Computational Intelligence and Neuroscience

Human Behavior Modelling in Engineering Management under Industry 4.0


Publishing date
01 Aug 2022
Status
Published
Submission deadline
08 Apr 2022

Lead Editor

1Central South University, Changsha, China

2City University of Hong Kong, Hong Kong

3Xi'an University of Architecture and Technology, Xian, China

4China University of Mining and Technology, Xuzhou, China

5London South Bank University, London, UK


Human Behavior Modelling in Engineering Management under Industry 4.0

Description

Human society is entering the era of Industry 4.0. Automatic production and intelligent manufacturing are proliferating rapidly across industries, freeing the workforce from simple but repetitive work. The roles of humans in engineering systems are continuously changing. In addition, engineering systems are becoming more intelligent and complex, which further increases the difficulties in engineering management. Engineering management is facing unprecedented opportunities and challenges. As humans are the most important and determining factor of management, human behavior can significantly affect the efficiency of engineering management.

However, it is usually not easy to understand the mechanism of decision-making process and human behavior in engineering management. This often requires interdisciplinary knowledge in engineering, management, neural engineering, mathematics, and psychology. Efficient tools for human behavior modelling and simulation are urgently needed. With the development of mathematical algorithms and computational technologies, intelligent modelling and simulation bring innovation to research on human behavior in engineering management. Specifically, advanced modelling and simulation methods, such as machine learning, deep learning, big data, system dynamics, and agent-based modelling, enable revealing the mechanism of decision-making process and predicting human behavior under various circumstances. Intelligent modelling and simulation provide valuable information and databases for managers to improve the efficiency of engineering management. There should be continuous research exploring applying intelligent techniques to model and simulate human behavior in engineering management.

The aim of this Special Issue is to solicit research and offer a timely opportunity to scholars and industry practitioners to discuss, share, and disseminate current innovations in intelligent modelling and simulation techniques for human behavior in engineering management under Industry 4.0. Authors are invited to present original research and review articles that will stimulate continuing efforts in this field. We hope that this Special Issue helps solve practical engineering problems and bring theoretical contributions.

Potential topics include but are not limited to the following:

  • Human behavior changes in engineering management under Industry 4.0
  • Intelligent modelling and simulation methods for human behavior in engineering management
  • Dynamic relationships between human behavior and the efficiency of engineering management under Industry 4.0
  • Machine learning and deep learning for human behavior in engineering management
  • Data mining for human behavior in engineering management
  • System dynamics for human behavior in engineering management
  • Agent-based modelling for human behaviour in engineering management
  • Complex employee relationship network modelling in engineering management under Industry 4.0
  • Intelligent prediction, monitoring, assessment, and management of human behavior in engineering management
  • Data-driven and intelligent decision support systems in engineering management under Industry 4.0

Articles

  • Special Issue
  • - Volume 2022
  • - Article ID 3019440
  • - Research Article

Evaluation Index System of Economic and Social Development Pilot Area Based on Spatial Network Structure Analysis

Jing Tu
  • Special Issue
  • - Volume 2022
  • - Article ID 3615492
  • - Research Article

Influencing Factors of CO2 Emissions in Chinese Power Industry: A Study from the Production and Consumption Perspectives

Qiang Liu | Chunmei Mao | Fan Tian
  • Special Issue
  • - Volume 2022
  • - Article ID 4620930
  • - Research Article

Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data

Xin Yan | Junhui Yan
  • Special Issue
  • - Volume 2022
  • - Article ID 2257313
  • - Research Article

Analysis of Transient Response of ZPW-2000A Jointless Track Circuit Considering Frequency Variation

Bin Zhao | Guanghao Yu | ... | Jingning Ou
  • Special Issue
  • - Volume 2022
  • - Article ID 9359353
  • - Research Article

Deep Neural Networks for Automatic Flower Species Localization and Recognition

Touqeer Abbas | Abdul Razzaq | ... | Casper Shikali Shivachi
  • Special Issue
  • - Volume 2022
  • - Article ID 7115627
  • - Research Article

Collaborative Filtering Algorithm-Based Destination Recommendation and Marketing Model for Tourism Scenic Spots

Kejun Lin | Shixin Yang | Sang-Gyun Na
  • Special Issue
  • - Volume 2022
  • - Article ID 2819269
  • - Research Article

Research on Multicamera Photography Image Art in BERT Motion Based on Deep Learning Mode

Zhao Zhao | Mingyang Song | Hongyue Tang
  • Special Issue
  • - Volume 2022
  • - Article ID 1615676
  • - Research Article

An Improved Bearing Fault Diagnosis Model of Variational Mode Decomposition Based on Linked Extension Neural Network

Tichun Wang | Jiayun Wang
  • Special Issue
  • - Volume 2022
  • - Article ID 2563210
  • - Research Article

Comprehensive Evaluation on Teachers’ Knowledge Sharing Behavior Based on the Improved TOPSIS Method

Xiaojuan Yu | Dianshun Hu | ... | Yan Xiao
  • Special Issue
  • - Volume 2022
  • - Article ID 8323962
  • - Review Article

A Comprehensive Review of Recent Deep Learning Techniques for Human Activity Recognition

Viet-Tuan Le | Kiet Tran-Trung | Vinh Truong Hoang

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