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Mathematics
Special Issues
Learning Algorithms and Neural Networks
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Learning Algorithms and Neural Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 954

Special Issue Editor

Dr. Chungling Tu

E-Mail Website
Guest Editor
Department of Computer Systems Engineering, Tshwane University of Technology, Pretoria 0002, South Africa
Interests: neural networks; learning algorithms; optimization algorithms; mathematical modeling; AI applications

Special Issue Information

Dear Colleagues,

We are pleased to invite your contributions to a Special Issue focusing on learning algorithms and neural networks. With the rapid development of artificial intelligence (AI) systems and machine learning in recent years, learning algorithms and neural networks have gained significant attention due to their robust mathematical foundations and computational capabilities. Learning algorithms and neural networks have emerged as pivotal technologies, enabling a wide range of applications across fields such as industry, agriculture, healthcare, finance, robotics, networks, and autonomous systems. Their ability to model complex, high-dimensional data and improve decision making has made them indispensable in solving both theoretical and practical challenges in AI.

This Special Issue aims to bring together cutting-edge research that explores the development, application, and optimization of learning algorithms as well as neural networks. We invite submissions that focus on innovative approaches, methodologies, and applications, and contribute to a deeper understanding of how these technologies can be further advanced to address critical issues in AI-driven systems.

We welcome original research and review articles on (but not limited to) the following topics:

  • Neural network architectures and optimization.
  • Learning algorithms‘ theoretical advancements and optimization.
  • Innovative algorithms for reinforcement learning and deep learning models.
  • Mathematical modeling in AI.
  • AI in signal processing and information systems.
  • AI applications in cybersecurity and networking.
  • Ethical implications and challenges in neural network-driven AI systems.
  • AI in the IoT, robotics, and automation.
  • Improvements in unsupervised, supervised, and semi-supervised learning.

I look forward to receiving your contributions.

Dr. Chungling Tu
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. 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 2600 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

  • neural networks
  • learning algorithms
  • deep learning
  • optimization algorithms
  • supervised/unsupervised learning
  • AI applications
  • cybersecurity
  • networking technologies
  • data mining
  • IoT
  • robotics and automation
  • feature engineering

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

Published Papers (2 papers)

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Research

33 pages, 3576 KiB  
Article
Analyzing the Impact of Organic Food Consumption on Citizens Health Using Unsupervised Machine Learning
by Giulio Angiolini and Giovanna Maria Dimitri
Mathematics 2025, 13(8), 1272; https://doi.org/10.3390/math13081272 - 12 Apr 2025
Viewed by 196
Abstract
Despite the growing popularity of organic foods, research on their effects on human health, particularly regarding cancer and diabetes, remains limited. While some studies suggest potential health benefits, others yield conflicting results or lack sufficient evidence to draw conclusions. Understanding the causal relationship [...] Read more.
Despite the growing popularity of organic foods, research on their effects on human health, particularly regarding cancer and diabetes, remains limited. While some studies suggest potential health benefits, others yield conflicting results or lack sufficient evidence to draw conclusions. Understanding the causal relationship between organic food consumption and health outcomes is challenging, especially with limited datasets. Our study examines the correlation between organic food consumption and the prevalence of cancer and diabetes in European nations over time. We compared these findings with data from 100 Italian citizens regarding their perceptions of organic food’s health benefits collected through a novel questionnaire. To identify patterns, we applied Affinity Propagation clustering to group countries based on organic food consumption and disease prevalence. We also created an animated map to visualize cluster progression over time and used the Global Multiplexity Index to evaluate consistency. Our analysis revealed two subgroups of European countries exhibiting significant similarities in organic food consumption and health outcomes. The clustering analysis performed year-by-year on three variables across European nations using the Affinity Propagation algorithm revealed that two clusters consistently maximized the Global Multiplexity Index over time. The first cluster included Belgium, Finland, Ireland, Italy, and Spain, while the second comprised Bulgaria, Turkey, Romania, Ukraine, Czech Republic, Hungary, Poland, Greece, and Russia. These clusters displayed distinct trends concerning sustainable development goals (SDGs) related to organic farming and non-communicable diseases. Additionally, mapping SDG indicators along with geographic and socio-economic factors supported our findings. Moreover, we introduced a novel dataset and offered insights into both the European context and the Italian scenario, contributing to further research on organic food’s impact on public health. Full article
(This article belongs to the Special Issue Learning Algorithms and Neural Networks)
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40 pages, 6118 KiB  
Article
Single-Source and Multi-Source Cross-Subject Transfer Based on Domain Adaptation Algorithms for EEG Classification
by Rito Clifford Maswanganyi, Chunling Tu, Pius Adewale Owolawi and Shengzhi Du
Mathematics 2025, 13(5), 802; https://doi.org/10.3390/math13050802 - 27 Feb 2025
Viewed by 459
Abstract
Transfer learning (TL) has been employed in electroencephalogram (EEG)-based brain–computer interfaces (BCIs) to enhance performance for cross-session and cross-subject EEG classification. However, domain shifts coupled with a low signal-to-noise ratio between EEG recordings have been demonstrated to contribute to significant variations in EEG [...] Read more.
Transfer learning (TL) has been employed in electroencephalogram (EEG)-based brain–computer interfaces (BCIs) to enhance performance for cross-session and cross-subject EEG classification. However, domain shifts coupled with a low signal-to-noise ratio between EEG recordings have been demonstrated to contribute to significant variations in EEG neural dynamics from session to session and subject to subject. Critical factors—such as mental fatigue, concentration, and physiological and non-physiological artifacts—can constitute the immense domain shifts seen between EEG recordings, leading to massive inter-subject variations. Consequently, such variations increase the distribution shifts across the source and target domains, in turn weakening the discriminative knowledge of classes and resulting in poor cross-subject transfer performance. In this paper, domain adaptation algorithms, including two machine learning (ML) algorithms, are contrasted based on the single-source-to-single-target (STS) and multi-source-to-single-target (MTS) transfer paradigms, mainly to mitigate the challenge of immense inter-subject variations in EEG neural dynamics that lead to poor classification performance. Afterward, we evaluate the effect of the STS and MTS transfer paradigms on cross-subject transfer performance utilizing three EEG datasets. In this case, to evaluate the effect of STS and MTS transfer schemes on classification performance, domain adaptation algorithms (DAA)—including ML algorithms implemented through a traditional BCI—are compared, namely, manifold embedded knowledge transfer (MEKT), multi-source manifold feature transfer learning (MMFT), k-nearest neighbor (K-NN), and Naïve Bayes (NB). The experimental results illustrated that compared to traditional ML methods, DAA can significantly reduce immense variations in EEG characteristics, in turn resulting in superior cross-subject transfer performance. Notably, superior classification accuracies (CAs) were noted when MMFT was applied, with mean CAs of 89% and 83% recorded, while MEKT recorded mean CAs of 87% and 76% under the STS and MTS transfer paradigms, respectively. Full article
(This article belongs to the Special Issue Learning Algorithms and Neural Networks)
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