Special Issue "Advance in Machine Learning"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Advanced Digital and Other Processes".

Deadline for manuscript submissions: 15 October 2021.

Special Issue Editors

Dr. Konstantinos Demertzis
E-Mail Website
Guest Editor
Laboratory of Complex Systems, Department of Physics, Faculty of Sciences, International Hellenic University, Kavala Campus, 65404 St. Loukas, Greece
Interests: AI cybersecurity; AI big data; AI blockchain; AI DevOps; Geo AI
Special Issues and Collections in MDPI journals
Prof. Dr. Lazaros Iliadis
E-Mail Website
Guest Editor
Laboratory of Mathematics and Informatics (ISCE), Department of Civil Engineering, School of Engineering, Faculty of Mathematics, Programming and general courses, Democritus University of Thrace, 67100 Xanthi, Greece
Interests: computational intelligence; expert systems; multiagent systems; fuzzy theory; decision support systems; pattern recognition; neural networks; machine learning; intelligent optimization
Special Issues and Collections in MDPI journals
Dr. Nikos Tziritas
E-Mail Website
Guest Editor
Department of Computer Science and Telecommunications, School of Sciences, University of Thessaly, Greece
Interests: parallel and distributed systems; distributed machine learning; performance optimization; IoT/IIoT; real-time big data analytics; cloud computing
Special Issues and Collections in MDPI journals
Dr. Panayotis Kikiras
E-Mail Website
Guest Editor
EDA Research and Technology Coordinator - Head of Unit Technology and Innovation at European Defence Agency
Interests: real-time architectures; machine learning; sensor networks; edge computing; ontologies; semantic web; user modeling; emergency management; ambient intelligence

Special Issue Information

Dear Colleagues,

Machine learning is filling the gaps between theory and practice and helps to change virtually every aspect of modern lives. Today, advance in machine learning algorithms accomplishes tasks to solving real-world problems that until recently only expert humans could perform.  

In this Special Issue, we seek research and case studies that demonstrate the application of machine learning to support applied scientific research, in any area of science and technology. Example topics include (but are not limited to) the following topics applied to machine learning:

  • New machine learning algorithms
  • New optimization techniques
  • Distributed machine learning systems and architectures
  • New applications on real-time/big data analytics
  • Intelligent applications
  • Quantum machine learning
  • Data and code integration
  • Visualization of modern systems and networks
  • High-throughput data analysis
  • Comparison and alignment methods

Dr. Konstantinos Demertzis
Prof. Dr. Lazaros Iliadis
Dr. Nikos Tziritas
Dr. Panayotis Kikiras
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. Processes is an international peer-reviewed open access monthly 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

  • Deep Learning
  • Spiking Neural Computation
  • Big Data Architectures
  • Data Lakes
  • Quantum Machine Learning
  • Stream Learning
  • Meta-Learning
  • Ambient Intelligence
  • Real-Time Analytics
  • Distributed Systems

Published Papers (4 papers)

