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Symmetry
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Machine Learning and Data Analysis II
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Machine Learning and Data Analysis II

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4813

Special Issue Editor

Dr. Marcin Michalak

E-Mail Website
Guest Editor
Department of Computer Networks and Systems, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: image processing; data mining; machine learning; pattern recognition; rough set theory; biclustering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the great success of our Special Issue "Machine Learning and Data Analysis" we decided to set up a second volume.

There is no need to convince anyone about the huge influence of theoretical models of machine learning or data analysis techniques on our present way of living. They influence many science disciplines including industry, medicine, transport, and many others. We may observe how different approaches are mixed to become a new and complete model: classifiers for image analysis as well as image pattern recognition algorithms for classification; neural networks for clustering, classification or time series prediction; feature selection and extraction algorithms for preprocessing step of many of above mentioned applications.

The topics of the Special Issue include but are not limited to the following:

  • Supervised learning;
  • Unsupervised learning;
  • Time series analysis;
  • Descriptive analysis;
  • Biclustering;
  • Genetic algorithms;
  • ML & DM applications;
  • Artificial neural networks;
  • Deep learning;
  • Decision support systems;
  • Anomaly detection;
  • Image analysis;
  • Pattern recognition.

Welcome to read the publications in "Machine Learning and Data Analysis" at https://www.mdpi.com/journal/symmetry/special_issues/Machine_Learning_Data_Analysis.

Dr. Marcin Michalak
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. Symmetry 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 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

  • machine learning
  • data analysis
  • process modelling
  • time series prediction

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Related Special Issue

Published Papers (5 papers)

