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Mathematics
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Artificial Intelligence and Algorithms
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Artificial Intelligence and Algorithms

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 3130

Special Issue Editor

Prof. Dr. Fuhsing Wang

E-Mail Website
Guest Editor
Department of Information Management, Chinese Culture University Taiwan, Taipei, Taiwan
Interests: artificial intelligence; eLearning; interconnection networks; graph theory; algorithms

Special Issue Information

Dear Colleagues,

The Special Issue, “Artificial Intelligence and Algorithms”, aims to delve into the mathematical foundations and innovative advancements in the rapidly evolving fields of artificial intelligence (AI) and algorithm development. As AI technologies continue to revolutionize various domains, the role of sophisticated mathematical models and algorithms becomes increasingly critical in driving these innovations. This collection seeks to explore the theoretical and foundational aspects of algorithms that underlie AI systems. This includes work in algorithmic complexity, computational models, optimization techniques, and new paradigms in AI.

This Special Issue will highlight key areas where mathematics intersects with AI, including but not limited to optimization techniques, statistical methods, machine learning algorithms, and computational complexity. We seek to explore how mathematical theories and frameworks underpin the development of intelligent systems that can learn, adapt, and make decisions. Topics of interest include the design and analysis of algorithms for deep learning, reinforcement learning, natural language processing, and data mining, as well as the application of advanced mathematical tools such as linear algebra, probability theory, and differential equations in solving complex AI problems.

By bringing together contributions from mathematicians, computer scientists, and AI researchers, this Special Issue aims to provide a comprehensive overview of the state-of-the-art mathematical techniques driving AI. We invite theoretical and applied research papers that present novel mathematical models, innovative algorithms, and their practical applications in AI. This collection will serve as a valuable resource for researchers and practitioners looking to deepen their understanding of the mathematical principles that form the backbone of artificial intelligence and its algorithms.

Prof. Dr. Fuhsing Wang
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

  • machine learning
  • deep learning
  • neural networks
  • natural language processing
  • computer vision
  • adaptive learning systems
  • big data
  • algorithm optimization
  • cognitive computing

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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

40 pages, 3396 KB  
Article
Using KeyGraph and ChatGPT to Detect and Track Topics Related to AI Ethics in Media Outlets
by Wei-Hsuan Li and Hsin-Chun Yu
Mathematics 2025, 13(17), 2698; https://doi.org/10.3390/math13172698 - 22 Aug 2025
Viewed by 189
Abstract
This study examines the semantic dynamics and thematic shifts in artificial intelligence (AI) ethics over time, addressing a notable gap in longitudinal research within the field. In light of the rapid evolution of AI technologies and their associated ethical risks and societal impacts, [...] Read more.
This study examines the semantic dynamics and thematic shifts in artificial intelligence (AI) ethics over time, addressing a notable gap in longitudinal research within the field. In light of the rapid evolution of AI technologies and their associated ethical risks and societal impacts, the research integrates the theory of chance discovery with the KeyGraph algorithm to conduct topic detection through a keyword network built through iterative semantic exploration. ChatGPT is employed for semantic interpretation, enhancing both the accuracy and comprehensiveness of the detected topics. Guided by the double helix model of human–AI interaction, the framework incorporates a dual-layer validation process that combines cross-model semantic similarity analysis with expert-informed quality checks. An analysis of 24 authoritative AI ethics reports published between 2022 and 2024 reveals a consistent trend toward semantic stability, with high cross-model similarity across years (2022: 0.808 ± 0.023; 2023: 0.812 ± 0.013; 2024: 0.828 ± 0.015). Statistical tests confirm significant differences between single-cluster and multi-cluster topic structures (p < 0.05). The thematic findings indicate a shift in AI ethics discourse from a primary emphasis on technical risks to broader concerns involving institutional governance, societal trust, and the regulation of generative AI. Core keywords, such as bias, privacy, and ethics, recur across all years, reflecting the consolidation of an integrated governance framework that encompasses technological robustness, institutional adaptability, and social consensus. This dynamic semantic analysis framework contributes empirically to AI ethics governance and offers actionable insights for researchers and interdisciplinary stakeholders. Full article
(This article belongs to the Special Issue Artificial Intelligence and Algorithms)
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22 pages, 3030 KB  
Article
Research on Emotion-Based Inspiration Mechanism in Art Creation by Generative AI
by Yuan-Chih Yu
Mathematics 2025, 13(16), 2597; https://doi.org/10.3390/math13162597 - 14 Aug 2025
Viewed by 521
Abstract
This research presents a generative AI mechanism designed to assist artists in finding inspiration and developing ideas during their creative process by leveraging their emotions as a driving force. The proposed iterative inspiration cycle, complete with feedback loops, helps artists digitally capture their [...] Read more.
This research presents a generative AI mechanism designed to assist artists in finding inspiration and developing ideas during their creative process by leveraging their emotions as a driving force. The proposed iterative inspiration cycle, complete with feedback loops, helps artists digitally capture their creative emotions and use them as a guiding “vision” for creating artwork. Within the mechanism, the “Emotion Vision” images, generated from sketch line drawings and creative emotion prompts, are a medium designed to inspire artists. Experimental results demonstrate a positive inspirational effect, particularly in the creation of ‘Abstract Expressionism’ and ‘Impressionism’ artworks. In addition, we introduce the Emotion Vision Score metric, which quantifies the effectiveness of emotional inspiration. This metric evaluates how well “Emotion Vision” images inspire artists by balancing sketch intentions, creative emotions, and inspirational diversity, thus identifying the most effective images for inspiration. This novel mechanism integrates emotional intelligence into AI for art creation, allowing it to understand and replicate human emotion in its outputs. By enhancing emotional depth and ensuring consistency in generative AI, this research aims to advance digital art creation and contribute to the evolution of artistic expression through generative AI. Full article
(This article belongs to the Special Issue Artificial Intelligence and Algorithms)
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16 pages, 3451 KB  
Article
Session-Based Recommendation Method Using Popularity-Stratified Preference Modeling
by Yayelin Mo and Haowen Wang
Mathematics 2025, 13(6), 960; https://doi.org/10.3390/math13060960 - 14 Mar 2025
Viewed by 1542
Abstract
Large-scale offline evaluations of user–project interactions in recommendation systems are often biased due to inherent feedback loops. To address this, many studies have employed propensity scoring. In this work, we extend these methods to session-based recommendation tasks by refining propensity scoring calculations to [...] Read more.
Large-scale offline evaluations of user–project interactions in recommendation systems are often biased due to inherent feedback loops. To address this, many studies have employed propensity scoring. In this work, we extend these methods to session-based recommendation tasks by refining propensity scoring calculations to reflect dataset-specific characteristics. We evaluate our approach using neural models, specifically GRU4REC, and K-Nearest Neighbors (KNN)-based models on music and e-commerce datasets. GRU4REC is selected for its proven sequential model and computational efficiency, serving as a robust baseline against which we compare traditional methods. Our analysis of trend distributions reveals significant variations across datasets, and based on these insights, we propose a hierarchical approach that enhances model performance. Experimental results demonstrate substantial improvements over baseline models, providing a clear pathway for mitigating biases in session-based recommendation systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Algorithms)
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