War strategy based deep learning algorithm for students' academic performance prediction in education systems (Record no. 23274)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250807104219.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250807b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26996 |
| Author | Narayanan, K. Sankara |
| 245 ## - TITLE STATEMENT | |
| Title | War strategy based deep learning algorithm for students' academic performance prediction in education systems |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.38(3), Jan |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Pune |
| Name of publisher, distributor, etc. | Engineering Education Foundation |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 223-236p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Researcherinterestineducationdata mining has increased significantly in a variety of sectors. The recent research works use a variety of machinelearningtechniquestopredictstudents' academic success in the educational sectors. They sufferfromseriousdrawbackslikelowforecast accuracy,highprocessingtimes,andoverhead. Therefore, the proposed work aims to develop a new model for projecting students' academic progress. The main goal of this paper is to develop a smart and automatedsystemforpredictingthestudents’academic performance from the given students’data. For this purpose, a novel optimization and deep learningclassificationmethodologiesare implemented in this study. Here, the public UCI education training dataset is obtained to develop the predictionframeworkforforecastingstudents' academic achievement. The most correlated features from the preprocessed schooling dataset are chosen using the War Strategy Optimization (WStO) method to improve predicting performance. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4642 |
| Topical term or geographic name entry element | Humanities and Applied Sciences |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26997 |
| Co-Author | Kumaravel, A. |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Sangli Rajarambapu Institute Of Technology |
| International Standard Serial Number | 2349-2473 |
| Title | Journal of engineering education transformations (JEET) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://journaleet.in/index.php/jeet/article/view/2390/2223 |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 07/08/2025 | 2025-1272 | 07/08/2025 | 07/08/2025 | Articles Abstract Database |