1187
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Optimization Algorithm ◽  
Social Group ◽  
Design Problem ◽  
Research Problem ◽  
Linear Antenna ◽  
Array Optimization ◽  
Special Distribution ◽  
Social Group Optimization

Antenna array optimization is a major research problem in the field of electromagnetic and antenna engineering. The optimization typically involves in handling several radiation parameters like Sidelobe level (SL) and beamwidth (BW). In this paper, the linear antenna array (LAA) configuration is considered with symmetrical distribution of excitation and special distribution. The objective of the design problem considered involves in generating optimized patterns in terms of SLL and BW and check the robustness of the social group optimization algorithm (SGOA). The analysis of the design problem is carried out in terms of radiation pattern plots. The simulation is carried out in Matlab.

scholarly journals A comparative study of social group optimization with a few recent optimization algorithms

2020 ◽  
Author(s):  
Anima Naik ◽  
Suresh Chandra Satapathy
Keyword(s):  
Global Optimization ◽  
Social Group ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
Search Optimization ◽  
The Social ◽  
Complex Optimization ◽  
Social Group Optimization ◽  
New Algorithms

Abstract From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.

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