Machine Learning Enabled e-Learner Non-Verbal Behavior Detection in IoT Environment

Abdelzahir Abdelmaboud,Fahd N Al-Wesabi,Mesfer Al Duhayyim,Radwa Marzouk,Taiseer Abdalla Elfadil Eisa,Mohammed Rizwanullah,Manar Ahmed Hamza,Abu Serwar Zamani

doi:10.32604/cmc.2022.024240

Abstract

Internet of Things (IoT) with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications. At the same time, machine learning (ML) and data mining approaches are presented for accomplishing prediction and classification processes. With this motivation, this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection (IML-ELNVBD) in IoT environment. The proposed IML-ELNVBD technique allows the IoT devices such as audio sensors, cameras, etc. which are then connected to the cloud server for further processing. In addition, the modelling and extraction of behaviour take place. Moreover, extreme learning machine sparse autoencoder (ELM-SAE) model is employed for the detection and classification of non-verbal behaviour. Finally, the Ant Colony Optimization (ACO) algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE model. In order to ensure the improved performance of the IML-ELNVBD model, a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.

Highlights

Over the past decades, e-learning has shown significant growth and gained considerable interest
The Internet of things (IoT) envisions a near future, where the object of day to day lives would be armed with microcontroller, transceiver for digital transmission, and appropriate protocol stacks would enable us to interact with the user [5], it becomes an essential part of the Internet” [6]
The proposed IML-ELNVBD technique allows the IoT devices such as audio sensors, cameras, etc. which are connected to the cloud server for further processing

Summary

Introduction

E-learning has shown significant growth and gained considerable interest. The IoT envisions a near future, where the object of day to day lives would be armed with microcontroller, transceiver for digital transmission, and appropriate protocol stacks would enable us to interact with the user [5], it becomes an essential part of the Internet” [6] In this aspect, communication access and with the distinct kinds of devices and gadgets such as audio recorder, camera, Google Glass, smartwatches, sensors, Digital board displays, and so on, the IoT would increase the growth of learning circumstances which utilize the data created using this object to offer dynamic service to the learners, content developers and teachers in an innovative campus [7]

Objectives

Methods

Findings

Conclusion