Special Issue "Applied Deep Learning: Business and Industrial Applications"

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 15 November 2018

Special Issue Editors

Guest Editor
Prof. Dr. Miguel-Angel Sicilia

Department of Computer Science, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Website | E-Mail
Interests: business analytics; machine learning; distributed systems; knowledge representation
Guest Editor
Prof. Dr. Ignacio Olmeda

Department of Computer Science, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Website | E-Mail
Interests: deep learning; fintech; business analytics; artificial intelligence; industry 4.0.
Guest Editor
Prof. Dr. Alessandro Lameiras Koerich

Department of Software and IT Engineering, École de Technologie Supérieure (ÉTS), University of Québec, Montréal, Canada
Website | E-Mail
Interests: big data analytics; machine learning; affective computing; deep learning; computer vision

Special Issue Information

Dear Colleagues,

In the last decade, deep learning has emerged as a sub-field of machine learning that opens new possibilities for applications. The success of deep learning algorithms and architectures in the fields of image processing, speech recognition or text processing are demonstrations of its potential for a variety of tasks. This has been made possible due to a combination of advances in the training algorithms that can be traced back to neural networks and the exploitation of parallel computing and GPUs in deep learning frameworks, such as Theano, TensorFlow, and others.

In this Special Issue, we aim at collecting recent research on applications of deep learning and practical aspects of devising and deploying deep learning at scale.

The Special Issue welcomes contributions in one of the following categories, or in other related ones:

  • Applications of deep learning techniques in the broad range of business analytics problems and problems related to industrial processes.
  • Systematic or analytic reviews of the application of deep learning to categories of problems or concrete settings, that inform further research.
  • Papers dealing with practical aspects of devising and deploying deep learning, including empirical comparisons of deep learning libraries, evaluation of tools for deep learning or any other aspect related to the practice in the field.

Prospective authors are encouraged to contact the Guest Editors for advice about the scope of potential submissions.

Prof. Dr. Miguel-Angel Sicilia
Prof. Dr. Ignacio Olmeda
Prof. Dr. Alessandro Lameiras Koerich
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 papers will be 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. Big Data and Cognitive Computing is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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
  • neural networks
  • autoencoders

Published Papers (1 paper)

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Research

Open AccessArticle Risks of Deep Reinforcement Learning Applied to Fall Prevention Assist by Autonomous Mobile Robots in the Hospital
Big Data Cogn. Comput. 2018, 2(2), 13; https://doi.org/10.3390/bdcc2020013
Received: 30 April 2018 / Revised: 2 June 2018 / Accepted: 7 June 2018 / Published: 17 June 2018
PDF Full-text (2535 KB) | HTML Full-text | XML Full-text
Abstract
Our previous study proposed an automatic fall risk assessment and related risk reduction measures. A nursing system to reduce patient accidents was also developed, therefore reducing the caregiving load of the medical staff in hospitals. However, there are risks associated with artificial intelligence
[...] Read more.
Our previous study proposed an automatic fall risk assessment and related risk reduction measures. A nursing system to reduce patient accidents was also developed, therefore reducing the caregiving load of the medical staff in hospitals. However, there are risks associated with artificial intelligence (AI) in applications such as assistant mobile robots that use deep reinforcement learning. In this paper, we discuss safety applications related to AI in fields where humans and robots coexist, especially when applying deep reinforcement learning to the control of autonomous mobile robots. First, we look at a summary of recent related work on robot safety with AI. Second, we extract the risks linked to the use of autonomous mobile assistant robots based on deep reinforcement learning for patients in a hospital. Third, we systematize the risks of AI and propose sample risk reduction measures. The results suggest that these measures are useful in the fields of clinical and industrial safety. Full article
(This article belongs to the Special Issue Applied Deep Learning: Business and Industrial Applications)
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