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Applied Sciences
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Machine Learning and Soft Computing: Current Trends and Applications
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Machine Learning and Soft Computing: Current Trends and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 10796

Special Issue Editors

Dr. Ansel Yoan Rodríguez González

E-Mail Website
Guest Editor
CICESE-UT3, Tepic 63155, Mexico
Interests: data mining; pattern recognition; machine learning; evolutionary computation
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Ángel Álvarez Carmona

E-Mail Website
Guest Editor
Centro de Investigación en Matemáticas (CIMAT), Consejo Nacional de Ciencia y Tecnología (CONACyT), Mexico City 03940, Mexico
Interests: natural language processing; artificial intelligence

Special Issue Information

Dear Colleagues,

In contemporary times, the adoption of machine learning and soft computing techniques has established itself as a customary practice across research domains and industries such as healthcare, finance, manufacturing, energy, education, business management, and beyond. These methodologies have significantly bolstered decision-making capabilities and facilitated the automation of numerous processes. In order to stay abreast of the prevailing approaches within these realms, this Special Issue is dedicated to examining contributions that elucidate the latest trends and contemporary methodologies, within both industry and academia. Contributions encompass, though are not confined to, the following:

  • Advanced Machine Learning Algorithms
    • Deep learning architectures.
    • Reinforcement learning.
    • Ensemble methods.
    • Transfer learning.
  • Soft Computing Paradigms
    • Fuzzy systems.
    • Genetic algorithms.
    • Neural networks.
    • Swarm intelligence.
  • Natural Language Processing
    • Sentiment analysis.
    • Topic modeling.
    • Named entity recognition.
  • Interdisciplinary Applications
    • Healthcare informatics.
    • Financial prediction and analysis.
    • Image and speech recognition.
  • Explainability and Interpretability
    • Interpretable machine learning models.
    • Explainable AI approaches.
  • Ethical Considerations in Machine Learning
    • Bias and fairness.
    • Transparency and accountability.

We welcome both original research articles and comprehensive reviews that contribute to the understanding and development of machine learning and soft computing. Researchers are encouraged to present innovative solutions, share practical experiences, and highlight challenges and opportunities in this rapidly evolving field.

Yours sincerely,

Dr. Ansel Yoan Rodríguez González
Dr. Miguel Ángel Álvarez Carmona
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. Applied Sciences 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 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

  • advanced machine learning algorithms
  • deep learning architectures
  • reinforcement learning
  • ensemble methods
  • transfer learning soft computing paradigms
  • fuzzy systems
  • genetic algorithms
  • neural networks
  • swarm intelligence natural language processing
  • sentiment analysis
  • topic modeling
  • named entity recognition interdisciplinary applications
  • healthcare informatics
  • financial prediction and analysis
  • image and speech recognition explainability and interpretability
  • interpretable machine learning models
  • explainable AI approaches ethical considerations in machine learning
  • bias and fairness
  • transparency and accountability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

