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.

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

This issue is now closed for submissions.

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 6623554
  • - Research Article

Network Intrusion Detection Based on an Improved Long-Short-Term Memory Model in Combination with Multiple Spatiotemporal Structures

Xiaolong Huang
  • Special Issue
  • - Volume 2021
  • - Article ID 5585062
  • - Research Article

Multiobjective Optimization Method of Coevolution to Intelligent Agricultural Dynamic Services under the Internet of Things Environment

Haihong Liang
  • Special Issue
  • - Volume 2021
  • - Article ID 5533374
  • - Research Article

CPEH: A Clustering Protocol for the Energy Harvesting Wireless Sensor Networks

Yu Han | Jian Su | ... | Jian Li
  • Special Issue
  • - Volume 2021
  • - Article ID 5579637
  • - Research Article

Video Stream Session Migration Method Using Deep Reinforcement Learning in Cloud Computing Environment

Lingling Li | Huixia Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 5553635
  • - Research Article

Coword and Cluster Analysis for the Romance of the Three Kingdoms

Chao Fan | Yu Li
  • Special Issue
  • - Volume 2021
  • - Article ID 6635638
  • - Research Article

Microgrid Group Control Method Based on Deep Learning under Cloud Edge Collaboration

Yazhe Mao | Baina He | ... | Yanchen Dong
  • Special Issue
  • - Volume 2021
  • - Article ID 6680300
  • - Research Article

An Empirical Study on Optimal the Allocations in Advertising and Operation Innovation on Supply Chain Alliance for Complex Data Analysis

Jiang-Tao Wang | Jian-Jun Yu | ... | Shu-Fen Zhang
  • Special Issue
  • - Volume 2021
  • - Article ID 6695626
  • - Research Article

Markdown Time for Perishables Based on Dynamic Quality Evaluation for Complex Data Analysis

Jiang-Tao Wang | Jian-Jun Yu | ... | Shu-Fen Zhang
  • Special Issue
  • - Volume 2021
  • - Article ID 6646812
  • - Research Article

A Fast Hybrid Strategy-Based RFID Tag Identification Protocol

Xinyan Wang
Wireless Communications and Mobile Computing
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Acceptance rate11%
Submission to final decision151 days
Acceptance to publication66 days
CiteScore2.300
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