Computational Intelligence and Neuroscience

Explainable and Reliable Machine Learning by Exploiting Large-Scale and Heterogeneous Data


Publishing date
01 Jan 2021
Status
Closed
Submission deadline
14 Aug 2020

Lead Editor

1University of the District of Columbia, Washington, USA

2Southeast University, Nanjing, China

3University of Central Florida, Orlando, USA

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

Explainable and Reliable Machine Learning by Exploiting Large-Scale and Heterogeneous Data

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

Description

The exponentially growing availability of data such as images, videos, and speech from myriad sources, including social media and the Internet of Things, is driving the demand for high-performance data analysis algorithms. Deep learning is currently an extremely active research area in machine learning and pattern recognition. It provides computational models of multiple nonlinear processing neural network layers to learn and represent data with increasing levels of abstraction. Deep neural networks are able to implicitly capture intricate structures of large-scale data and deploy in cloud computing and high-performance computing platforms.

The deep learning approach has demonstrated remarkable performances across a range of applications, including computer vision, image classification, face/speech recognition, and medical communications. However, deep neural networks yield ‘black-box’ input-output mappings that can be challenging to explain to users. Especially in the medical, military, and legal fields, black-box machine learning techniques are unacceptable, since decisions may have a profound impact on peoples’ lives due to the lack of interpretability. In addition, many other open problems and challenges still exist, such as computational and time costs, repeatability of the results, convergence, and the ability to learn from a very small amount of data and to evolve dynamically.

This Special Issue aims to present the latest theoretical and technical advancements of machine and deep learning models and algorithms with improved computational efficiency and scalability. We hope that papers published will improve the understanding and explainability of deep neural networks, enhance the mathematical foundation of deep neural networks, and increase the computational efficiency and stability of the machine and deep learning training process with new algorithms that will scale.

Potential topics include but are not limited to the following:

  • Supervised, unsupervised, and reinforcement learning
  • Classification, clustering, and optimization for big data analytics
  • Extracting understanding from large-scale and heterogeneous data
  • Dimensionality reduction and analysis of large-scale and complex data
  • Quantifying or visualizing the interpretability of deep neural networks
  • Stability improvement of deep neural network optimization
  • Time series prediction and water flow forecasting using machine and deep learning
  • Novel machines and deep learning approaches in the applications of image/signal processing, business intelligence, games, healthcare, bioinformatics, and security

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 8863847
  • - Research Article

A Study on Differences between Simplified and Traditional Chinese Based on Complex Network Analysis of the Word Co-Occurrence Networks

Zhongqiang Jiang | Dongmei Zhao | ... | Yidong Chen
  • Special Issue
  • - Volume 2020
  • - Article ID 8818794
  • - Research Article

Stability Analysis for Nonlinear Impulsive Control System with Uncertainty Factors

Zemin Ren | Shiping Wen | ... | Ning Tang
  • Special Issue
  • - Volume 2020
  • - Article ID 8846438
  • - Research Article

Adaptive State Observer Design for Dynamic Links in Complex Dynamical Networks

Zilin Gao | Jiang Xiong | ... | Qingshan Liu
  • Special Issue
  • - Volume 2020
  • - Article ID 8816125
  • - Research Article

An Improved Sign Language Translation Model with Explainable Adaptations for Processing Long Sign Sentences

Jiangbin Zheng | Zheng Zhao | ... | Yiqi Tong
  • Special Issue
  • - Volume 2020
  • - Article ID 8882279
  • - Research Article

Application of Offshore Visibility Forecast Based on Temporal Convolutional Network and Transfer Learning

Zhenyu Lu | Cheng Zheng | Tingya Yang
  • Special Issue
  • - Volume 2020
  • - Article ID 8848363
  • - Research Article

Learning-Based Lane-Change Behaviour Detection for Intelligent and Connected Vehicles

Luyao Du | Wei Chen | ... | Di Wu
  • Special Issue
  • - Volume 2020
  • - Article ID 8823906
  • - Review Article

Extracting Parallel Sentences from Nonparallel Corpora Using Parallel Hierarchical Attention Network

Shaolin Zhu | Yong Yang | Chun Xu
  • Special Issue
  • - Volume 2020
  • - Article ID 8858588
  • - Research Article

A Radar Signal Recognition Approach via IIF-Net Deep Learning Models

Ji Li | Huiqiang Zhang | ... | Wei Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 8887453
  • - Research Article

Image Target Recognition via Mixed Feature-Based Joint Sparse Representation

Xin Wang | Can Tang | ... | Wei Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 8879795
  • - Research Article

A Compressive Sensing Model for Speeding Up Text Classification

Kelin Shen | Peinan Hao | Ran Li
Computational Intelligence and Neuroscience
 Journal metrics
Acceptance rate28%
Submission to final decision79 days
Acceptance to publication37 days
CiteScore5.400
Journal Citation Indicator0.630
Impact Factor3.633
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.