Special Issue "Artificial Intelligence and Machine Learning Based Methods and Applications"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 October 2022 | Viewed by 1373

Special Issue Editors

Dr. Adrian Sergiu Darabant
E-Mail
Guest Editor
Department of Computer Science, Faculty of Mathematics and Computer Science, Babes Bolyai University, 1st Mihail Kogălniceanu Street, 400084 Cluj-Napoca, Romania
Interests: computer vision; deep learning; convolutional neural networks; unsupervised learning; methods; facial feature analysis; mathematical models
Dr. Diana-Laura Borza
E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Mathematics and Computer Science, Babes Bolyai University, 1st Mihail Kogălniceanu Street, 400084 Cluj-Napoca, Romania
Interests: deep learning; biometrics; visual surveillance; facial feature analysis

Special Issue Information

Dear Colleagues,

Recent developments in artificial intelligence and especially machine learning have led these fields of research from purely theoretic approaches to fully applied industrial research not only in computer science, but in pretty much every other conceivable domain as well.

Globally, artificial intelligence (AI) has become one of the core areas providing fundamental building blocks for computer vision systems, computational modeling, security threat assessment, systems mimicking biological intelligence, multiagent systems, data transformation methods, etc.

The purpose of this Special Issue is to provide a research-publishing environment where articles with the latest developments not only in theoretical mathematical aspects of AI and machine learning, but also practical applications of ML and AI in computer vision, vision systems, statistical learning, reinforcement learning, and deep learning, data analysis and filtering, data transformation, speech processing, clustering and classification, knowledge extraction and discovery, natural language processing, and parallel and distributed AI methods could be submitted.

Contributions are welcome on both theoretical and practical models. The selection criteria will be based on formal and technical soundness, experimental support, and the relevance of the contribution.

Dr. Adrian Sergiu Darabant
Dr. Diana-Laura Borza
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. Mathematics 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 1800 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

  • computer vision
  • classification and clustering
  • reinforcement learning
  • learning algorithms
  • pattern recognition
  • data filtering and transformation
  • parallelization in learning algorithms
  • probabilistic and statistical methods
  • deep neural networks
  • convolutional neural networks
  • adversarial systems
  • intelligent agents
  • evolutionary programming
  • text analysis
  • natural language processing (NLP)
  • feature extraction and analysis

Published Papers (2 papers)

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Research

Article
LSTM-Based Broad Learning System for Remaining Useful Life Prediction
Mathematics 2022, 10(12), 2066; https://doi.org/10.3390/math10122066 - 15 Jun 2022
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Abstract
Prognostics and health management (PHM) are gradually being applied to production management processes as industrial production is gradually undergoing a transformation, turning into intelligent production and leading to increased demands on the reliability of industrial equipment. Remaining useful life (RUL) prediction plays a [...] Read more.
Prognostics and health management (PHM) are gradually being applied to production management processes as industrial production is gradually undergoing a transformation, turning into intelligent production and leading to increased demands on the reliability of industrial equipment. Remaining useful life (RUL) prediction plays a pivotal role in this process. Accurate prediction results can effectively provide information about the condition of the equipment on which intelligent maintenance can be based, with many methods applied to this task. However, the current problems of inadequate feature extraction and poor correlation between prediction results and data still affect the prediction accuracy. To overcome these obstacles, we constructed a new fusion model that extracts data features based on a broad learning system (BLS) and embeds long short-term memory (LSTM) to process time-series information, named as the B-LSTM. First, the LSTM controls the transmission of information from the data to the gate mechanism, and the retained information generates the mapped features and forms the feature nodes. Then, the random feature nodes are supplemented by an activation function that generates enhancement nodes with greater expressive power, increasing the nonlinear factor in the network, and eventually the feature nodes and enhancement nodes are jointly connected to the output layer. The B-LSTM was experimentally used with the C-MAPSS dataset and the results of comparison with several mainstream methods showed that the new model achieved significant improvements. Full article
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Article
Correlation Assessment of the Performance of Associative Classifiers on Credit Datasets Based on Data Complexity Measures
Mathematics 2022, 10(9), 1460; https://doi.org/10.3390/math10091460 - 26 Apr 2022
Viewed by 388
Abstract
One of the four basic machine learning tasks is pattern classification. The selection of the proper learning algorithm for a given problem is a challenging task, formally known as the algorithm selection problem (ASP). In particular, we are interested in the behavior of [...] Read more.
One of the four basic machine learning tasks is pattern classification. The selection of the proper learning algorithm for a given problem is a challenging task, formally known as the algorithm selection problem (ASP). In particular, we are interested in the behavior of the associative classifiers derived from Alpha-Beta models applied to the financial field. In this paper, the behavior of four associative classifiers was studied: the One-Hot version of the Hybrid Associative Classifier with Translation (CHAT-OHM), the Extended Gamma (EG), the Naïve Associative Classifier (NAC), and the Assisted Classification for Imbalanced Datasets (ACID). To establish the performance, we used the area under the curve (AUC), F-score, and geometric mean measures. The four classifiers were applied over 11 datasets from the financial area. Then, the performance of each one was analyzed, considering their correlation with the measures of data complexity, corresponding to six categories based on specific aspects of the datasets: feature, linearity, neighborhood, network, dimensionality, and class imbalance. The correlations that arise between the measures of complexity of the datasets and the measures of performance of the associative classifiers are established; these results are expressed with Spearman’s Rho coefficient. The experimental results correctly indicated correlations between data complexity measures and the performance of the associative classifiers. Full article
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