用人工神经网络预测学生的期末成绩

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Mining and Analytics Pub Date : 2022-07-18 DOI:10.26599/BDMA.2021.9020030

Tarik Ahajjam;Mohammed Moutaib;Haidar Aissa;Mourad Azrour;Yousef Farhaoui;Mohammed Fattah

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引用次数: 2

摘要

人工智能（AI）是基于允许机器为人类做出决策的算法。这项技术以多种方式增强了用户的体验。在教育领域已经进行了几项研究，以使用各种机器学习（ML）算法来解决学生的定向和表现问题。本文的主要目标是使用基于神经网络的智能系统预测摩洛哥学生在Guelmim Oued Noun地区的表现，神经网络是最好的数据挖掘技术之一，为我们提供了最好的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predicting Students' Final Performance Using Artificial Neural Networks

Artificial Intelligence (AI) is based on algorithms that allow machines to make decisions for humans. This technology enhances the users' experience in various ways. Several studies have been conducted in the field of education to solve the problem of student orientation and performance using various Machine Learning (ML) algorithms. The main goal of this article is to predict Moroccan students' performance in the region of Guelmim Oued Noun using an intelligent system based on neural networks, one of the best data mining techniques that provided us with the best results.

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.

期刊最新文献

Contents Front Cover Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals Gender-Based Analysis of User Reactions to Facebook Posts