Performance evolution of a newly developed general-use hybrid AIS-ANN system: AaA-response

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dc.contributor.authorOzsen, Seral
dc.contributor.authorGunes, Salih
dc.date.accessioned2020-03-26T18:42:50Z
dc.date.available2020-03-26T18:42:50Z
dc.date.issued2013
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, we have developed a nonlinear recognition system in the artificial immune systems (AIS) field named 'AaA-response (artificial neural network (ANN)-aided AIS-response)', which is different from previous AIS methods in that it uses a different modeling strategy in the formation of the memory response. Because it also uses ANNs in the determination of the correct output, it can be seen as a hybrid system that involves AIS and ANN approaches. Unlike the other AIS methods, AaA-response uses multiple system units (or antibodies) to form an output for a presented input. This property gives the proposed system the ability of producing the desired output values, other than just being a classification algorithm. That is, AaA-response can also be used as a regression method, like ANNs, by producing any output value for the given inputs. The parameter analyses of the system were conducted on an artificially generated dataset, the Chainlink dataset, and the important points in the parameter selection were emphasized. To investigate the performance of the system for real-world problems, the Iris dataset and Statlog Heart Disease dataset, taken from the University of California Irvine machine learning repository, were used. The system, which obtained 99.33% classification accuracy on the Iris dataset, has shown an important performance superiority with regard to the classification accuracy to other methods in the literature by reaching 90.37% classification accuracy for the Statlog Heart Disease dataset.en_US
dc.description.sponsorshipScientific Research Projects of Selcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis study was supported by the Scientific Research Projects of Selcuk University.en_US
dc.identifier.doi10.3906/elk-1201-22en_US
dc.identifier.endpage1719en_US
dc.identifier.issn1300-0632en_US
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1703en_US
dc.identifier.urihttps://dx.doi.org/10.3906/elk-1201-22
dc.identifier.urihttps://hdl.handle.net/20.500.12395/29716
dc.identifier.volume21en_US
dc.identifier.wosWOS:000325373300014en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherTUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEYen_US
dc.relation.ispartofTURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial immune systemsen_US
dc.subjectartificial neural networksen_US
dc.subjectrecognitionen_US
dc.subjectmemory responseen_US
dc.subjectlearningen_US
dc.titlePerformance evolution of a newly developed general-use hybrid AIS-ANN system: AaA-responseen_US
dc.typeArticleen_US

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