17 pages, 3053 KiB  
Article
Innovative EMD-Based Technique for Preventing Coffee Grinder Damage from Stones with FPGA Implementation
by Chiang Liang Kok, Yuwei Dai, Yit Yan Koh, Maoyang Xiang and Tee Hui Teo
Appl. Sci. 2025, 15(3), 1579; https://doi.org/10.3390/app15031579 - 4 Feb 2025
Viewed by 789
Abstract
Coffee is one of the most widely consumed beverages globally, with Americans averaging 3.1 cups per day. However, before coffee beans can be brewed into a drinkable form, they undergo several critical stages, including harvesting, processing, roasting, grinding, and extraction. During the processing [...] Read more.
Coffee is one of the most widely consumed beverages globally, with Americans averaging 3.1 cups per day. However, before coffee beans can be brewed into a drinkable form, they undergo several critical stages, including harvesting, processing, roasting, grinding, and extraction. During the processing and roasting phases, a significant challenge arises: stones that are similar in size and shape to coffee beans can inadvertently mix into the batch. These stones are difficult to detect using conventional methods, and their presence can have severe consequences. When stones are ground alongside coffee beans, they can cause significant damage to the grinder’s burrs. Commercial coffee grinders typically employ conical or flat burrs, which consist of two circular discs or an inner blade and a disc. These burrs undergo specialized heat treatment and surface processing to ensure durability and precision, making them highly expensive components. Replacing damaged burrs is not only costly but also requires meticulous calibration of the parallelism between the inner blade and the disc to maintain grinding quality. The introduction of stones into the grinding process can lead to equipment damage, resulting in operational downtime and financial losses. To address this issue, this paper proposes a novel method based on Empirical Mode Decomposition (EMD) for detecting stones in coffee beans. The approach analyzes the acoustic wave patterns generated when stones impact or rotate within the grinder. Full article
(This article belongs to the Special Issue Machine Learning and Soft Computing: Current Trends and Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Overall block diagram of the project.</p> Full article ">Figure 2
<p>Filtered signal feature.</p> Full article ">Figure 3
<p>Characteristic of fault mode.</p> Full article ">Figure 4
<p>Full evaluation setup.</p> Full article ">Figure 5
<p>FPGA implementation block diagram.</p> Full article ">Figure 6
<p>IMFs extraction design [<a href="#B31-applsci-15-01579" class="html-bibr">31</a>].</p> Full article ">Figure 7
<p>Module reuse design in IMFs extraction stage.</p> Full article ">Figure 8
<p>Simulation result of FPGA implementation.</p> Full article ">
16 pages, 1107 KiB  
Article
Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis
by Burçin Tutcu, Mehmet Kayakuş, Mustafa Terzioğlu, Güler Ferhan Ünal Uyar, Hasan Talaş and Filiz Yetiz
Appl. Sci. 2024, 14(17), 7459; https://doi.org/10.3390/app14177459 - 23 Aug 2024
Cited by 2 | Viewed by 2283
Abstract
IT is recognized as the engine of the digital world. The fact that this technology has multiple sub-sectors makes it the driving force of the economy. With these characteristics, the sector is becoming the center of attention of investors. Considering that investors prioritize [...] Read more.
IT is recognized as the engine of the digital world. The fact that this technology has multiple sub-sectors makes it the driving force of the economy. With these characteristics, the sector is becoming the center of attention of investors. Considering that investors prioritize profitability, it becomes a top priority for managers to make accurate and reliable profitability forecasts. The aim of this study is to estimate the profitability of IT sector firms traded in Borsa Istanbul using machine learning methods. In this study, the financial data of 13 technology firms listed in the Borsa Istanbul Technology index and operating between March 2000 and December 2023 were used. Return on assets (ROA) and return on equity (ROE) were estimated using machine learning methods such as neural networks, multiple linear regression and decision tree regression. The results obtained reveal that the performance of artificial neural networks (ANN) and multiple linear regression (MLR) are particularly effective. Full article
(This article belongs to the Special Issue Machine Learning and Soft Computing: Current Trends and Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>ANN structure.</p> Full article ">Figure 2
<p>R<sup>2</sup> Values of models for ROA.</p> Full article ">Figure 3
<p>RMSE values of models for ROA.</p> Full article ">Figure 4
<p>R<sup>2</sup> Values of models for ROE.</p> Full article ">Figure 5
<p>RMSE values of models for ROE.</p> Full article ">
29 pages, 10766 KiB  
Article
Machine Learning-Based Feature Extraction and Classification of EMG Signals for Intuitive Prosthetic Control
by Chiang Liang Kok, Chee Kit Ho, Fu Kai Tan and Yit Yan Koh
Appl. Sci. 2024, 14(13), 5784; https://doi.org/10.3390/app14135784 - 2 Jul 2024
Cited by 17 | Viewed by 6463
Abstract
Signals play a fundamental role in science, technology, and communication by conveying information through varying patterns, amplitudes, and frequencies. This paper introduces innovative methodologies for processing electromyographic (EMG) signals to develop artificial intelligence systems capable of decoding muscle activity for controlling arm movements. [...] Read more.
