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Gradient One-to-One Optimizer and Deep Learning based Student Stress Level Prediction Model

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Title Gradient One-to-One Optimizer and Deep Learning based Student Stress Level Prediction Model
 
Creator Xu, Wenjing
Singh, Vineeta
Swarup, Shivam
Kant, Kamal
Dwivedi, Abhishek
Mamoria, Pushpa
Virmani, Amit
Kumar, Alok
Agrahari, Omkar
Kaushik, Vandana Dixit
 
Subject Deep spiking neural network
Lorentzian similarity
One-to-one based optimization
Stochastic gradient descent
Stress level prediction
 
Description 1184-1193
Student stress-based issues are considered as the most common reason in the student environment. Student stress level
prediction is the major source for students’ academic performance and health. Students' stress levels increase the prevalence
of psychological as well as physical challenges like nervousness, anxiety, and depression. Over the past years, different
machine learning and deep learning based models have been proposed for student stress level prediction but they suffer
certain limitations such as complex structure, less efficiency, high chance of misclassification, high chance of making
mistakes. Predicting stress levels at early stage may help to minimize its impact and various serious health problems
pertaining to this mental state. For this, automated frameworks are needed to predict stress levels accurately. This study
proposes a hybrid approach named as GOOBO: DSNN (Gradient One-to-One Based Optimization: Deep Spiking Neural
Network), that may identify stress accurately and efficiently utilizing optimization based hybrid of deep learning techniques.
Here, the GOOBO is designed by incorporating Stochastic Gradient Descent (SGD) and One-to-One Based Optimization
(OOBO). Here DSNN has been used which uses spiking neurons having different learning dynamics compared to traditional
artificial neurons. Here proposed stress prediction model’s effectiveness has been enhanced by bio-inspired nature of
DSNN simulating biological neural systems. The performance of the proposed GOOBO-DSNN is analyzed for its
effectiveness using evaluation metrics such as accuracy, sensitivity, specificity, and precision. The proposed GOOBODSNN
attained the maximum accuracy, sensitivity, specificity, and precision as compared to recently developed models.
The proposed GOOBO-DSNN accomplished the higher accuracy, sensitivity, specificity, and precision of 90.976 %, 91.698
%, 91.336 %, and 90.179 % respectively. Duplicate attributes have been deleted, and missing values are filled in during the
preprocessing step of the dataset.
 
Date 2024-11-08T06:40:24Z
2024-11-08T06:40:24Z
2024-11
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/64841
https://doi.org/10.56042/jsir.v83i11.12298
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.83(11) [November 2024]