Special Issue "Machine Learning for Big Data--Big Data Service 2019"

A special issue of Information (ISSN 2078-2489).

Deadline for manuscript submissions: closed (20 February 2020).

Special Issue Editor

Dr. Birsen Sirkeci
Website
Guest Editor
Department of Electrical Engineering, San Jose State University, San Jose, CA 95192, USA
Interests: machine learning; signal processing; communications; wireless networks

Special Issue Information

Dear Colleagues,

Big Data has incredible potential in fields such as health case, self-driving vehicles, online advertising, energy, and finance. However, its potential highly relies on the performance of the data analytics methods used. Machine learning is a field that deals with understanding data and building models. Traditional machine-learning techniques rely on assumptions that are no longer valid in the context of Big Data. This Special Issue will address challenges and possible solutions specific to machine learning in the context of Big Data. These issues could be related to any of the aspects of Big Data: its volume, variety, velocity, veracity, or valence.

Dr. Birsen Sirkeci
Guest Editor

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. Information is an international peer-reviewed open access monthly 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

  • machine learning
  • big data

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance
Information 2020, 11(4), 208; https://doi.org/10.3390/info11040208 - 14 Apr 2020
Abstract
Heating appliances consume approximately 48 % of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before [...] Read more.
Heating appliances consume approximately 48 % of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data. Full article
(This article belongs to the Special Issue Machine Learning for Big Data--Big Data Service 2019)
Show Figures

Figure 1

Back to TopTop