Wireless Communications and Mobile Computing

Deep and Transfer Learning Approaches for Complex Data Analysis in the Industry 4.0 Era


Publishing date
01 Dec 2021
Status
Closed
Submission deadline
16 Jul 2021

Lead Editor

1Nantong University, Nantong, China

2Changshu Institute of Technology, Changshu, China

3University of Malaya, Kuala Lumpur, Malaysia

4Jiangnan University, Wuxi, China

This issue is now closed for submissions.
More articles will be published in the near future.

Deep and Transfer Learning Approaches for Complex Data Analysis in the Industry 4.0 Era

This issue is now closed for submissions.
More articles will be published in the near future.

Description

With the rapid development of information and communications technology, intelligent networking of machines, and processes for the industry has been created. Industry 4.0 is the current trend of data exchange, and automation in industry technologies. Technologies utilise existing data, and several other data sources. Collecting data from connected assets, transforming existing manufacturing processes, and gaining efficiency on multiple levels, make the industry 4.0 era possible. Finding the insights of complex data makes related industries more intelligent, more efficient and more customer-focused. It also helps create the smart industry, and detect new industry models.

Although deep learning can learn enough mapping patterns from input-output relationships, complex data analysis still comes with many challenges. The most important challenge is how to improve the generalization ability of a model when encountering different complex situations. Industry 4.0 era is full of scenarios that are not covered by an infinite number of datasets hence why we cannot train the model to predict well in all of them. Recently, rapid advancements in deep & transfer learning approaches have revolutionised a large number of areas of machine learning and data science. The main highlight of deep and transfer learning is it can learn enough knowledge representation from complex data, and it can also transfer the knowledge learned from the finite datasets in scenarios that are not covered.

The aim of this Special Issue is to collate original research articles, as well as review articles, discussing the ever-increasing challenges of complex data such as Internet of Things (IoT) network data, real-time data, industrial chain data, product data, etc. We also hope that this Special Issue inspires further exploitation and development of deep, and transfer learning approaches for complex data analysis in the industry 4.0 era. Submissions focusing on the recent advancements of complex data via deep, and transfer learning methods within the industry 4.0 era are particularly encouraged.

Potential topics include but are not limited to the following:

  • Novel theories in deep and transfer learning
  • Representation learning for complex data analysis in the industry 4.0 era
  • Online deep and transfer learning frameworks for real-time data analysis in the industry 4.0 era
  • Explainable deep and transfer learning models for complex data analysis in the industry 4.0 era
  • Multi-view, and multi-task deep and transfer models for complex data analysis in the industry 4.0 era
  • Manifold-based deep and transfer models for complex data analysis in the industry 4.0 era
  • High-efficiency deep and transfer models for big data analysis in the industry 4.0 era

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 7672762
  • - Research Article

Application of High-Resolution Remote Sensing Image for Individual Tree Identification of Pinus sylvestris and Pinus tabulaeformis

Hong Li | Wunian Yang
  • Special Issue
  • - Volume 2021
  • - Article ID 6094924
  • - Research Article

Research on the Influencing Factors of Film Consumption and Box Office Forecast in the Digital Era: Based on the Perspective of Machine Learning and Model Integration

Qi He | Bin Hu
  • Special Issue
  • - Volume 2021
  • - Article ID 3570412
  • - Research Article

Innovation and Practice of Music Education Paths in Universities under the Popularity of 5G Network

Haoyu Cao
  • Special Issue
  • - Volume 2021
  • - Article ID 1140611
  • - Research Article

Big Data and Deep Learning-Based Video Classification Model for Sports

Lin Wang | Haiyan Zhang | Guoliang Yuan
  • Special Issue
  • - Volume 2021
  • - Article ID 4266417
  • - Research Article

Research and Implementation of the Sports Analysis System Based on 3D Image Technology

Hongwei Wang | Jie Gao | Jingjing Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 3132062
  • - Research Article

Research and Development of Inventory Management and Human Resource Management in ERP

Bo Zhao | Chunlei Tu
  • Special Issue
  • - Volume 2021
  • - Article ID 6309221
  • - Research Article

Influencing Factors of Microlecture on the Teaching Effect of Ideological and Political Courses in Colleges

Yuqian Jin
  • Special Issue
  • - Volume 2021
  • - Article ID 8357488
  • - Research Article

Integrated Design of Graduate Education Information System of Universities in Digital Campus Environment

Jing Ma | Bo Feng
  • Special Issue
  • - Volume 2021
  • - Article ID 6399266
  • - Research Article

Construction and Simulation of a Multiattribute Training Data Mining Model for Basketball Players Based on Big Data

Yunbin Li | Jinyan Ge | Wei Hao
  • Special Issue
  • - Volume 2021
  • - Article ID 6393638
  • - Research Article

An Improved MOEA/D Algorithm for Complex Data Analysis

Weihua Qian | Jiahui Liu | ... | BingShuai Liu
Wireless Communications and Mobile Computing
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
Acceptance rate33%
Submission to final decision81 days
Acceptance to publication37 days
CiteScore4.300
Journal Citation Indicator0.390
Impact Factor2.336
 Submit

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.