{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T08:03:11Z","timestamp":1764403391537,"version":"3.37.3"},"reference-count":24,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100020275","name":"Gedimino Technical University, Doctoral Office and Rector Office, Vilnius, Lithuania","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100020275","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3425585","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T18:48:56Z","timestamp":1720550936000},"page":"97712-97725","source":"Crossref","is-referenced-by-count":1,"title":["An Empirical Determination of Optimum Artificial Intelligence Algorithm: Detection Using Signal-to-Noise Ratio Approach"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3249-4493","authenticated-orcid":false,"given":"Nana Kwame","family":"Gyamfi","sequence":"first","affiliation":[{"name":"Department of Information Systems, Vilnius Gedimino Technical University, Vilnius, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2263-3947","authenticated-orcid":false,"given":"Nikolaj","family":"Goranin","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Vilnius Gedimino Technical University, Vilnius, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2796-9001","authenticated-orcid":false,"given":"Dainius","family":"\u010ceponis","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Vilnius Gedimino Technical University, Vilnius, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/math10152552"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44886-1_25"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-011-9230-1"},{"volume-title":"Data Mining: Practical Machine Learning Tools and Techniques","year":"2020","author":"Witten","key":"ref4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1828\/1\/012005"},{"issue":"4","key":"ref6","first-page":"447","article-title":"A review and empirical comparison of univariate outlier detection methods","volume":"37","author":"Saleem","year":"2021","journal-title":"Pakistan J. Statist."},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"volume-title":"The Phi-Coefficient, the Tetrachoric Correlation Coefficient, and the Pearson-Yule Debate","year":"2011","key":"ref8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.11613\/BM.2012.031"},{"volume-title":"Overfitting and Underfitting With Machine Learning Algorithms","year":"2019","author":"Brownlee","key":"ref11"},{"key":"ref12","article-title":"The problem with metrics is a fundamental problem for AI","author":"Thomas","year":"2020","journal-title":"arXiv:2002.08512"},{"volume-title":"An Independent Component Analysis Algorithm Using Signal Noise Ratio","year":"2012","author":"Hu","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1088\/0957-0233\/25\/11\/115301"},{"volume-title":"Signal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning","year":"2019","author":"Yuan","key":"ref15"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1364\/AO.58.004933"},{"volume-title":"The WEKA Workbench. Online Appendix for `Data Mining: Practical Machine Learning Tools and Techniques","year":"2016","author":"Frank","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/s0167-9236(02)00110-0"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/wcnc.2008.116"},{"volume-title":"System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs","year":"1987","author":"Taguchi","key":"ref20"},{"key":"ref21","first-page":"82","article-title":"Performance measures in dynamic parameter design","volume":"10","author":"Joseph","year":"2002","journal-title":"J. Jpn. Qual. Eng. Soc."},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2857506"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/2317976"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100476"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10589658.pdf?arnumber=10589658","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T04:35:02Z","timestamp":1721709302000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10589658\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3425585","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2024]]}}}