Deep Learning Architecture based on Behavioral and Academic Perspectives for Classifying Students in a University

Authors

  • Srivats S. Ramanujam B. Tech Computer Science and Engineering, School of Computing, SASTRA Deemed University, Thanjavur, India Author
  • J. Naren Asst Professor, School of Computing, SASTRA Deemed University, Tirumalaisamudram, Thanjavur, Tamil Nadu, India. Author
  • Nivedha Jayaseelan B. Tech Computer Science and Engineering, School of Computing, SASTRA Deemed University, Thanjavur, India. Author
  • Vijayalakshmi . Tech Computer Science and Engineering, School of Computing, SASTRA Deemed University, Thanjavur, India Author
  • Dr.G. Vithya Professor, School of Computing, KL University, Vijayawada, AP, India. Author

DOI:

https://doi.org/10.61841/4gvcd816

Keywords:

Deep Learning, Students Academic Performance.

Abstract

 Research on the academic records of students has become increasingly popular. Recent research results advocates’ prospects that a student’s lifestyle and personality can have a major impact on his/her ethos. It has become increasingly common for students studying in universities to not fully realize the potential and prospects in a professional sphere. Technology can aid students in realizing their caliber and prosper in the professional domain. In the proposed work, a system has been suggested which examines the feasibilities of the impact that personality features can have on a student’s career in addition to academic performance. 

Downloads

Download data is not yet available.

Downloads

Published

18.09.2024

Issue

Vol. 23 No. 1 (2019): 23 (1) 2019

Section

Articles

License

Copyright (c) 2024 Author

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

How to Cite

S. Ramanujam, S., Naren, J., Jayaseelan, N., Vijayalakshmi, & Vithya, G. (2024). Deep Learning Architecture based on Behavioral and Academic Perspectives for Classifying Students in a University. International Journal of Psychosocial Rehabilitation, 23(1), 366-370. https://doi.org/10.61841/4gvcd816