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Research

23 pages, 5188 KiB  
Article
Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Tests
by Andrea Coccaro, Marco Letizia, Humberto Reyes-González and Riccardo Torre
Symmetry 2024, 16(8), 942; https://doi.org/10.3390/sym16080942 - 23 Jul 2024
Cited by 6 | Viewed by 765
Abstract
Normalizing flows have emerged as a powerful brand of generative models, as they not only allow for efficient sampling of complicated target distributions but also deliver density estimation by construction. We propose here an in-depth comparison of coupling and autoregressive flows, both based [...] Read more.
Normalizing flows have emerged as a powerful brand of generative models, as they not only allow for efficient sampling of complicated target distributions but also deliver density estimation by construction. We propose here an in-depth comparison of coupling and autoregressive flows, both based on symmetric (affine) and non-symmetric (rational quadratic spline) bijectors, considering four different architectures: real-valued non-Volume preserving (RealNVP), masked autoregressive flow (MAF), coupling rational quadratic spline (C-RQS), and autoregressive rational quadratic spline (A-RQS). We focus on a set of multimodal target distributions of increasing dimensionality ranging from 4 to 400. The performances were compared by means of different test statistics for two-sample tests, built from known distance measures: the sliced Wasserstein distance, the dimension-averaged one-dimensional Kolmogorov–Smirnov test, and the Frobenius norm of the difference between correlation matrices. Furthermore, we included estimations of the variance of both the metrics and the trained models. Our results indicate that the A-RQS algorithm stands out both in terms of accuracy and training speed. Nonetheless, all the algorithms are generally able, without too much fine-tuning, to learn complicated distributions with limited training data and in a reasonable time of the order of hours on a Tesla A40 GPU. The only exception is the C-RQS, which takes significantly longer to train, does not always provide good accuracy, and becomes unstable for large dimensionalities. All algorithms were implemented using TensorFlow2 and TensorFlow Probability and have been made available on GitHub. Full article
(This article belongs to the Special Issue Machine Learning and Data Analysis II)
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21 pages, 3192 KiB  
Article
Cutting-Edge Machine Learning Techniques for Accurate Prediction of Agglomeration Size in Water–Alumina Nanofluids
by Behzad Vaferi, Mohsen Dehbashi and Ali Hosin Alibak
Symmetry 2024, 16(7), 804; https://doi.org/10.3390/sym16070804 - 27 Jun 2024
Viewed by 787
Abstract
Nanoparticle agglomeration is one of the most problematic phenomena during nanofluid synthesis by a two-step procedure. Understanding and accurately estimating agglomeration size is crucial, as it significantly affects nanofluids’ properties, behavior, and successful applications. To the best of our knowledge, the literature has [...] Read more.
Nanoparticle agglomeration is one of the most problematic phenomena during nanofluid synthesis by a two-step procedure. Understanding and accurately estimating agglomeration size is crucial, as it significantly affects nanofluids’ properties, behavior, and successful applications. To the best of our knowledge, the literature has not yet applied machine learning methods to estimate alumina agglomeration size in water-based nanofluids. So, this research employs a range of machine learning models—Random Forest, Adaptive Boosting, Extra Trees, Categorical Boosting, and Multilayer Perceptron Neural Networks—to predict alumina agglomeration sizes in water-based nanofluids. To this end, a comprehensive experimental database, including 345 alumina agglomeration sizes in water-based nanofluids, compiled from 29 various sources from the literature, is utilized to train these models and monitor their generalization ability in the testing stage. The models estimate agglomeration size based on multiple factors: alumina concentration, ultrasonic time, power, frequency, temperature, surfactant type and concentration, and pH levels. The relevancy test based on the Pearson method clarifies that Al2O3 agglomeration size in water primarily depends on ultrasonic frequency, ultrasonic power, alumina concentration in water, and surfactant concentration. Comparative analyses based on numerical and graphical techniques reveal that the Categorical Boosting model surpasses others in accurately simulating this complex phenomenon. It effectively captures the intricate relationships between key features and alumina agglomeration size, achieving an average absolute relative deviation of 6.75%, a relative absolute error of 12.83%, and a correlation coefficient of 0.9762. Furthermore, applying the leverage method to the experimental data helps identify two problematic measurements within the database. These results validate the effectiveness of the Categorical Boosting model and contribute to the broader goal of enhancing our understanding and control of nanofluid properties, thereby aiding in improving their practical applications. Full article
(This article belongs to the Special Issue Machine Learning and Data Analysis II)
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21 pages, 5171 KiB  
Article
Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study
by Saadin Oyucu, Sezer Dümen, İremnur Duru, Ahmet Aksöz and Emre Biçer
Symmetry 2024, 16(4), 436; https://doi.org/10.3390/sym16040436 - 5 Apr 2024
Cited by 3 | Viewed by 1042
Abstract
Li-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more [...] Read more.
