Special Issue "Advances in Machine Learning and High-Performance Calculations for Innovative Technologies Development"

A special issue of Applied System Innovation (ISSN 2571-5577).

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Prof. Dr. Andrey Chernov
E-Mail Website
Guest Editor
The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
Interests: intelligent systems; machine learning; high performance computing; mathematical modeling; computational materials
Prof. Dr. Maria Butakova
E-Mail Website
Guest Editor
The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
Interests: artificial intelligence; artificial neural networks; decision-making systems; smart materials

Special Issue Information

Dear Colleagues,

The last few years showed the significant impact that machine learning techniques and high-performance computation have had on innovative technologies. Applications of machine learning and high-performance computation cover a wide area in science from computer and data science through mathematics and physics up to chemistry and biology. Impressive advances in industry have been obtained due to using artificial intelligence with high-performance codes on the modern supercomputer, GPU, and cluster platforms.

The technological and fundamental results in machine learning and high-performance computation depend at least on three following aspects: (1) advances in architectures, models, and algorithms; (2) efficient technologies to analyze big data and development of novel approaches to learning from online and streaming data; (3) progress in computer platforms, including multicore, cluster, GPU, cloud, neuromorphic computing. In this Special Issue, we call for original and timely contributions in the general field of artificial intelligence and computing in which advanced methods in machine learning or high-performance computation have been used to: efficiently explore structures and processes in natural and materials science; unravel dependencies in large-scale systems and big data; elaborate novel learning techniques to explore complex multiscale systems and dynamical processes; establish predictive models and explainable approaches for solving problems in a wide range of fundamental research and industrial applications.

Major topics of interest, by no means exclusive, are:

  • Advanced machine learning models, deep learning models, and model fusion;
  • Advanced and high-dimensional data analytics, large-scale data mining;
  • High-performance and multiscale simulation of complex systems and networks;
  • Large-scale, stream, and online machine learning, algorithms, and software;
  • Advanced neural networks techniques and their applications;
  • Parallel and high-performance computing, hardware acceleration, novel computing algorithms, and software codes;
  • Applications of machine learning and high-performance computing in research and industry.

Prof. Dr. Andrey Chernov
Prof. Dr. Maria Butakova
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 System Innovation is an international peer-reviewed open access quarterly 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.

Published Papers (1 paper)

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Perspective
Applications of Machine Learning and High-Performance Computing in the Era of COVID-19
Appl. Syst. Innov. 2021, 4(3), 40; https://doi.org/10.3390/asi4030040 - 30 Jun 2021
Cited by 1 | Viewed by 1255
Abstract
During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a [...] Read more.
During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a vital role. These technologies helped to contain the disease’s spread by predicting contaminated people/places, as well as forecasting future trends. In this article, we provide insights into the applications of machine learning (ML) and high-performance computing (HPC) in the era of COVID-19. We discuss the person-specific data that are being collected to lower the COVID-19 spread and highlight the remarkable opportunities it provides for knowledge extraction leveraging low-cost ML and HPC techniques. We demonstrate the role of ML and HPC in the context of the COVID-19 era with the successful implementation or proposition in three contexts: (i) ML and HPC use in the data life cycle, (ii) ML and HPC use in analytics on COVID-19 data, and (iii) the general-purpose applications of both techniques in COVID-19’s arena. In addition, we discuss the privacy and security issues and architecture of the prototype system to demonstrate the proposed research. Finally, we discuss the challenges of the available data and highlight the issues that hinder the applicability of ML and HPC solutions on it. Full article
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