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Innovative Technology for Sustainable Anticipatory Computing Computing

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 2755

Special Issue Editor


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Guest Editor
Department of Computer Science & Engineering, Anyang University, Anyang 14028, South Korea
Interests: neural networks; fuzzy systems; deep learning; biomedical signal analysis; stock forecasting; machine learning

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has been cited in examples of “anticipatory computing,” such as on the National Public Radio site: “Computers That Know What You Need, Before You Ask (March 17, 2014)”. In AI, anticipation computing occurs when an agent or system makes decisions, expectations, forecasts, and predictions without necessarily explicitly possessing the future states.

This Special Issue aims to explore the state-of-the-art of emerging theoretical and technical solutions for the concept of anticipatory computing. By means of this Special Issue, some deep learning methods and associated AI techniques applied for anticipatory computing will be further dealt with and shared.

The topics of interest include but are not limited to:

(1) Sustainable anticipatory computing for biomedical engineering

(2) Sustainable anticipatory computing for business forecasting

(3) Sustainable anticipatory computing for deep learning

(4) Sustainable anticipatory computing for artificial intelligence

(5) Sustainable anticipatory computing for time series applications

(6) Sustainable anticipatory computing for mobile devices

Prof. Sang-Hong Lee
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 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. Sustainability 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

  • Sustainable Biomedical engieering
  • Business forecasting
  • Deep learning
  • Artificial intelligence
  • Big data
  • Machine learning

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Published Papers (1 paper)

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Research

27 pages, 5900 KiB  
Article
Design and Verification of Process Discovery Based on NLP Approach and Visualization for Manufacturing Industry
by Junhyung Moon, Gyuyoung Park, Minyeol Yang and Jongpil Jeong
Sustainability 2022, 14(3), 1103; https://doi.org/10.3390/su14031103 - 19 Jan 2022
Cited by 2 | Viewed by 2357
Abstract
When a consultant of a company that provides a smart factory solution consults with a customer, it is difficult to define the outline of the manufacturing process and create all activities within the process by case. It requires a large amount of resources [...] Read more.
When a consultant of a company that provides a smart factory solution consults with a customer, it is difficult to define the outline of the manufacturing process and create all activities within the process by case. It requires a large amount of resources from the company to perform a task. In this study, we propose a process discovery automation system that helps consultants define manufacturing processes. In addition, for process discovery, a fully attention-based transformer model, which has recently shown a strong performance, was applied. To be useful to consultants, we solved the black box characteristics of the deep learning model applied to process discovery and proposed a visualization method that can be used in the monitoring system when explaining the discovery process. In this study, we used the event log of the metal fabrication process to perform the modeling, visualization, and evaluation. Full article
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