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Mathematics
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Artificial Intelligence and Data Science, 2nd Edition
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Artificial Intelligence and Data Science, 2nd Edition

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

Deadline for manuscript submissions: 11 August 2025 | Viewed by 631

Special Issue Editors

Dr. Shuo Yu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Dalian University of Technology, Dalian 116078, China
Interests: data science; network science; knowledge science; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Feng Xia

E-Mail Website
Guest Editor
School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia
Interests: data science; artificial intelligence; graph learning; anomaly detection; systems engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Building on the success of the first edition, we are pleased to announce the second edition of our Special Issue on "Artificial Intelligence and Data Science, 2nd Edition".

Data science serves as the core theory and methodology for extracting valuable insights from data. The rapid evolution of artificial intelligence (AI) technologies has significantly expanded and enriched the field of data science, driving transformative impacts across various domains, including cybersecurity, healthcare, fraud detection, transportation, and more. By integrating advanced AI methodologies with data science, researchers and practitioners have developed hybrid approaches that enable the seamless transition from data to information, knowledge, and actionable decisions.

Despite the remarkable progress in big data and AI technologies, the theoretical frameworks and technical mechanisms that underpin their success remain in a nascent stage. Isolated advancements in either AI or data science are insufficient to sustain the growth of intelligent, data-driven applications. Therefore, a deeper exploration of the fundamental theories and interdisciplinary approaches is urgently needed to propel both fields forward and unlock their full potential in addressing real-world challenges.

This Special Issue invites submissions that aim to address the following critical questions:

  • How can interdisciplinary approaches break the barriers between methodologies and theories to further advance AI and data science?
  • What will the new paradigms of AI and data science look like?
  • How can AI and data science technologies achieve greater impact in practical applications?

We welcome original research articles and reviews that explore innovative theories, methodologies, and applications at the intersection of AI and data science. Topics of interest include, but are not limited to, the following:

  • Knowledge-driven AI technologies;
  • Advanced deep learning approaches, such as fairness learning;
  • Security, trust, and privacy in AI and data science;
  • Few-shot, one-shot, and zero-shot learning methodologies;
  • Data governance strategies and frameworks;
  • Intelligent computing paradigms, including auto machine learning and lifelong learning;
  • Applications in urgent domains, such as anomaly detection;
  • Complexity theory and its implications for AI and data science;
  • High-performance computing for large-scale AI models;
  • Big data technologies and their applications;
  • Data analytics and visualization techniques;
  • Real-world applications, including healthcare, transportation, and beyond.

We look forward to your contributions and encourage you to submit your high-quality work to this Special Issue. Together, let us continue to push the boundaries of AI and data science, fostering innovation and real-world impact.

Dr. Shuo Yu
Prof. Dr. Feng Xia
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

  • intelligent computing such as auto machine learning, lifelong learning, etc.
  • complexity theory
  • high-performance computing
  • big data technologies and applications

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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

16 pages, 3735 KiB  
Article
A Novel Trustworthy Toxic Text Detection Method with Entropy-Oriented Invariant Representation Learning for Portuguese Community
by Wenting Fan, Haoyan Song and Jun Zhang
Mathematics 2025, 13(13), 2136; https://doi.org/10.3390/math13132136 - 30 Jun 2025
Abstract
With the rapid development of digital technologies, data-driven methods have demonstrated commendable performance in the toxic text detection task. However, several challenges remain unresolved, including the inability to fully capture the nuanced semantic information embedded in text languages, the lack of robust mechanisms [...] Read more.
With the rapid development of digital technologies, data-driven methods have demonstrated commendable performance in the toxic text detection task. However, several challenges remain unresolved, including the inability to fully capture the nuanced semantic information embedded in text languages, the lack of robust mechanisms to handle the inherent uncertainty of text languages, and the utilization of static fusion strategies for multi-view information. To address these issues, this paper proposes a comprehensive and dynamic toxic text detection method. Specifically, we design a multi-view feature augmentation module by combining bidirectional long short-term memory and BERT as a dual-stream framework. This module captures a more holistic representation of semantic information by learning both local and global features of texts. Next, we introduce an entropy-oriented invariant learning module by minimizing the conditional entropy between view-specific representations to align consistent information, thereby enhancing the representation generalization. Meanwhile, we devise a trustworthy text recognition module by defining the Dirichlet function to model uncertainty estimation of text prediction. And then, we perform the evidence-based information fusion strategy to dynamically aggregate decision information between views with the help of the Dirichlet distribution. Through these components, the proposed method aims to overcome the limitations of traditional methods and provide a more accurate and reliable solution for toxic language detection. Finally, extensive experiments on the two real-world datasets show the effectiveness and superiority of the proposed method in comparison with seven methods. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science, 2nd Edition)
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36 pages, 9139 KiB  
Article
On the Synergy of Optimizers and Activation Functions: A CNN Benchmarking Study
by Khuraman Aziz Sayın, Necla Kırcalı Gürsoy, Türkay Yolcu and Arif Gürsoy
Mathematics 2025, 13(13), 2088; https://doi.org/10.3390/math13132088 - 25 Jun 2025
Viewed by 211
Abstract
In this study, we present a comparative analysis of gradient descent-based optimizers frequently used in Convolutional Neural Networks (CNNs), including SGD, mSGD, RMSprop, Adadelta, Nadam, Adamax, Adam, and the recent EVE optimizer. To explore the interaction between optimization strategies and activation functions, we [...] Read more.
In this study, we present a comparative analysis of gradient descent-based optimizers frequently used in Convolutional Neural Networks (CNNs), including SGD, mSGD, RMSprop, Adadelta, Nadam, Adamax, Adam, and the recent EVE optimizer. To explore the interaction between optimization strategies and activation functions, we systematically evaluate all combinations of these optimizers with four activation functions—ReLU, LeakyReLU, Tanh, and GELU—across three benchmark image classification datasets: CIFAR-10, Fashion-MNIST (F-MNIST), and Labeled Faces in the Wild (LFW). Each configuration was assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, mean absolute error (MAE), and mean squared error (MSE). All experiments were performed using k-fold cross-validation to ensure statistical robustness. Additionally, two-way ANOVA was employed to validate the significance of differences across optimizer–activation combinations. This study aims to highlight the importance of jointly selecting optimizers and activation functions to enhance training dynamics and generalization in CNNs. We also consider the role of critical hyperparameters, such as learning rate and regularization methods, in influencing optimization stability. This work provides valuable insights into the optimizer–activation interplay and offers practical guidance for improving architectural and hyperparameter configurations in CNN-based deep learning models. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science, 2nd Edition)
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