{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:25:54Z","timestamp":1774538754723,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685724","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,17]]},"abstract":"<jats:p>The widespread adoption of the internet has transformed communication, work, and information access, emphasizing the need for high-speed connectivity. Accurate prediction of network latency, particularly Ping, is essential for enhancing user experiences and optimizing network efficiency. This study focuses on predicting Ping latency using data from ADSL internet speed tests, incorporating variables such as geographical coordinates (longitude and latitude), subscribed package download and upload speeds, and internet provider band. The dataset is split into training and testing sets for model evaluation. Through our analysis of ADSL speed test data, we achieve a Mean Absolute Percentage Error (MAPE) of 11.98% for Ping prediction. These results provide valuable insights for stakeholders aiming to enhance the reliability and efficiency of broadband services. For network operators and service providers, our results provide a roadmap for optimizing infrastructure and refining management approaches to deliver superior service quality. Likewise, end-users stand to benefit from improved network performance, leading to smoother online interactions and heightened satisfaction.<\/jats:p>","DOI":"10.3233\/faia241596","type":"book-chapter","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T08:29:28Z","timestamp":1742372968000},"source":"Crossref","is-referenced-by-count":1,"title":["Prediction of the Internet Delay Using Machine Learning Techniques"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6802-8235","authenticated-orcid":false,"given":"Watcharaphong","family":"Yookwan","sequence":"first","affiliation":[{"name":"Faculty of Informatics, Burapha University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9696-3586","authenticated-orcid":false,"given":"Krisana","family":"Chinnasarn","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, Burapha University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5192-8998","authenticated-orcid":false,"given":"Waranrach","family":"Viriyavit","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, Burapha University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Information Modelling and Knowledge Bases XXXVI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241596","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T08:29:28Z","timestamp":1742372968000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241596"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,17]]},"ISBN":["9781643685724"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241596","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,17]]}}}