An Improved LNN-ACO Framework with Optimal Feature Selection for Breast Tumor Detection.
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| Title: | An Improved LNN-ACO Framework with Optimal Feature Selection for Breast Tumor Detection. |
|---|---|
| Authors: | Sappa, Neeraja1, nsreeram@gitam.in, Lingam, Greeshma1, glingam@gitam.edu |
| Source: | Engineering, Technology & Applied Science Research; Feb2026, Vol. 16 Issue 1, p30710-30715, 6p |
| Database: | Applied Science & Technology Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 192212499 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Improved LNN-ACO Framework with Optimal Feature Selection for Breast Tumor Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Sappa%2C+Neeraja%22">Sappa, Neeraja</searchLink><relatesTo>1</relatesTo>, <i>nsreeram@gitam.in</i><br /><searchLink fieldCode="AU" term="%22Lingam%2C+Greeshma%22">Lingam, Greeshma</searchLink><relatesTo>1</relatesTo>, <i>glingam@gitam.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering%2C+Technology+%26+Applied+Science+Research%22">Engineering, Technology & Applied Science Research</searchLink>; Feb2026, Vol. 16 Issue 1, p30710-30715, 6p |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=192212499 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.48084/etasr.14070 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 30710 Titles: – TitleFull: An Improved LNN-ACO Framework with Optimal Feature Selection for Breast Tumor Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sappa, Neeraja – PersonEntity: Name: NameFull: Lingam, Greeshma IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22414487 Numbering: – Type: volume Value: 16 – Type: issue Value: 1 Titles: – TitleFull: Engineering, Technology & Applied Science Research Type: main |
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