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Mathematics
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Mathematical and Computing Sciences for Artificial Intelligence
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Mathematical and Computing Sciences for Artificial Intelligence

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1977

Special Issue Editor

Prof. Dr. Chong-zhi Gao

E-Mail Website
Guest Editor
School of Computer Science, Guangzhou University, Guangzhou, China
Interests: security and privacy in machine learning and artificial intelligence

Special Issue Information

Dear Colleagues,

​The field of artificial intelligence relies on a deep understanding of mathematics, statistics and computer science to create algorithms that can learn from data and make intelligent decisions. However, due to the lack of data and bias in data, as well as the complexity of real-world systems, there are still many challenges in this field, including mathematical foundations and modelling in artificial intelligence, better optimization algorithms, interpretability of artificial intelligence, and building AI algorithms to solve specific application problems. Since mathematics is the foundation of artificial intelligence, and the integration of mathematical and computing sciences plays a crucial role in advancing AI research, this Special Issue aims to explore the latest developments, methodologies, and applications that highlight the synergy between mathematics, computing, and AI. We welcome original research papers addressing various aspects of mathematical and computing sciences for artificial intelligence. Topics of interest include, but are not limited to:

  • Optimization algorithms and machine learning;
  • Probabilistic modeling and Bayesian inference in AI;
  • Reinforcement learning and control theory;
  • Adversarial attacks and defense in AI;
  • Game theory and AI decision;
  • Mathematical approaches to explainable AI;
  • Graph theory and network analysis for AI systems;
  • Natural language processing and computational linguistics;
  • Mathematical modeling for computer vision;
  • The applications of AI in the field of medical sciences.

Prof. Dr. Chong-zhi Gao
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.

Published Papers (2 papers)

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Research

18 pages, 426 KiB  
Article
Optimizing Attribute Reduction in Multi-Granularity Data through a Hybrid Supervised–Unsupervised Model
by Zeyuan Fan, Jianjun Chen, Hongyang Cui, Jingjing Song and Taihua Xu
Mathematics 2024, 12(10), 1434; https://doi.org/10.3390/math12101434 - 7 May 2024
Viewed by 392
Abstract
Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively [...] Read more.
Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have numerous shortcomings, such as addressing complex constraints and conducting multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This novel method stands out by addressing complex constraints and facilitating multi-perspective effectiveness evaluations. It has several advantages: (1) it combines supervised and unsupervised learning methods, allowing for nuanced data interpretation and enhanced attribute selection; (2) by incorporating multi-granularity structures, the algorithm can analyze data at various levels of granularity. This allows for a more detailed understanding of data characteristics at each level, which can be crucial for complex datasets; and (3) by using neighborhood relations instead of indiscernibility relations, the method effectively handles uncertain and fuzzy data, making it suitable for real-world datasets that often contain imprecise or incomplete information. It not only selects the optimal granularity level or attribute set based on specific requirements, but also demonstrates its versatility and robustness through extensive experiments on 15 UCI datasets. Comparative analyses against six established attribute reduction algorithms confirms the superior reliability and consistency of our proposed method. This research not only enhances the understanding of attribute reduction mechanisms, but also sets a new benchmark for future explorations in the field. Full article
(This article belongs to the Special Issue Mathematical and Computing Sciences for Artificial Intelligence)
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17 pages, 1081 KiB  
Article
Malicious Traffic Classification via Edge Intelligence in IIoT
by Maoli Wang, Bowen Zhang, Xiaodong Zang, Kang Wang and Xu Ma
Mathematics 2023, 11(18), 3951; https://doi.org/10.3390/math11183951 - 17 Sep 2023
Viewed by 1031
Abstract
The proliferation of smart devices in the 5G era of industrial IoT (IIoT) produces significant traffic data, some of which is encrypted malicious traffic, creating a significant problem for malicious traffic detection. Malicious traffic classification is one of the most efficient techniques for [...] Read more.
The proliferation of smart devices in the 5G era of industrial IoT (IIoT) produces significant traffic data, some of which is encrypted malicious traffic, creating a significant problem for malicious traffic detection. Malicious traffic classification is one of the most efficient techniques for detecting malicious traffic. Although it is a labor-intensive and time-consuming process to gather large labeled datasets, the majority of prior studies on the classification of malicious traffic use supervised learning approaches and provide decent classification results when a substantial quantity of labeled data is available. This paper proposes a semi-supervised learning approach for classifying malicious IIoT traffic. The approach utilizes the encoder–decoder model framework to classify the traffic, even with a limited amount of labeled data available. We sample and normalize the data during the data-processing stage. In the semi-supervised model-building stage, we first pre-train a model on a large unlabeled dataset. Subsequently, we transfer the learned weights to a new model, which is then retrained using a small labeled dataset. We also offer an edge intelligence model that considers aspects such as computation latency, transmission latency, and privacy protection to improve the model’s performance. To achieve the lowest total latency and to reduce the risk of privacy leakage, we first create latency and privacy-protection models for each local, edge, and cloud. Then, we optimize the total latency and overall privacy level. In the study of IIoT malicious traffic classification, experimental results demonstrate that our method reduces the model training and classification time with 97.55% accuracy; moreover, our approach boosts the privacy-protection factor. Full article
(This article belongs to the Special Issue Mathematical and Computing Sciences for Artificial Intelligence)
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