Complexity

Deep Learning Methods Applied to Complex Big Data Analysis


Publishing date
01 Mar 2021
Status
Closed
Submission deadline
30 Oct 2020

Lead Editor
Guest Editors

1Nanjing University of Information Science and Technology, Nanjing, China

2Donghua University, Shanghai, China

3Case Western Reserve University, Cleveland, USA

This issue is now closed for submissions.

Deep Learning Methods Applied to Complex Big Data Analysis

This issue is now closed for submissions.

Description

At present, the emergence of increasingly complex big data brings more challenges to the current big data analysis technology. Complexity is the fundamental difference between complex big data and traditional big data. It is mainly manifested in four aspects: source diversity, type complexity, structure complexity, and internal pattern complexity. At present, there has been a lot of research progress on the source diversity and the type complexity. However, the structure complexity and internal pattern complexity are the difficulties in the analysis of complex big data, among which the internal pattern complexity is the most widely encountered. Many complex big data sets have complex contents (for example, some image data set contains a variety of scenes or a variety of objects), which requires that the processing methods have robust processing capability for a variety of complex objects.

Many complex big data are greatly affected by external factors (for example, the content of an image changes greatly due to the influence of illumination and occlusion), which requires the processing method to be robust to complex changes. Some complex big data sets have large amounts of data or high feature dimensions, and some applications need real-time processing and have high requirements on the calculation efficiency of massive data. Because of its multi-layer nonlinear structure, the deep learning model has a strong feature learning ability, which provides an effective way to solve the above problems. However, different from the traditional big data learning method, we still need to comprehensively use all kinds of knowledge and means (including text mining, image processing, complex networks, knowledge transfer, graph neural networks, etc.) to study the internal pattern complexity in complex big data.

Therefore, this Special Issue aims to collate original research and review articles that emphasise the important role of deep learning for complex big data analysis, especially for the analysis of internal pattern complexity. It aims to call for state-of-the-art researches in the theory, algorithm, modelling, system, and application of deep learning-based complex big data analysis and to demonstrate the latest efforts of relevant researchers.

Potential topics include but are not limited to the following:

  • Deep learning methods for analysis of complex big data with internal pattern complexity
  • Deep learning methods for the analysis of complex multi-source time-series data
  • Deep learning methods for semantic information extraction from complex image and video data with internal pattern complexity
  • Deep learning methods for the analysis of multi-source and multi-dimensional complex big data
  • Graph neural network methods for complex network data analysis (complex social network analysis, complex power network analysis, graph correlation analysis, etc.)
  • Model acceleration for deep learning of complex big data

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 6692313
  • - Research Article

A Spatial-Temporal Self-Attention Network (STSAN) for Location Prediction

Shuang Wang | AnLiang Li | ... | Muhammad Asif
  • Special Issue
  • - Volume 2021
  • - Article ID 6457246
  • - Research Article

Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation

Hua Xie | Minghua Zhang | ... | Haiyan Chen
  • Special Issue
  • - Volume 2021
  • - Article ID 2352185
  • - Research Article

DefogNet: A Single-Image Dehazing Algorithm with Cyclic Structure and Cross-Layer Connections

Suting Chen | Wenhao Fan | ... | Yong Huang
  • Special Issue
  • - Volume 2021
  • - Article ID 2678124
  • - Research Article

MSLp: Deep Superresolution for Meteorological Satellite Image

Liling Zhao | Hao Yu | Yan Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 6629634
  • - Research Article

Realistic Speech-Driven Talking Video Generation with Personalized Pose

Xu Zhang | Liguo Weng
  • Special Issue
  • - Volume 2020
  • - Article ID 5831632
  • - Review Article

A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data

Guokai Zhang | Haoping Xiao | ... | Liying Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 6431712
  • - Research Article

A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

Yang Yujun | Yang Yimei | Xiao Jianhua
  • Special Issue
  • - Volume 2020
  • - Article ID 8855885
  • - Research Article

Feature Guided CNN for Baby’s Facial Expression Recognition

Qing Lin | Ruili He | Peihe Jiang
  • Special Issue
  • - Volume 2020
  • - Article ID 8706285
  • - Research Article

Improved ML-Based Technique for Credit Card Scoring in Internet Financial Risk Control

Shuangshuang Fan | Yanbo Shen | Shengnan Peng
  • Special Issue
  • - Volume 2020
  • - Article ID 7141320
  • - Research Article

A Methodology for Calculating Greenhouse Effect of Aircraft Cruise Using Genetic Algorithm-Optimized Wavelet Neural Network

Yong Tian | Lina Ma | ... | Qian Wang
Complexity
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Acceptance rate11%
Submission to final decision120 days
Acceptance to publication21 days
CiteScore4.400
Journal Citation Indicator0.720
Impact Factor2.3
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