{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T08:50:13Z","timestamp":1782291013502,"version":"3.54.5"},"reference-count":27,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Mongkut\u2019s Institute of Technology Ladkrabang","award":["2564-02-05-011"],"award-info":[{"award-number":["2564-02-05-011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["International Journal of Mathematics and Mathematical Sciences"],"published-print":{"date-parts":[[2022,4,22]]},"abstract":"<jats:p>In this research, the normalization performance of the proposed adjusted min-max methods was compared to the normalization performance of statistical column, decimal scaling, adjusted decimal scaling, and min-max methods, in terms of accuracy and mean square error of the final classification outcomes. The evaluation process employed an artificial neural network classification on a large variety of widely used datasets. The best method was min-max normalization, providing 84.0187% average ranking of accuracy and 0.1097 average ranking of mean square error across all six datasets. However, the proposed adjusted-2 min-max normalization achieved a higher accuracy and a lower mean square error than min-max normalization on each of the following datasets: white wine quality, Pima Indians diabetes, vertical column, and Indian liver disease datasets. For example, the proposed adjusted-2 min-max normalization on white wine quality dataset achieved 100% accuracy and 0.00000282 mean square error. To conclude, for some classification applications on one of these specific datasets, the proposed adjusted-2 min-max normalization should be used over the other tested normalization methods because it performed better.<\/jats:p>","DOI":"10.1155\/2022\/3584406","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T19:50:27Z","timestamp":1650657027000},"page":"1-9","source":"Crossref","is-referenced-by-count":97,"title":["Performance Comparison of New Adjusted Min-Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9158-2178","authenticated-orcid":true,"given":"Saichon","family":"Sinsomboonthong","sequence":"first","affiliation":[{"name":"Department of Statistics, School of Science, King Mongkut\u2019s Institute of Technology Ladkrabang, Chalongkrung, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.5815\/ijcnis.2017.11.04"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.14456\/tjst.2021.2"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.25046\/aj060415"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.47893\/IJCCT.2013.1201"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.5120\/20443-2793"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/226\/1\/012082"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.21449\/ijate.479404"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.46592\/turkager.2020.v01i02.011"},{"key":"9","article-title":"Wine quality data set","author":"P. 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