Special Issue "Deep Learning for Data Analysis"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 513

Special Issue Editors

Dr. Andres Alvarez-Meza
E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Computer Engineering, Universidad Nacional de Colombia, Manizales 17001, Colombia
Interests: machine learning; deep leaerning; signal processing; neuro-engineering; computer vision
Dr. David Cárdenas-Peña
E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira 660001, Colombia
Interests: signal processing; computer vision; deep learning; pattern recognition

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on Deep Learning for Data Analysis.

Nowadays, Deep Learning is one of the central topics on widespread applications regarding data processing and analysis. In particular, the representation capability of deep learning approaches and their optimization frameworks, mainly based on automatic differentiation and parallel computing, yields sophisticated tools to extract relevant information from raw data, attracting more and more interest from the research community in several fields, such as computer vision, neuro-engineering, big data, business intelligence, time-series forecasting, natural language processing, artificial intelligence, among others.

In this Special Issue, we invite submissions exploring theoretical or applied research concerning recent advances in deep learning methods. Also, comprehensive review and survey papers are welcome.

Dr. Andres Alvarez-Meza
Dr. David Cárdenas-Peña
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • data analysis
  • machine learning
  • representation learning
  • computer vision
  • neuroengineering
  • big data
  • time series forecasting
  • natural language processing
  • ariticial intelligence

Published Papers (1 paper)

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Research

Article
A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer
Appl. Sci. 2022, 12(13), 6396; https://doi.org/10.3390/app12136396 - 23 Jun 2022
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Abstract
Time series prediction is crucial for advanced control and management of complex systems, while the actual data are usually highly nonlinear and nonstationary. A novel broad echo state network is proposed herein for the prediction problem of complex time series data. Firstly, the [...] Read more.
Time series prediction is crucial for advanced control and management of complex systems, while the actual data are usually highly nonlinear and nonstationary. A novel broad echo state network is proposed herein for the prediction problem of complex time series data. Firstly, the framework of the broad echo state network with cascade of mapping nodes (CMBESN) is designed by embedding the echo state network units into the broad learning system. Secondly, the number of enhancement layer nodes of the CMBESN is determined by proposing an incremental algorithm. It can obtain the optimal network structure parameters. Meanwhile, an optimization method is proposed based on the nonstationary statistic metrics to determine the enhancement layer. Finally, experiments are conducted both on the simulated and actual datasets. The results show that the proposed CMBESN and its optimization have good prediction capability for nonstationary time series data. Full article
(This article belongs to the Special Issue Deep Learning for Data Analysis)
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