| 000 | a | ||
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| 999 |
_c23274 _d23274 |
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| 003 | OSt | ||
| 005 | 20250807104219.0 | ||
| 008 | 250807b xxu||||| |||| 00| 0 eng d | ||
| 040 |
_aAIKTC-KRRC _cAIKTC-KRRC |
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| 100 |
_926996 _aNarayanan, K. Sankara |
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| 245 | _aWar strategy based deep learning algorithm for students' academic performance prediction in education systems | ||
| 250 | _aVol.38(3), Jan | ||
| 260 |
_aPune _bEngineering Education Foundation _c2024 |
||
| 300 | _a223-236p. | ||
| 520 | _aResearcherinterestineducationdata 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 |
_94642 _aHumanities and Applied Sciences |
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| 700 |
_926997 _aKumaravel, A. |
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| 773 | 0 |
_dSangli Rajarambapu Institute Of Technology _x2349-2473 _tJournal of engineering education transformations (JEET) |
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| 856 |
_uhttps://journaleet.in/index.php/jeet/article/view/2390/2223 _yClick here |
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| 942 |
_2ddc _cAR |
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