Li-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more effective battery management systems that can predict and promptly report any operational discrepancies. To achieve this, an array of machine learning (ML) and artificial intelligence (AI) methodologies have been employed to analyze data from Li-ion batteries, facilitating the estimation of critical parameters like state of charge (SoC) and state of health (SoH). The continuous enhancement of ML and AI algorithm efficiency remains a pivotal focus of scholarly inquiry. Our study distinguishes itself by separately evaluating traditional machine learning frameworks and advanced deep learning paradigms to determine their respective efficacy in predictive modeling. We dissected the performances of an assortment of models, spanning from conventional ML techniques to sophisticated, hybrid deep learning constructs. Our investigation provides a granular analysis of each model’s utility, promoting an informed and strategic integration of ML and AI in Li-ion battery state of health prognostics. Specifically, a utilization of machine learning algorithms such as Random Forests (RFs) and eXtreme Gradient Boosting (XGBoost), alongside regression models like Elastic Net and foundational neural network approaches including Multilayer Perceptron (MLP) were studied. Furthermore, our research investigated the enhancement of time series analysis using intricate models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their outcomes with those of hybrid models, including a RNN-long short-term memory (LSTM), CNN-LSTM, CNN-Gated Recurrent Unit (GRU) and RNN-GRU. Comparative evaluations reveal that the RNN-LSTM configuration achieved a Mean Squared Error (MSE) of 0.043, R-Squared of 0.758, Root Mean Square Error (RMSE) of 0.208, and Mean Absolute Error (MAE) of 0.124, whereas the CNN-LSTM framework reported an MSE of 0.039, R-Squared of 0.782, RMSE of 0.197, and MAE of 0.122, underscoring the potential of deep learning-based hybrid models in advancing the accuracy of battery state of health assessments. Full article
(This article belongs to the Special Issue Machine Learning and Data Analysis II)
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21 pages, 4286 KiB  
Article
Velocity Estimations in Blood Microflows via Machine Learning Symmetries
by Gerardo Alfonso Perez and Jaime Virgilio Colchero Paetz
Symmetry 2024, 16(4), 428; https://doi.org/10.3390/sym16040428 - 4 Apr 2024
Cited by 1 | Viewed by 728
Abstract
Improving velocity forecasts of blood microflows could be useful in biomedical applications. We focus on estimating the velocity of the blood in capillaries. Modeling blood microflow in capillaries is a complex process. In this paper, we use artificial intelligence techniques for this modeling: [...] Read more.
Improving velocity forecasts of blood microflows could be useful in biomedical applications. We focus on estimating the velocity of the blood in capillaries. Modeling blood microflow in capillaries is a complex process. In this paper, we use artificial intelligence techniques for this modeling: more precisely, artificial neural networks (ANNs). The selected model is able to accurately forecast the velocity, with an R2 of 0.8992 comparing the forecast with the actual velocity. A key part of ANN model creation is selecting the appropriate parameters for the ANN, such as the number of neurons, the number of layers and the type of training algorithm used. A grid approach with 327,600 simulations was used. It is shown that there are substantial, statistically significant differences when different types of ANN structures are used. It is also shown that the proposed model is robust regarding the initial random initialization of weights in the ANN. Additionally, the sensitivity of the selected models to additional noise was also tested. Full article
(This article belongs to the Special Issue Machine Learning and Data Analysis II)
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19 pages, 532 KiB  
Article
Shifting Pattern Biclustering and Boolean Reasoning Symmetry
by Marcin Michalak and Jesús S. Aguilar-Ruiz
Symmetry 2023, 15(11), 1977; https://doi.org/10.3390/sym15111977 - 26 Oct 2023
Viewed by 933
Abstract
There are several goals of the two-dimensional data analysis: one may be interested in searching for groups of similar objects (clustering), another one may be focused on searching for some dependencies between a specified one and other variables (classification, regression, associate rules induction), [...] Read more.
There are several goals of the two-dimensional data analysis: one may be interested in searching for groups of similar objects (clustering), another one may be focused on searching for some dependencies between a specified one and other variables (classification, regression, associate rules induction), and finally, some may be interested in serching for well-defined patterns in the data called biclusters. It was already proved that there exists a mathematically proven symmetry between some patterns in the matrix and implicants of data-defined Boolean function. This paper provides the new look for a specific pattern search—the pattern named the δ-shifting pattern. The shifting pattern is interesting, as it accounts for constant fluctuations in data, i.e., it captures situations in which all the values in the pattern move up or down for one dimension, maintaining the range amplitude for all the dimensions. Such a behavior is very common in real data, e.g., in the analysis of gene expression data. In such a domain, a subset of genes might go up or down for a subset of patients or experimental conditions, identifying functionally coherent categories. A δ-shifting pattern meets the necessity of shifting pattern induction together with the bias of the real values acquisition where the original shifts may be disturbed with some outer conditions. Experiments with a real dataset show the potential of our approach at finding biclusters with δ-shifting patterns, providing excellent performance. It was possible to find the 12×9 pattern in the 112×9 input data with MSR=0.00653. The experiments also revealed that δ-shifting patterns are quite difficult to be found by some well-known methods of biclustering, as these are not designed to focus on shifting patterns—results comparable due to MSR had much more variability (in terms of δ) than patterns found with Boolean reasoning. Full article
(This article belongs to the Special Issue Machine Learning and Data Analysis II)
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