Special Issue "Actionable Pattern-Driven Analytics and Prediction"

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

Deadline for manuscript submissions: 31 October 2019

Special Issue Editors

Guest Editor
Prof. Jerry Chun-Wei Lin

Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Bergen, Norway
Website | E-Mail
Interests: data mining; machine learning; artificial intelligence; social computing; multimedia and image processing; privacy-preserving and security technologies
Guest Editor
Prof. Chun-Hao Chen

Tamkang University, Taiwan
Website | E-Mail
Interests: artificial intelligence; financial technology; data mining; Internet of Things; time series; deep learning

Special Issue Information

Dear Colleagues,

Pattern-driven analytics and mining has received a lot of attention in the last two decades, since information discovered in data can be used to support decision and strategy making. In addition to traditional methods for mining interesting patterns, several machine learning and optimization methods have been proposed in artificial intelligence to find interesting patterns and retrieve that information in a reasonable time, or in a big data environment. This Special Issue focuses on the topic of discovering actionable knowledge in realistic situations and enterprise applications. We thus welcome original, creative, innovative, cutting-edge, and state-of-the-art theoretical and applied contributions on this topic, including on the following aspects: (1) Next-generation data analytics and prediction theories, methodologies, frameworks, and processes to support actionable pattern-driven analytics and prediction; (2) developing new machine learning and optimization algorithms and methods for handling the big data environment to retrieve actionable patterns in a reasonable and acceptable time; (3) design of operational tools and systems to address business concerns and deliver actionable patterns for business purposes and processes; (4) investigation of novel trends in pattern-driven analytics using AI techniques for different domains and applications; and (5) studies on the security and privacy of actionable knowledge discovery and related organizational and social issues.  

Prof. Jerry Chun-Wei Lin
Prof. Chun-Hao Chen
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. 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 1500 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

  • Pattern-driven analytics and prediction
  • Machine learning and optimization
  • Artificial intelligence
  • Actional knowledge discovery

Published Papers

This special issue is now open for submission.
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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