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International Journal of Neural Network, 2022, 3(2); doi: 10.38007/NN.2022.030204.

Prediction of College Students' Grade Four Grades by Support Vector Machine and Deep Neural Network

Author(s)

Ruoxin Lin

Corresponding Author:
Ruoxin Lin
Affiliation(s)

College of Mathematics and Data Science, Minjiang University, Fuzhou 350108, China

Abstract

Grade prediction is one of the key research contents of educational big data. It predicts students' future academic performance through the existing grade data and related educational information. It can provide valuable decision-making basis for education-related workers. In order to solve the deficiencies in the existing research on college students' grade 4 achievement prediction, this paper discusses the function equation of support vector machine and deep neural network and the concept of college students' grade 4 grades. The parameter settings and datasets implemented by the model are briefly introduced. And the structure of the four-level achievement prediction model designed in this paper is designed and discussed, and finally the accuracy rate of the four-level achievement prediction model designed in this paper is tested against the MMG prediction model for the classification accuracy of the four different college students' grades. In the comparison, the experimental data show that the accuracy of the four-level grade prediction classification of the support vector machine and the deep neural network is not much different, both in the range of 0.85% to 0.91%, and higher than the MMG model's four-level grade prediction classification. The accuracy rate is about 0.5%, so it verifies the use value of the support vector machine and deep neural network for college students' grade-4 grade prediction application.

Keywords

Support Vector Machine, Deep Neural Network, College Grade Four, Grade Prediction

Cite This Paper

Ruoxin Lin. Prediction of College Students' Grade Four Grades by Support Vector Machine and Deep Neural Network. International Journal of Neural Network (2022), Vol. 3, Issue 2: 27-35. https://doi.org/10.38007/NN.2022.030204.

References

[1] Hsu C C, Huang W Y, Lee J Y. Research on the Motivation and Attitude of College students' Physical Education in Taiwan. International Journal of Physical Education, Fitness and Sports, 2019, 8(1):95-109. https://doi.org/10.26524/ijpefs19112

[2] Emir E, Ocak J. Prediction Level of the Fourth Grade Students' Scientific Attitudes on Reflective Thinking Skills for Problem Solving. Open Journal for Educational Research, 2020, 4(2):87-102. https://doi.org/10.32591/coas.ojer.0402.02087e

[3] Rosso A C, Oliver M S, Papalia Z , et al. Frequent restful sleep is associated with the absence of depressive symptoms and higher grade point average among college students - ScienceDirect. Sleep Health, 2020, 6(5):618-622. https://doi.org/10.1016/j.sleh.2020.01.018

[4] Piccini J P, Harrington J. Midregional Pro–Atrial Natriuretic Peptide and Atrial Fibrillation. Journal of the American College of Cardiology, 2022, 79(14):1382-1384. https://doi.org/10.1016/j.jacc.2022.01.043

[5] Rosales-Perez A, Garcia S, Terashima-Marin H, et al. MC2ESVM: Multiclass Classification Based on Cooperative Evolution of Support Vector Machines. IEEE Computational Intelligence Magazine, 2018, 13(2):18-29. https://doi.org/10.1109/MCI.2018.2806997

[6] Nalepa J, Kawulok M. Selecting training sets for support vector machines: a review. Artificial Intelligence Review, 2018, 52(2):1-44. https://doi.org/10.1007/s10462-017-9611-1

[7] Al-Zoubi A M, Faris H, Alqatawna J, et al. Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts. Knowledge-Based Systems, 2018, 153(AUG.1):91-104. https://doi.org/10.1016/j.knosys.2018.04.025

[8] Bhat S A, Mehbodniya A, Alwakeel A E, et al. Human Recognition using Single-Input-Single-Output Channel Model and Support Vector Machines. International Journal of Advanced Computer Science and Applications, 2021, 12(2):811-823. https://doi.org/10.14569/IJACSA.2021.01202102

[9] Ni W, Ni W W, Indradewi I. Detection of Covid Chest X-Ray using Wavelet and Support Vector Machines. International Journal of Engineering and Emerging Technology, 2020, 5(2):116-121.

[10] Shaik N B, Pedapati S R, Taqvi S A, et al. Classification of Faults in Oil and Gas Pipelines using Support Vector Machines. Pertanika Journal of Science and Technology, 2020, 28(S1):173-184.

[11] Beltrami M, Carlos L. A grid-quadtree model selection method for support vector machines. Expert Systems with Application, 2020, 146(May):113172.1-113172.13. https://doi.org/10.1016/j.eswa.2019.113172

[12] Vrigazova B, Ivanov I. Tenfold Bootstrap Procedure For Support Vector Machines. Computer Science, 2020, 21(2):241-257. https://doi.org/10.7494/csci.2020.21.2.3634

[13] Jalali M, Kekatos V, Gatsis N, et al. Designing Reactive Power Control Rules for Smart Inverters Using Support Vector Machines. IEEE Transactions on Smart Grid, 2020, 11(2):1759-1770. https://doi.org/10.1109/TSG.2019.2942850

[14] C Sánchez-Sánchez, Izzo D. Real-time optimal control via Deep Neural Networks: study on landing problems. Journal of Guidance, Control, and Dynamics, 2018, 41(5):1122-1135. https://doi.org/10.2514/1.G002357

[15] Waldeland A U, Charles J A, Leiv-J. G, et al. Convolutional neural networks for automated seismic interpretation. The Leading Edge, 2018, 37(7):529-537. https://doi.org/10.1190/tle37070529.1

[16] Atzmon M, Maron H, Lipman Y. Point Convolutional Neural Networks by Extension Operators. ACM Transactions on Graphics, 2018, 37(4CD):71.1-71.12. https://doi.org/10.1145/3197517.3201301

[17] Jaime G, Anibal P, Samuel L, et al. Glomerulus Classification and Detection Based on Convolutional Neural Networks. Journal of Imaging, 2018, 4(1):20-20. https://doi.org/10.3390/jimaging4010020

[18] Okamoto T, Tachibana K, Toda T, et al. Deep neural network-based power spectrum reconstruction to improve quality of vocoded speech with limited acoustic parameters. Acoustical Science and Technology, 2018, 39(2):163-166. https://doi.org/10.1250/ast.39.163