Optimized Attention Enhanced Temporal Graph Convolutional Network-based Cloud Resource Allocation Supported IoT for Students' Health Monitoring System

Summary

This protocol describes an optimized attention-enhanced temporal graph convolutional network for cloud-based Internet of Things (IoT) student health monitoring.

Abstract

Sensor technology progressions have paved the way for the rapid expansion of the Internet of Things (IoT) applications to construct behavioral and physiological monitoring systems, like an IoT-based student healthcare monitoring system. The status of student health observation is necessary because the number of students who survive loneliness is increasing in large geographical areas. This research article presents an approach named optimized attention enhanced temporal graph convolutional network-based cloud resource allocation supported Internet of Things for students' health monitoring system (HMS-AETGCN-NGOA-IoT). The proposed HMS-AETGCN-NGOA-IoT is implemented using MATLAB. To detect students' health status, performance metrics like precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time, and ROC are considered. The HMS-AETGCN-NGOA-IoT approach achieves 19.11%, 24.12%, and 28.13% higher specificity; 24.93%, 23.04%, and 9.51% lower computation time; 15.2%, 25.45%, and 13.91% higher ROC values; and 8.45%, 20.98%, and 27.55% higher accuracy compared with the existing Health Monitoring System based on Message Passing Neural Network for Internet of Things(HMS-MPNN-IoT), Health Monitoring System based on Support Vector Machine for Internet of Things(HMS-SVM-IoT) and Health Monitoring System based on Deep Neural Network for Internet of Things(HMS-DNN-IoT) methods, respectively.

Introduction

One of the most valuable and exciting research areas is cloud computing¹. This computing technique purchases structure and software services, and user-requested services from Internet². A number of clients, together with cloud computing requests, are rising day by day. As a result, enhancing the speed and precision of cloud computing is critical³. Cloud computing improves patient monitoring⁴. The cloud offers a stable foundation for hard and massive computing tasks, such as data storing and processing, device services, and other information processing activities

Protocol

All required materials, software, and equipment used in this study are mentioned in the Table of Materials.

Data acquisition and preparation

The Data Set used in this work is the Student Mental Health Dataset, which was obtained from the publicly available Kaggle repository²⁶. The dataset contains self-reported questions and responses collected from university students, covering demographic information, academic stress, sleep patterns, and mental health-related indicators. The data includes both sensitive and non-sensitive health conditions with a balanced....

Results

The representative results validate the effectiveness of the proposed HMS-AETGCN-NGOA-IoT approach in identifying sensitive and non-sensitive health issues of students. Enhanced accuracy and F1-score values prove the effectiveness of the attention mechanism in the temporal graph convolutional network in identifying the temporal patterns and relationships of the health features. The high specificity and ROC curves ensure accurate identification with fewer false alarms, and the lower computation time proves the efficiency .......

Discussion

The proposed HMS-AETGCN-NGOA-IoT model demonstrates a significant advancement in automated student health monitoring by effectively integrating IoT data acquisition with a sophisticated deep learning framework. The core of this model's success lies in the Attention Enhanced Temporal Graph Convolutional Network (AETGCN), which is specifically designed to handle the complex, relational, and time-dependent nature of health data. By modeling students and their physiological/behavioral parameters as a dynamic graph, the AETGC.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
MATLAB	MathWorks		R2023a or later
Operating system	windows 10
Personal Computer	N/A		Memory 8 GB RAM
Processor	Intel, Core i5
Student Mental Health Dataset	Kaggle		https://www.kaggle.com/datasets/shariful07/student-mental-health