{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:13:35Z","timestamp":1776975215006,"version":"3.51.4"},"reference-count":32,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,1,30]],"date-time":"2022-01-30T00:00:00Z","timestamp":1643500800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,1,30]]},"abstract":"<jats:p>Recently, artificial intelligence (AI) domain increased to contain finance, education, health, mining, and education. Artificial intelligence controls the performance of systems that use new technologies, especially in the education environment. The multiagent system (MAS) is considered an intelligent system to facilitate the e-learning process in the educational environment. MAS is used to make interaction easily among agents, which supports the use of feature selection. The feature selection methods are used to select the important and relevant features from the database that could help machine learning algorithms produce high performance. This paper aims to propose an effective and suitable system for multiagent-based machine learning algorithms and feature selection methods to enhance the e-learning process in the educational environment which predicts pass or fail results. The univariate and Extra Trees feature selection methods are used to select the essential attributes from the database. Five machine learning algorithms named Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and K-nearest neighbors algorithm (KNN) are applied to all features and selected features. The results showed that the learning algorithm that has been measured by the Extra Trees method has achieved the highest performance depending on the evaluation of cross-validation and testing.<\/jats:p>","DOI":"10.1155\/2022\/2941840","type":"journal-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T05:50:24Z","timestamp":1643608224000},"page":"1-8","source":"Crossref","is-referenced-by-count":20,"title":["Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6565-5508","authenticated-orcid":true,"given":"Shrouk H.","family":"Hessen","sequence":"first","affiliation":[{"name":"Faculty of Computers and Information, Menoufia University, Shebin Elkom, Egypt"},{"name":"Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt"}]},{"given":"Hatem M.","family":"Abdul-kader","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information, Menoufia University, Shebin Elkom, Egypt"}]},{"given":"Ayman E.","family":"Khedr","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information Technology, Future University in Egypt (FUE), Cairo, Egypt"}]},{"given":"Rashed K.","family":"Salem","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information, Menoufia University, Shebin Elkom, Egypt"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.03.104"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.15446\/dyna.v86n211.79743"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1544\/1\/012003"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/692\/2\/022025"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.9781\/ijimai.2013.217"},{"key":"6","doi-asserted-by":"crossref","first-page":"5814","DOI":"10.24297\/ijct.v14i6.1913","article-title":"Enhanced cloud computing framework to improve the educational process in higher education: a case study of helwan university in Egypt","volume":"14","author":"A. 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