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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Abdulwahid, Nibras Othman Akrout, Sana Fakhfakh Amor, Ikram Amous Ben Miracl Laboratory Enet'com Technopole of Sfax Sfax University Road Tunis km 10 Sfax3018 Tunisia Department of Information Systems College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

The traditional educational systems in certain nations, such as those in the Arab world, use the previous year's scores to forecast academic achievement. Meanwhile, the science, technology, engineering, arts and mathematics (STEAM) educational system also considers the student's talents and interests, in addition to their test results, for each of the years of study used to forecast academic achievement. However, despite the numerous variables that could potentially impact a student's future profession, the STEAM educational system accurately predicts the academic achievement of students in five to seven broad majors. A smart model, data, generative, factors (DGF), has been proposed to assign a specific major for each student based on their academic background, interests, skills, and main influencing factors. The DGF model uses the supervised deep neural network and is trained by the generative adversarial network (GAN) algorithm. The DGF model has the capability to predict a specific major among 17 different majors for every student, with a high learning performance (1.4133), a plausible error rate (0.0046), a rational number of learning epochs (206), and an optimal accuracy of 99.99%. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Deep Learning Depp neural network generative adversarial network algorithm predicting student's academic performance STEAM Education

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Predicted Specified STEAM Student Majors Depending On Many Factors Using generative adversarial networks

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Procedia Computer Science 2023年 225卷 581-590页

作者： Nibras Othman Abdulwahid Sana Fakhfakh Akrout Ikram Amous Ben Amor

关键词： Depp neural network predicting student's academic performance STEAM Education Deep Learning generative adversarial network algorithm

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