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Research

Article
Designed a Passive Grinding Test Machine to Simulate Passive Grinding Process
Processes 2021, 9(8), 1317; https://doi.org/10.3390/pr9081317 - 29 Jul 2021
Viewed by 239
Abstract
Passive grinding is a high-speed rail grinding maintenance strategy, which is completely different from the conventional rail active grinding system. In contrast to active grinding, there is no power to drive the grinding wheel to rotate actively in passive grinding. The passive grinding [...] Read more.
Passive grinding is a high-speed rail grinding maintenance strategy, which is completely different from the conventional rail active grinding system. In contrast to active grinding, there is no power to drive the grinding wheel to rotate actively in passive grinding. The passive grinding process is realized only by the cooperation of grinding pressure, relative motion, and deflection angle. Grinding tests for passive grinding can help to improve the passive grinding process specifications and be used for the development of passive grinding wheels. However, most of the known grinding methods are active grinding, while the passive grinding machines and processes are rarely studied. Therefore, a passive grinding test machine was designed to simulate passive grinding in this study. This paper gives a detailed description and explanation of the structure and function of the passive grinding tester. Moreover, the characteristics of the grinding process and parameter settings of the testing machine were discussed based on the passive grinding principle. The design of a passive grinding test machine provides experimental equipment support for investigating passive grinding behavior and grinding process. Full article
(This article belongs to the Special Issue Advance in Machine Learning)
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Article
Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis
Processes 2021, 9(8), 1267; https://doi.org/10.3390/pr9081267 - 22 Jul 2021
Viewed by 353
Abstract
With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of [...] Read more.
With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of the pattern of spread already recorded. These conditions caused by the unprecedented pandemic have highlighted the importance of reliable data from official sources, their complete recording and analysis, and accurate investigation of epidemiological indicators in almost real time. There is an ongoing research demand for reliable and effective modeling of the disease but also the formulation of substantiated views to make optimal decisions for the design of preventive or repressive measures by those responsible for the implementation of policy in favor of the protection of public health. The main objective of the study is to present an innovative data-analysis system of COVID-19 disease progression in Greece and her border countries by real-time statistics about the epidemiological indicators. This system utilizes visualized data produced by an automated information system developed during the study, which is based on the analysis of large pandemic-related datasets, making extensive use of advanced machine learning methods. Finally, the aim is to support with up-to-date technological means optimal decisions in almost real time as well as the development of medium-term forecast of disease progression, thus assisting the competent bodies in taking appropriate measures for the effective management of the available health resources. Full article
(This article belongs to the Special Issue Advance in Machine Learning)
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Article
Lifetime Prediction Using a Tribology-Aware, Deep Learning-Based Digital Twin of Ball Bearing-Like Tribosystems in Oil and Gas
Processes 2021, 9(6), 922; https://doi.org/10.3390/pr9060922 - 24 May 2021
Viewed by 476
Abstract
The recent decline in crude oil prices due to global competition and COVID-19-related demand issues has highlighted the need for the efficient operation of an oil and gas plant. One such avenue is accurate predictions about the remaining useful life (RUL) of components [...] Read more.
The recent decline in crude oil prices due to global competition and COVID-19-related demand issues has highlighted the need for the efficient operation of an oil and gas plant. One such avenue is accurate predictions about the remaining useful life (RUL) of components used in oil and gas plants. A tribosystem is comprised of the surfaces in relative motion and the lubricant between them. Lubricant oils play a significant role in keeping any tribosystem such as bearings and gears working smoothly over the lifetime of the oil and gas plant. The lubricant oil needs replenishment from time to time to avoid component breakdown due to the increased presence of wear debris and friction between the sliding surfaces of bearings and gears. Traditionally, this oil change is carried out at pre-determined times. This paper explored the possibilities of employing machine learning to predict early failure behavior in sensor-instrumented tribosystems. Specifically, deep learning and tribological data obtained from sensors deployed on the components can provide more accurate predictions about the RUL of the tribosystem. This automated maintenance can improve the overall efficiency of the component. The present study aimed to develop a deep learning-based digital twin for accurately predicting the RUL of a tribosystem comprised of a ball bearing-like test apparatus, a four-ball tester, and lubricant oil. A commercial lubricant used in the offshore oil and gas components was tested for its extreme pressure performance, and its welding load was measured using a four-ball tester. Three accelerated deterioration tests was carried out on the four-ball tester at a load below the welding load. Based on the wear scar measurements obtained from the experimental tests, the RUL data were used to train a multivariate convolutional neural network (CNN). The training accuracy of the model was above 99%, and the testing accuracy was above 95%. This work involved the model-free learning prediction of the remaining useful lifetime of ball bearing-type contacts as a function of key sensor input data (i.e., load, friction, temperature). This model can be deployed for in-field tribological machine elements to trigger automated maintenance without explicitly measuring the wear phenomenon. Full article
(This article belongs to the Special Issue Advance in Machine Learning)
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Article
A Study on Standardization of Security Evaluation Information for Chemical Processes Based on Deep Learning
Processes 2021, 9(5), 832; https://doi.org/10.3390/pr9050832 - 10 May 2021
Viewed by 313
Abstract
Hazard and operability analysis (HAZOP) is one of the most commonly used hazard analysis methods in the petrochemical industry. The large amount of unstructured data in HAZOP reports has generated an information explosion which has led to a pressing need for technologies that [...] Read more.
Hazard and operability analysis (HAZOP) is one of the most commonly used hazard analysis methods in the petrochemical industry. The large amount of unstructured data in HAZOP reports has generated an information explosion which has led to a pressing need for technologies that can simplify the use of this information. In order to solve the problem that massive data are difficult to reuse and share, in this study, we propose a new deep learning framework for Chinese HAZOP documents to perform a named entity recognition (NER) task, aiming at the characteristics of HAZOP documents, such as polysemy, multi-entity nesting, and long-distance text. Specifically, the preprocessed data are input into an embeddings from language models (ELMo) and a double convolutional neural network (DCNN) model to extract rich character features. Meanwhile, a bidirectional long short-term memory (BiLSTM) network is used to extract long-distance semantic information. Finally, the results are decoded by a conditional random field (CRF), and then output. Experiments were carried out using the HAZOP report of a coal seam indirect liquefaction project. The experimental results for the proposed model showed that the accuracy rate of the optimal results reached 90.83, the recall rate reached 92.46, and the F-value reached the highest 91.76%, which was significantly improved as compared with other models. Full article
(This article belongs to the Special Issue Advance in Machine Learning)
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