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Hybrid artificial neural network and structural equation modelling techniques: a survey.

A S Albahri1,2, Alhamzah Alnoor3,4, A A Zaidan5

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Summary

This study reviews hybrid Structural Equation Modelling (SEM) and Artificial Neural Network (ANN) applications across 11 industries. It introduces a taxonomy and identifies research gaps, particularly in healthcare for autistic children.

Keywords:
Artificial neural networkAutistic childrenStructural equation modellingTherapy

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Area of Science:

  • Multidisciplinary research integrating advanced analytical techniques.
  • Focus on hybrid Structural Equation Modelling (SEM) and Artificial Neural Network (ANN) methodologies.

Background:

  • Traditional SEM is limited in capturing non-compensatory relationships.
  • Growing importance of SEM and ANN in diverse fields necessitates a comprehensive review.
  • Need for a unified understanding of SEM-ANN applications and their limitations.

Purpose of the Study:

  • To systematically review and classify SEM-ANN techniques across 11 industries over the past six years.
  • To develop a state-of-the-art SEM-ANN classification taxonomy.
  • To identify research gaps and future directions for synergistic multidisciplinary studies.

Main Methods:

  • Systematic literature review of 239 articles from 2016-2021.
  • Searches conducted on Web of Science, ScienceDirect, Scopus, and IEEE Xplore.
  • Selection of 60 articles classified under 11 industry categories.

Main Results:

  • Identified a growing trend of hybrid SEM-ANN analysis in various sectors.
  • Manufacturing and technology sectors show the highest number of SEM-ANN studies.
  • Construction and SME sectors exhibit the least engagement with SEM-ANN.
  • Highlighted research possibilities, motivations, challenges, and limitations.

Conclusions:

  • The hybrid SEM-ANN approach offers linear and non-compensatory relationship insights.
  • Future research should focus on healthcare, specifically therapy adoption for autistic children.
  • Developed taxonomy provides a valuable reference for academics and practitioners in SEM-ANN applications.