Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Advanced Applications of Machine Learning Technologies and Deep Learning Technologies...
share announcement

Advanced Applications of Machine Learning Technologies and Deep Learning Technologies in Big Data Analytics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 6389

Special Issue Editors

Prof. Dr. Ping-Feng Pai

E-Mail Website
Guest Editor
Department of Information Management, National Chi Nan University, Nantou 54561, Taiwan
Interests: machine learning; neural networks; deep learning; big data; data mining; metaheuristics and optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qingjin Peng

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB, Canada
Interests: integrated CAD/CAPP/CAM systems; virtual manufacturing; reverse engineering; system modeling and simulation
Prof. Dr. Kuo-Ping Lin

E-Mail Website
Guest Editor
Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan
Interests: heuristic algorithm; artificial intelligence; system dynamics; reliability engineering; intelligent decision system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

With the evolutionary development of information technology and information systems, machine learning and deep learning techniques have been emerging and powerful tools in dealing with big data problems in many fields. The aim of this Special Issue is to explore applications of machine learning and deep learning approaches in big data analytics. The scope includes, but is not limit to, using machine learning or deep learning techniques in the following issues:

  1. Finance analysis;
  2. Manufacturing systems;
  3. Pandemics forecasting and analysis;
  4. Social media systems and sentiment analysis;
  5. Healthcare systems;
  6. Energy issues;
  7. Hotel management;
  8. Hospital management;
  9. Parameter selections or hybrid systems in machine learning or deep learning;
  10. Survey papers in related fields.

Prof. Dr. Ping-Feng Pai
Prof. Dr. Qingjin Peng
Prof. Dr. Kuo-Ping Lin
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. Electronics 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 2400 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
  • deep learning
  • big data

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 476 KiB  
Article
A Fusion Decision-Making Architecture for COVID-19 Crisis Analysis and Management
by Kuang-Hua Hu, Chengjie Dong, Fu-Hsiang Chen, Sin-Jin Lin and Ming-Chin Hung
Electronics 2022, 11(11), 1793; https://doi.org/10.3390/electronics11111793 - 6 Jun 2022
Viewed by 1927
Abstract
The COVID-19 outbreak has had considerably harsh impacts on the global economy, such as shutting down and paralyzing industrial production capacity and increasing the unemployment rate. For enterprises, relying on past experiences and strategies to respond to such an unforeseen financial crisis is [...] Read more.
The COVID-19 outbreak has had considerably harsh impacts on the global economy, such as shutting down and paralyzing industrial production capacity and increasing the unemployment rate. For enterprises, relying on past experiences and strategies to respond to such an unforeseen financial crisis is not appropriate or sufficient. Thus, there is an urgent requirement to reexamine and revise an enterprise’s inherent crisis management architecture so as to help it recover sooner after having encountered extremely negative economic effects. To fulfill this need, the present paper introduces a fusion architecture that integrates artificial intelligence and multiple criteria decision making to exploit essential risk factors and identify the intertwined relations between dimensions/criteria for managers to prioritize improvement plans and deploy resources to key areas without any waste. The result indicated the accurate improvement priorities, which ran in the order of financial sustainability (A), customer and stakeholders (B), enablers’ learning and growth (D), and internal business process (C) based on the measurement of the impact. The method herein will help to effectively and efficiently support crisis management for an organization confronting COVID-19. Among all the criteria, maintaining fixed reserves was the most successful factor regarding crisis management. Full article
(This article belongs to the Special Issue Advanced Applications of Machine Learning Technologies and Deep Learning Technologies in Big Data Analytics)
►▼ Show Figures

Figure 1

11 pages, 8019 KiB  
Article
The Use of Convolutional Neural Networks and Digital Camera Images in Cataract Detection
by Chi-Ju Lai, Ping-Feng Pai, Marvin Marvin, Hsiao-Han Hung, Si-Han Wang and Din-Nan Chen
Electronics 2022, 11(6), 887; https://doi.org/10.3390/electronics11060887 - 11 Mar 2022
Cited by 11 | Viewed by 3548
Abstract
Cataract is one of the major causes of blindness in the world. Its early detection and treatment could greatly reduce the risk of deterioration and blindness. Instruments commonly used to detect cataracts are slit lamps and fundus cameras, which are highly expensive and [...] Read more.
Cataract is one of the major causes of blindness in the world. Its early detection and treatment could greatly reduce the risk of deterioration and blindness. Instruments commonly used to detect cataracts are slit lamps and fundus cameras, which are highly expensive and require domain knowledge. Thus, the problem is that the lack of professional ophthalmologists could result in the delay of cataract detection, where medical treatment is inevitable. Therefore, this study aimed to design a convolutional neural network (CNN) with digital camera images (CNNDCI) system to detect cataracts efficiently and effectively. The designed CNNDCI system can perform the cataract identification process accurately in a user-friendly manner using smartphones to collect digital images. In addition, the existing numerical results provided by the literature were used to demonstrate the performance of the proposed CNNDCI system for cataract detection. Numerical results revealed that the designed CNNDCI system could identify cataracts effectively with satisfying accuracy. Thus, this study concluded that the presented CNNDCI architecture is a feasible and promising alternative for cataract detection. Full article
(This article belongs to the Special Issue Advanced Applications of Machine Learning Technologies and Deep Learning Technologies in Big Data Analytics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop