Special Issue "Classical & Quantum-Inspired Machine Learning and Optimization in Large-Scale, Real-World Problems"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Quantum Electronics".

Deadline for manuscript submissions: closed (10 January 2022) | Viewed by 307

Special Issue Editors

Dr. Richard Alan Peters
E-Mail Website
Guest Editor
School of Engineering, Vanderbilt University, Nashville, TN 37235, USA
Interests: digital image processing; computer vision; digital signal processing; computer graphics; software engineering; electromagnetic theory; applied mathematics
Dr. Saptarshi Sengupta
E-Mail Website
Guest Editor
Department of Computer Science and Information Systems, Murray State University, Murray, KY 42071, USA
Interests: machine learning; big data analytics; computational intelligence; natural computing; cyber physical systems; reliability

Special Issue Information

Dear Colleagues,

In this Special Issue, the focus is on the theory and applications of classical and quantum-inspired models of computation in solving complex problems across different industrial regimes. Original, high-quality articles that cover the theory and practice of state-of-the-art computational models are solicited. In real-world optimization problems, the importance of nature-inspired approaches arises out of their ability to approximately solve a number of otherwise hard problems by leveraging guided random search. These approaches may include, among others, the use of genetic algorithms, swarm intelligence, neural networks, and deep learning as well as constructs that build on the power of evolution over time. A multitude of practical problems can be approached using such evolution-based algorithms, such as those found in operations research, random processes, computer vision, reliability science, and data visualization.

In light of the above, original submissions are invited that look at fundamental and applied problems where artificial intelligence, machine learning, and evolution intersect. Submitted articles should not have been previously published nor be currently under consideration for publication anywhere else. Potential topics include but are not limited to:

  • Theory of classical and quantum-inspired learning algorithms;
  • Reliability science;
  • Computer vision;
  • Operations research;
  • Data visualization;
  • Anomaly detection;
  • Sentiment analysis.

Our aim is for this Special Issue to provide a platform for researchers in the classical and quantum-inspired evolutionary and data-driven learning community to share their state-of-the-art findings.

Dr. Richard Alan Peters
Dr. Saptarshi Sengupta
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 2000 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

  • Evolutionary Intelligence
  • Reliability Science
  • Quantum-inspired Machine Learning
  • Optimization
  • Data-driven Learning
  • Soft Computing

Published Papers

There is no accepted submissions to this special issue at this moment.
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