Research Article OptimalLearningBehaviorPredictionSystemBasedonCognitive Style Using Adaptive Optimization-Based Neural Network Ghada Aldabbagh , 1 Daniyal M. Alghazzawi , 1 Syed Hamid Hasan, 1 Mohammed Alhaddad , 1 Areej Malibari , 1 andLiCheng 2 1 Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box. 80221, Jeddah-21589, Saudi Arabia 2 Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences, ¨ Ur¨ umqi, China Correspondence should be addressed to Daniyal M. Alghazzawi; dghazzawi@kau.edu.sa Received 30 March 2020; Revised 4 September 2020; Accepted 30 September 2020; Published 5 November 2020 Academic Editor: Danilo Comminiello Copyright © 2020 Ghada Aldabbagh et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. e learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. e cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. erefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). e learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. e output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. e results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches. 1.Introduction With the progress and growth in Internet, there has been a rapid rise in the spread and dispensation of education [1]. e concept of e-learning does not mean only the provision of learning material to the potential learner on the web, but it also involves addressing the needs of instructors/teachers and learners/students who are seeking their own subject- related libraries. E-learning provides education to different learners at various levels irrespective of their learning needs, knowledge level, and preferences [2]. e learning process is fulfilled with the information of fresh interaction, self-regulation, and the individual knowledge of the students. e aim of the learning is to teach students until they gain complete access to the information they need. Important data are delivered by the enterprise resource planning (ERP) and learning management system (LMS) which aim to provide useful and wealthy data to the learners [3]. In the current age of globalization, appreciation of the quality of the education is the major process which is connected with the learning procedure. Eventually, schools require effective and better professionalism to improve student’s knowledge via e-learning method. Effective use of concentration, sense organs, learning ways, and experiences by the students are the means of an educational society. e e-learning process is based on Internet which is the means of self-regulation. Behavior, motivation, and individual cog- nitive styles play significant role in the learning process. Exponential development of mobile system also provides e-learning service to the schools through their people, Hindawi Complexity Volume 2020, Article ID 6097167, 13 pages https://doi.org/10.1155/2020/6097167