Signals play a fundamental role in science, technology, and communication by conveying information through varying patterns, amplitudes, and frequencies. This paper introduces innovative methodologies for processing electromyographic (EMG) signals to develop artificial intelligence systems capable of decoding muscle activity for controlling arm movements. The study investigates advanced signal processing techniques and machine learning classification algorithms using the GRABMyo dataset, aiming to enhance prosthetic control systems and rehabilitation technologies. A comprehensive analysis was conducted on signal processing techniques, including signal filtering and discrete wavelet transform (DWT), alongside a composite feature set comprising Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC), Slope Sign Changes (SSC), Root Mean Square (RMS), Enhanced Waveform Length (EWL), and Enhanced Mean Absolute Value (EMAV). These features, refined through Linear Discriminant Analysis (LDA) for dimensionality reduction, were classified using Support Vector Machine (SVM) algorithms. Signal filtering and DWT improved signal quality, facilitating better feature extraction, while the diverse feature set enhanced classification accuracy. LDA further improved accuracy by isolating the most informative features, and the SVM achieved optimal performance in decoding complex EMG patterns. Machine learning models, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), and the SVM, were evaluated, with the SVM outperforming the others. The significance of these results lies in their potential applications in prosthetic control systems and rehabilitation technologies. By accurately decoding muscle activity, the developed systems can facilitate more intuitive and responsive robotic arm movements, contributing to the advancement of innovative solutions for individuals requiring prosthetic devices or undergoing rehabilitation, hence improving the quality of life for users. This research marks a significant step forward in the integration of advanced signal processing and machine learning in the field of EMG analysis. Full article
(This article belongs to the Special Issue Machine Learning and Soft Computing: Current Trends and Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Overall process diagram for the approach of the research.</p> Full article ">Figure 2
<p>EMG-USB2+ multichannel amplifier by OTBioelecttronica [<a href="#B12-applsci-14-05784" class="html-bibr">12</a>].</p> Full article ">Figure 3
<p>GRABMyo dataset setup for electrodes on the forearm [<a href="#B12-applsci-14-05784" class="html-bibr">12</a>].</p> Full article ">Figure 4
<p>GRABMyo dataset folder and file exploration.</p> Full article ">Figure 5
<p>GRABMyo dataset flowchart for signal acquisition.</p> Full article ">Figure 6
<p>Available gesture list and selected research gestures.</p> Full article ">Figure 7
<p>Raw EMG signals captured from the wrist in the time domain.</p> Full article ">Figure 8
<p>Raw EMG signals captured from the forearm in the time domain.</p> Full article ">Figure 9
<p>Power Spectrum Density (PSD) analysis of raw EMG signals captured from the wrist in the frequency domain.</p> Full article ">Figure 10
<p>Power Spectrum Density (PSD) analysis of raw EMG signals captured from the forearm in the frequency domain.</p> Full article ">Figure 11
<p>PSD analysis of EMG signals captured from the wrist after average referencing.</p> Full article ">Figure 12
<p>PSD analysis of EMG signals captured from the forearm after average referencing.</p> Full article ">Figure 13
<p>PSD analysis of EMG signals captured from the wrist after the application of a Band-pass filter with a range of 10 Hz to 450 Hz.</p> Full article ">Figure 14
<p>PSD analysis of EMG signals captured from the wrist after the application of the 60 Hz Notch filter.</p> Full article ">Figure 15
<p>PSD analysis post filtering of EMG signals captured from the wrist.</p> Full article ">Figure 16
<p>PSD analysis post filtering of EMG signals captured from the forearm.</p> Full article ">Figure 17
<p>Discrete Wavelet Transform decomposition, block diagram form, with 4 levels of decomposition.</p> Full article ">Figure 18
<p>Discrete Wavelet Transform decomposition with 6 levels of decomposition.</p> Full article ">Figure 19
<p>Discrete Wavelet Transform decomposition, analysis form, with 4 levels of decomposition.</p> Full article ">Figure 20
<p>Comparison of signal distribution before and after Linear Discriminant Analysis (LDA) for 2 classes.</p> Full article ">Figure 21
<p>Visualization of K-Nearest Neighbor classes and classification method.</p> Full article ">Figure 22
<p>Visualization of Support Vector Machine classification methods [<a href="#B11-applsci-14-05784" class="html-bibr">11</a>].</p> Full article ">Figure 23
<p>Final process diagram illustrating the results of the research.</p> Full article ">Figure 24
<p>Scatter plot illustrating the classification of 215 EMG samples using the PCA–SVM model.</p> Full article ">Figure 25
<p>Scatter plot illustrating the classification of 215 EMG samples using the LDA–SVM model.</p> Full article ">Figure 26
<p>Methodology for processing the three sessions from the GRABMyo dataset.</p> Full article ">Figure 27
<p>Comparison of scatter plots between one session and the complete dataset of three sessions of data.</p> Full article ">Figure 28
<p>Confusion matrix for the SVM model using data from the complete dataset of three sessions of data.</p> Full article ">Figure 29
<p>Performance metrics for KNN, NB, and SVM classifiers using data from one session.</p> Full article ">Figure 30
<p>Comparing performance metrics for SVM classifiers utilizing data from one session and the complete dataset of three sessions of data.</p> Full article ">Figure 31
<p>Diagram depicting the schematic pathway of nerves extending from the brain to the hand [<a href="#B30-applsci-14-05784" class="html-bibr">30</a>,<a href="#B31-applsci-14-05784" class="html-bibr">31</a>,<a href="#B32-applsci-14-05784" class="html-bibr">32</a>,<a href="#B33-applsci-14-05784" class="html-bibr">33</a>].</p> Full article ">
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