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Predicting Student Performance with Adaptive Aquila Optimization-based Deep Convolution Neural Network

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Title Predicting Student Performance with Adaptive Aquila Optimization-based Deep Convolution Neural Network
 
Creator Lu, Jiayi
Singh, Vineeta
Singh, Suruchi
Kumar, Alok
Pandey, Saurabh
Verma, Deepak Kumar
Kaushik, Vandana Dixit
 
Subject Adaptive concept
Aquila optimizer
DCNN
KNN
Student performance prediction model
 
Description 1152-1164
Predicting student performance is the major problem for enhancing the educational procedures. A level of student’s
performance may be influenced by several factors like job of parents, sexual category and average scores obtained in prior
years. Student’s performance prediction is a challenging chore, which can help educational staffs and students of educational
institutions to follow the progress of students in their academic activities. Student performance enhancement and progress in
educational quality are the most vital part of educational organizations. Presently, it is essential for an educational
organization to predict the performance of students. Existing methods utilized only previous student performances for
prediction without including other significant behaviors of students. For addressing such problems, a proficient model is
proposed for prediction of student performance utilizing proposed Adaptive Aquila Optimization-allied Deep Convolution
Neural Network (DCNN). In this process, data transformation is initiated using the Yeo-Johnson transformation method.
Subsequently, feature selection is performed using Fisher Score to identify the most relevant features. Following feature
selection, data augmentation techniques are applied to enhance the dataset. Finally, student performance is predicted through
the utilization of a DCNN, with a focus on fine-tuning the network parameters for optimal performance. This fine-tuning is
achieved through the use of the Adaptive Aquila Optimizer (AAO), ensuring the network is poised to deliver the best
possible results in predicting student outcomes. Proposed AAO-based DCNN has achieved minimal error values of Mean
Square Error, Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, Mean Absolute Relative
Error, Mean Squared Relative Error, and Root Mean Squared Relative Error, respectively.
 
Date 2023-11-06T09:17:44Z
2023-11-06T09:17:44Z
2023-11
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/62861
https://doi.org/10.56042/jsir.v82i11.40
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(11) [November 2023]