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dc.contributor.authorDey, Alokananda
dc.contributor.authorDey, Sandip
dc.contributor.authorBhattacharyya, Siddhartha
dc.contributor.authorPlatoš, Jan
dc.contributor.authorSnášel, Václav
dc.date.accessioned2020-05-01T07:56:06Z
dc.date.available2020-05-01T07:56:06Z
dc.date.issued2020
dc.identifier.citationApplied Soft Computing. 2020, vol. 88, art. no. 106040.cs
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.urihttp://hdl.handle.net/10084/139451
dc.description.abstractThis paper is intended to identify the optimal number of clusters automatically from an image dataset using some quantum behaved nature inspired meta-heuristic algorithms. Due to the lack of sufficient information, it is difficult to identify the appropriate number of clusters from a dataset, which has enthused the researchers to solve the problem of automatic clustering and to open up a new era of cluster analysis with the help of several natures inspired meta-heuristic algorithms. In this paper, three quantum inspired meta-heuristic techniques, viz., Quantum Inspired Particle Swarm Optimization (QIPSO), Quantum Inspired Spider Monkey Optimization (QISMO) and Quantum Inspired Ageist Spider Monkey Optimization (QIASMO), have been proposed. A comparison has been outlined between the quantum inspired algorithms with their corresponding classical counterparts. The efficiency of the quantum inspired algorithms has been established over their corresponding classical counterparts with regards to fitness, mean, standard deviation, standard errors of fitness, convergence curves (for benchmarked mathematical functions) and computational time. Finally, the results of two statistical superiority tests, viz., t- test and Friedman test have been provided to prove the superiority of the proposed methods. The superiority of the proposed methods has been established on five publicly available real life image datasets, five Berkeley image datasets of different dimensions and four benchmark mathematical functions both visually and quantitatively.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesApplied Soft Computingcs
dc.relation.urihttp://doi.org/10.1016/j.asoc.2019.106040cs
dc.rights© 2019 Elsevier B.V. All rights reserved.cs
dc.subjectautomatic clusteringcs
dc.subjectcluster validity indicescs
dc.subjectquantum computingcs
dc.subjectmeta-heuristic algorithmscs
dc.subjectageist spider monkeycs
dc.subjectspider monkeycs
dc.subjectparticle swarm optimizationcs
dc.subjectt-testcs
dc.subjectFriedman testcs
dc.titleNovel quantum inspired approaches for automatic clustering of gray level images using Particle Swarm Optimization, Spider Monkey Optimization and Ageist Spider Monkey Optimization algorithmscs
dc.typearticlecs
dc.identifier.doi10.1016/j.asoc.2019.106040
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume88cs
dc.description.firstpageart. no. 106040cs
dc.identifier.wos000515094200044


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