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Mathematics
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Artificial Intelligence and Artificial Neural Networks
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Artificial Intelligence and Artificial Neural Networks

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 445

Special Issue Editors

Dr. Milos Antonijevic

E-Mail Website
Guest Editor
Faculty of Informatics and Computing, Singidunum University, 11000 Belgrade, Serbia
Interests: exploration of advanced deep learning algorithms; optimization techniques for improving the efficiency and accuracy of AI and neural network models; AI for cybersecurity; utilization of AI and ANNs in medical diagnosis
Dr. Dobrojevic Milos

E-Mail Website
Guest Editor
Faculty of Informatics and Computing, Singidunum University, 11000 Belgrade, Serbia
Interests: artificial inteligence; machine learning; AI models in computer vision; internet of things optimization and implementation

Special Issue Information

Dear Colleagues,

This Special Issue, “Artificial Intelligence and Artificial Neural Networks,” of Mathematics examines the most recent developments in AI and ANNs in many areas. It is about how these technologies are revolutionizing all aspects of problem-solving modeling. This Special Issue contains research articles dealing with utilizing AI and neural networks to solve complicated problems (both detection and prediction) across various sectors such as healthcare, human behavior, finance, security, engineering, sustainability, and autonomous systems. In this way, readers will be given an opportunity to keep themselves up-to-date with modern models, like deep learning, that imitate neural networks in the human brain and allow machines to learn from large datasets. Also, ethical considerations for developing AI models are discussed to ensure that these technologies benefit society. Consequently, anyone who is keen on comprehending how artificial intelligence and neural networks affect our society should consult this Special Issue, as it provides a complete and detailed guide in this matter. It offers valuable perspectives from both academic research and practical implementation.

With this Special Issue of Mathematics, we want to bring together the most recent theory and practical improvements to a number of AI models. As a result, research papers and review articles that look at how AI, especially ANNs, have evolved over time and problems where AI plays a big part are welcome.

Dr. Milos Antonijevic
Dr. Dobrojevic Milos
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 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

  • artificial intelligence
  • neural networks
  • deep learning
  • predictive modeling
  • transformers
  • machine learning
  • metaheuristic optimization
  • hyperparameter tuning

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Published Papers (1 paper)

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Research

18 pages, 945 KiB  
Article
Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation
by Min Huang, Zifeng Xie, Bo Sun and Ning Wang
Mathematics 2025, 13(4), 579; https://doi.org/10.3390/math13040579 (registering DOI) - 10 Feb 2025
Viewed by 75
Abstract
Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts regarding MSDA focus on enhancing multi-domain distributional alignment while omitting three issues, e.g., the class-level discrepancy quantification, the unavailability of noisy pseudo labels, and source transferability discrimination, potentially resulting [...] Read more.
Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts regarding MSDA focus on enhancing multi-domain distributional alignment while omitting three issues, e.g., the class-level discrepancy quantification, the unavailability of noisy pseudo labels, and source transferability discrimination, potentially resulting in suboptimal adaption performance. Therefore, we address these issues by proposing a prototype aggregation method that models the discrepancy between source and target domains at the class and domain levels. Our method achieves domain adaptation based on a group of prototypes (i.e., representative feature embeddings). A similarity score-based strategy is designed to quantify the transferability of each domain. At the class level, our method quantifies class-specific cross-domain discrepancy according to reliable target pseudo labels. At the domain level, our method establishes distributional alignment between noisy pseudo-labeled target samples and the source domain prototypes. Therefore, adaptation at the class and domain levels establishes a complementary mechanism to obtain accurate predictions. The results on three standard benchmarks demonstrate that our method outperforms most state-of-the-art methods. In addition, we provide further elaboration of the proposed method in light of the interpretable results obtained from the analysis experiments. Full article
(This article belongs to the Special Issue Artificial Intelligence and Artificial Neural Networks)
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