Increasing Accuracy in Predicting Student Test Scores with Neural Networks using Domain Reduction Technique of Principal Component Analysis-Reference-Cited by-同舟云学术

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Increasing Accuracy in Predicting Student Test Scores with Neural Networks using Domain Reduction Technique of Principal Component Analysis

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
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Author:

Brown Michael Scott¹,Rajashekar Bhavana¹,Pahnehkolaee Nastaran Davudi¹

Affiliation:

1. University of Maryland Baltimore County,Information Systems Department,Baltimore,Maryland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10068862/10068777/10069411.pdf?arnumber=10069411

Reference25 articles.

1. Student performance prediction using Support Vector Machine and K-Nearest Neighbor

2. Enhancement of student performance prediction using modified K-nearest neighbor

3. Predicting student performance using KNN classification in bigdata environment;nagesh;CVR Journal of Science and Technology,2017

4. Support vector machine—introduction to machine learning algorithms;gandhi;Towards Data Science,2018

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Increasing Accuracy in Predicting Student Test Scores with Regression Using Domain Reduction Technique of Principal Component Analysis;2023 IEEE Frontiers in Education Conference (FIE);2023-10-18

2. A Hybrid Approach for Leaf Classification Using Machine Learning and Deep Learning;2023 International Conference on Circuit Power and Computing Technologies (ICCPCT);2023-08-10

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