Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/334520
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dc.coverage.spatialSwarm intelligence based feature Selection methods and classifiers For cervical cancer classification in Gene expression
dc.date.accessioned2021-08-03T09:20:50Z-
dc.date.available2021-08-03T09:20:50Z-
dc.identifier.urihttp://hdl.handle.net/10603/334520-
dc.description.abstractIn the encompassing world, cervical squamous cell carcinoma is foresighted to be ubiquitous cancer having fourth rank and also obligatory death among women. According to World Health Organization (WHO), 86% of cervical cancer cases are reported across developing countries. Meanwhile, prostrate income countries account for the highest monotonous antecedent pertaining to cancer mortality. This cervical carcinoma springs gainst the cervix owing to the aberrant proliferation of cells with the potentiality to plunder or pervade supplementary organs of the body. Gene expression profiling is still extensively employed in cervical cancer research. Gene expression patterns have been explored in biological networks, especially a gene co-expression network that crops up as an offbeat comprehensive technique for microarray investigation. In gene co-expression networks, a part of physiognomy remains disproportionate at the moment of peculiar biological complication is resolved. Feature selection is considered a crucial aspect in identifying a portion of genes that yields better results based on this subset of features. Traditional gene selection techniques could not cultivate the finest potential series of genes which might reduce the accuracy of the classifier. For gene selection, numerous researchers have promoted optimization techniques. The new feature selection algorithm complies based on the metaheuristic algorithm and it is appropriate for choosing a subset of genes. Traditional Differential Co-expression Networks (DCNs) speculate that the underlying gene expression related to Differentially Expressed Genes (DEGs) and normal data are not evenly distributed. However, the optimal selection of genes or recurrence genes in the prediction also becomes a very crucial task newline
dc.format.extentxxv, 190p
dc.languageEnglish
dc.relationp.172-189
dc.rightsuniversity
dc.titleSwarm intelligence based feature Selection methods and classifiers For cervical cancer classification in Gene expression
dc.title.alternative
dc.creator.researcherGeeitha S
dc.subject.keywordClinical Pre Clinical and Health
dc.subject.keywordClinical Medicine
dc.subject.keywordRadiology Nuclear Medicine Medical Imaging
dc.subject.keywordcervical cancer
dc.subject.keywordGene expression
dc.description.note
dc.contributor.guideThangamani M
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2020
dc.date.awarded2020
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf43.13 kBAdobe PDFView/Open
03_vivaproceedings.pdf77.78 kBAdobe PDFView/Open
04_bonafidecertificate.pdf49.54 kBAdobe PDFView/Open
05_abstracts.pdf138.57 kBAdobe PDFView/Open
06_acknowledgements.pdf62.12 kBAdobe PDFView/Open
07_contents.pdf316.24 kBAdobe PDFView/Open
08_listoftables.pdf129.6 kBAdobe PDFView/Open
09_listoffigures.pdf166.74 kBAdobe PDFView/Open
10_listofabbreviations.pdf325.33 kBAdobe PDFView/Open
11_chapter1.pdf583.26 kBAdobe PDFView/Open
12_chapter2.pdf240.6 kBAdobe PDFView/Open
13_chapter3.pdf469.55 kBAdobe PDFView/Open
14_chapter4.pdf742.67 kBAdobe PDFView/Open
15_chapter5.pdf544.49 kBAdobe PDFView/Open
17_conclusion.pdf18.62 kBAdobe PDFView/Open
18_appendices.pdf578.79 kBAdobe PDFView/Open
19_references.pdf208.46 kBAdobe PDFView/Open
20_listofpublications.pdf131.87 kBAdobe PDFView/Open
80_recommendation.pdf52.1 kBAdobe PDFView/Open


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