Paper—Multidimensional Approach Based on Deep Learning to Improve the Prediction… Multidimensional Approach Based on Deep Learning to Improve the Prediction Performance of DNN Models https://doi.org/10.3991/ijet.v14i02.8873 Mohammed El Fouki (*) , Noura Aknin, K. Ed El. Kadiri Abdelmalek Essaadi University, Tetouan, Morocco melfouki@uae.ac.ma Abstract—The most of collected data samples from E-learning systems con- sist of correlated information caused by overlapping input instances, which de- crease the classifier credibility and reliability. This paper presents an improved classification model based on Deep Learning and Principal Component Analysis (PCA) method as its use in reducing the dimensionality of data. By this task, we introduce the best learning process to extract just the useful parameters that de- scribe students’ performances in an E-learning system. One of the primary goals of this technique is to help earlier in detecting the dropouts and discovering of students who need special attention, so that the teachers could provide the appro- priate counseling at the right time. This study presents the proposal approach and its algorithms. In addition, it shows how deep neural network was modeled in the training phase, and how PCA helps in the elimination of correlated information in our dataset to increase the classifier performance. Finally, we introduce an ex- ample of an application of the method in a data mining scenario, find out more references for further information. Keywords—Educational Data Mining (EDM), Classification, Deep Neural Network (DNN), Deep Learning, Principal Component Analysis (PCA). 1 Introduction Big Data Analytic, especially, Deep Learning is an interesting area of data science. Big Data has become more important because many public and private organizations have collected massive amounts of information that can contain useful knowledge on different topics such as medical informatics, marketing, fraud detection, cyber security, prediction and learning systems [1]. Consequently, companies such as Microsoft and Google are analyzing large volumes of data, affecting existing and future technologies. The deep learning algorithm is one of these new technologies that try to extract high- level of complex abstractions and representations of data through a hierarchical learn- ing process. These complex abstractions are learned according to several simple ab- stractions developed in the previous level of the hierarchy. Therefore, this study aims to analyze and learn the massive amounts of unattended data by using deep learning, which is based on an effective algorithm for data analytics where raw data is largely unmarked and unclassified [2]. Deep Learning is a promising algorithm of machine 30 http://www.i-jet.org