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: closed (30 November 2018).

Special Issue Editors

Prof. Dr. Miguel-Angel Sicilia
E-Mail Website
Guest Editor
Department of Computer Science, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Interests: business analytics; machine learning; distributed systems; knowledge representation
Prof. Dr. Ignacio Olmeda
E-Mail Website
Guest Editor
Department of Computer Science, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Interests: deep learning; fintech; business analytics; artificial intelligence; industry 4.0.
Prof. Dr. Alessandro Lameiras Koerich
E-Mail Website
Guest Editor
Department of Software and IT Engineering, École de Technologie Supérieure (ÉTS), University of Québec, Montréal, Canada
Interests: machine learning, big data analytics, deep architectures for audio analysis, multimodal deep learning

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) for publication in this open access journal is 1000 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
  • neural networks
  • autoencoders

Published Papers (2 papers)

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Research

Open AccessArticle
Leveraging Image Representation of Network Traffic Data and Transfer Learning in Botnet Detection
Big Data Cogn. Comput. 2018, 2(4), 37; https://doi.org/10.3390/bdcc2040037 - 27 Nov 2018
Abstract
The advancements in the Internet has enabled connecting more devices into this technology every day. The emergence of the Internet of Things has aggregated this growth. Lack of security in an IoT world makes these devices hot targets for cyber criminals to perform [...] Read more.
The advancements in the Internet has enabled connecting more devices into this technology every day. The emergence of the Internet of Things has aggregated this growth. Lack of security in an IoT world makes these devices hot targets for cyber criminals to perform their malicious actions. One of these actions is the Botnet attack, which is one of the main destructive threats that has been evolving since 2003 into different forms. This attack is a serious threat to the security and privacy of information. Its scalability, structure, strength, and strategy are also under successive development, and that it has survived for decades. A bot is defined as a software application that executes a number of automated tasks (simple but structurally repetitive) over the Internet. Several bots make a botnet that infects a number of devices and communicates with their controller called the botmaster to get their instructions. A botnet executes tasks with a rate that would be impossible to be done by a human being. Nowadays, the activities of bots are concealed in between the normal web flows and occupy more than half of all web traffic. The largest use of bots is in web spidering (web crawler), in which an automated script fetches, analyzes, and files information from web servers. They also contribute to other attacks, such as distributed denial of service (DDoS), SPAM, identity theft, phishing, and espionage. A number of botnet detection techniques have been proposed, such as honeynet-based and Intrusion Detection System (IDS)-based. These techniques are not effective anymore due to the constant update of the bots and their evasion mechanisms. Recently, botnet detection techniques based upon machine/deep learning have been proposed that are more capable in comparison to their previously mentioned counterparts. In this work, we propose a deep learning-based engine for botnet detection to be utilized in the IoT and the wearable devices. In this system, the normal and botnet network traffic data are transformed into image before being given into a deep convolutional neural network, named DenseNet with and without considering transfer learning. The system is implemented using Python programming language and the CTU-13 Dataset is used for evaluation in one study. According to our simulation results, using transfer learning can improve the accuracy from 33.41% up to 99.98%. In addition, two other classifiers of Support Vector Machine (SVM) and logistic regression have been used. They showed an accuracy of 83.15% and 78.56%, respectively. In another study, we evaluate our system by an in-house live normal dataset and a solely botnet dataset. Similarly, the system performed very well in data classification in these studies. To examine the capability of our system for real-time applications, we measure the system training and testing times. According to our examination, it takes 0.004868 milliseconds to process each packet from the network traffic data during testing. Full article
(This article belongs to the Special Issue Applied Deep Learning: Business and Industrial Applications)
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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 - 17 Jun 2018
Cited by 1
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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