Open Access

ARTICLE

Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

by Rohit Raja¹, Chetan Swarup², Abhishek Kumar^3,*, Kamred Udham Singh⁴, Teekam Singh⁵, Dinesh Gupta⁶, Neeraj Varshney⁷, Swati Jain⁸

1 Department of Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, 495009, India
2 Department of Basic Science, College of Science & Theoretical Studies, Saudi Electronic University, 13316, Saudi Arabia
3 Department of Computer Science & IT, JAIN (Deemed to be University), Bangalore, 560069, India
4 Computer Science and Information Science, Cheng Kung University, 621301, Taiwan
5 School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 248007, India
6 Department of CSE, I K Gujral Punjab Technical University, Jalandhar, 144603, India
7 Department of Computer Engineering and Applications, GLA University, Mathura, 281406, India
8 Department of Computer Science, Government J Yoganandam Chhattisgarh College, Raipur, 492001, India

* Corresponding Author: Abhishek Kumar. Email:

(This article belongs to the Special Issue: Edge Computing and Machine Learning for Improving Healthcare Services)

Computers, Materials & Continua 2022, 72(1), 2015-2031. https://doi.org/10.32604/cmc.2022.022904

Received 22 August 2021; Accepted 17 January 2022; Issue published 24 February 2022

Abstract

As far as the present state is concerned in detecting the behavioral pattern of humans (subject) using morphological image processing, a considerable portion of the study has been conducted utilizing frontal vision data of human faces. The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach. In this example, hybridization includes an artificial neural network (ANN) with a genetic algorithm (GA). We researched the geometrical properties extracted from side-vision human-face data. An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering. The close vicinity of minimum distance measurements is done for these clusters, mapped for proper classification and decision process of behavioral pattern. To identify the data acquired, support vector machines and artificial neural networks are utilized. A method known as an adaptive-unidirectional associative memory (AUTAM) was used to map one side of a human face to the other side of the same subject. The behavioral pattern has been detected based on two-class problem classification, and the decision process has been done using a genetic algorithm with best-fit measurements. The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET, Multi-PIE, Yale Face database, RTR, CASIA, etc. The complexity measures have also been calculated under worst-case and best-case situations.

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Keywords

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