Personalized ontology and deep training tree‐based optimal gated recurrent unit‐recurrent neural network for prediction of students' behavior

Abstract

Summary Digital libraries are now widely used by students all around the world due to the hassle‐free services it offers in the form of digitized study materials, ebooks, multimedia content and so forth. However, to increase user satisfaction and allegiance, a personalized digital library needs to be deployed to offer customized services to the users. To accomplish this objective, this article presents a novel scheme to design personalized digital libraries via the usage of the Protégé editor and machine learning techniques. The Protégé 4.3 tool is used to generate the digital library ontologies and identify the interrelated concepts. Domain knowledge acquisition, ontology organization, ontology elaboration, ensuring the consistency of the information, and ontology validation. The gated recurrent unit‐recurrent neural network (GRU‐RNN) with a deep training tree (DTT) is used to predict the different user behavior styles such as cognitive behavior, learning speed behavior, sedentary behavior, and aggressive behavior. The DTT is mainly incorporated to overcome the vanishing gradient issue associated with the GRU‐RNN and used in the place of gradient‐based optimization. A black widow optimization approach is employed to update the weights of the GRU‐RNN network in order to increase its accuracy. The efficiency of the proposed methodology is analyzed by using different performance metrics such as F‐score, accuracy, loss, precision, and recall scores. The results show that the personalized digital library created can be efficient in terms of usability.