Multi-thresholding with Kapur’s Entropy—A Study Using Bat Algorithm with Different Search Operators

2020 ◽  
pp. 61-78
Author(s):  
V. Rajinikanth ◽  
Nilanjan Dey ◽  
S. Kavitha
Keyword(s):  
Bat Algorithm ◽  
Kapur’S Entropy

scholarly journals Bat algorithm for multilevel image thresholding based on Otsu and Kapur’s entropy

2021 ◽  
Vol 1982 (1) ◽  
pp. 012076
Author(s):  
Shengli Yang ◽  
Qiang Chen ◽  
Ling Peng
Keyword(s):  
Bat Algorithm ◽  
Image Thresholding ◽  
Kapur’S Entropy

Skin Melanoma Assessment Using Kapur’s Entropy and Level Set—A Study with Bat Algorithm

2018 ◽  
pp. 193-202 ◽  
Author(s):  
V. Rajinikanth ◽  
Suresh Chandra Satapathy ◽  
Nilanjan Dey ◽  
Steven Lawrence Fernandes ◽  
K. Suresh Manic
Keyword(s):  
Level Set ◽  
Bat Algorithm ◽  
Skin Melanoma ◽  
Kapur’S Entropy

A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm

2017 ◽  
Vol 16 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Siyuan Lu ◽  
Xin Qiu ◽  
Jianping Shi ◽  
Na Li ◽  
Zhi-Hai Lu ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Detection System ◽  
Bat Algorithm ◽  
Pathological Brain Detection ◽  
Learning Machine

Test Case Prioritization Using Bat Algorithm

2019 ◽  
Vol 12 ◽  
Author(s):  
Anu Bajaj ◽  
Om Prakash Sangwan
Keyword(s):  
Bat Algorithm ◽  
Test Case ◽  
Test Case Prioritization

scholarly journals A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 786
Author(s):  
Yenny Villuendas-Rey ◽  
Eley Barroso-Cubas ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Swarm Intelligence ◽  
Data Clustering ◽  
Incomplete Data ◽  
Missing Values ◽  
Clustering Algorithms ◽  
Bat Algorithm ◽  
Hybrid Features ◽  
Bee Colony ◽  
Learning Tasks ◽  
Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

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