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Mathematics
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Advances in Algorithm Design and Machine Learning
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Advances in Algorithm Design and Machine Learning

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 October 2025 | Viewed by 3195

Special Issue Editors

Dr. Zhen Liu

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: data mining; big data and their applications; complex systems and network analysis; algorithm design; deep neural networks
Dr. Xiaodong Feng

E-Mail Website
Guest Editor
School of Information Management, Sun Yat-sen University, Guangzhou 510005, China
Interests: data ming; socia media mining; text anlytics; social networks; deep learning applications
Dr. Wenbo Xie

E-Mail Website
Guest Editor Assistant
School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, China
Interests: data mining; big data and their applications; algorithm design; human–machine coordination

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for the upcoming Special Issue, entitled “Advances in Algorithm Design and Machine Learning”. This Special Issue seeks to publish rigorous research articles that contribute to the advancement of algorithmic frameworks and machine learning methodologies. It will serve as a platform for disseminating research that demonstrates significant theoretical developments or practical applications.

We welcome submissions that cover a range of topics, including, but not limited to, the following:

  • Advanced optimization techniques for machine learning models;
  • Innovative deep learning methodologies;
  • Probabilistic and statistical models for machine intelligence;
  • Algorithms for large-scale data processing and analysis;
  • Reinforcement learning strategies for complex systems;
  • Approaches to transfer learning and domain adaptations;
  • Algorithm analysis for deep neural networks;
  • Algorithm design for natural language processing;
  • Algorithm design for computer vision;
  • Algorithm design for large-scale data annotations

Dr. Zhen Liu
Dr. Xiaodong Feng
Guest Editors

Dr. Wenbo Xie
Guest Editor Assistant

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

  • algorithm design
  • machine learning and deep learning
  • optimization techniques for machine learning
  • large-scale data processing
  • recommendation systems
  • complex systems and network analysis
  • deep neural networks
  • statistical learning
  • natural language processing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

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Research

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19 pages, 3076 KiB  
Article
Three-Stage Recursive Learning Technique for Face Mask Detection on Imbalanced Datasets
by Chi-Yi Tsai, Wei-Hsuan Shih and Humaira Nisar
Mathematics 2024, 12(19), 3104; https://doi.org/10.3390/math12193104 - 4 Oct 2024
Viewed by 1325
Abstract
In response to the COVID-19 pandemic, governments worldwide have implemented mandatory face mask regulations in crowded public spaces, making the development of automatic face mask detection systems critical. To achieve robust face mask detection performance, a high-quality and comprehensive face mask dataset is [...] Read more.
In response to the COVID-19 pandemic, governments worldwide have implemented mandatory face mask regulations in crowded public spaces, making the development of automatic face mask detection systems critical. To achieve robust face mask detection performance, a high-quality and comprehensive face mask dataset is required. However, due to the difficulty in obtaining face samples with masks in the real-world, public face mask datasets are often imbalanced, leading to the data imbalance problem in model training and negatively impacting detection performance. To address this problem, this paper proposes a novel recursive model-training technique designed to improve detection accuracy on imbalanced datasets. The proposed method recursively splits and merges the dataset based on the attribute characteristics of different classes, enabling more balanced and effective model training. Our approach demonstrates that the carefully designed splitting and merging of datasets can significantly enhance model-training performance. This method was evaluated using two imbalanced datasets. The experimental results show that the proposed recursive learning technique achieves a percentage increase (PI) of 84.5% in mean average precision (mAP@0.5) on the Kaggle dataset and of 186.3% on the Eden dataset compared to traditional supervised learning. Additionally, when combined with existing oversampling techniques, the PI on the Kaggle dataset further increases to 88.9%, highlighting the potential of the proposed method for improving detection accuracy in highly imbalanced datasets. Full article
(This article belongs to the Special Issue Advances in Algorithm Design and Machine Learning)
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Review

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34 pages, 2589 KiB  
Review
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining
by Yuxin You, Zhen Liu, Xiangchao Wen, Yongtao Zhang and Wei Ai
Mathematics 2025, 13(7), 1147; https://doi.org/10.3390/math13071147 - 31 Mar 2025
Viewed by 806
Abstract
Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. In recent years, significant progress has been made in this field through the development of graph neural networks (GNNs). However, GNNs are still [...] Read more.
Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. In recent years, significant progress has been made in this field through the development of graph neural networks (GNNs). However, GNNs are still deficient in generalizing to diverse graph data. Aiming to this issue, large language models (LLMs) could provide new solutions for graph mining tasks with their superior semantic understanding. In this review, we systematically review the combination and application techniques of LLMs and GNNs and present a novel taxonomy for research in this interdisciplinary field, which involves three main categories: GNN-driving-LLM(GdL), LLM-driving-GNN(LdG), and GNN-LLM-co-driving(GLcd). Within this framework, we reveal the capabilities of LLMs in enhancing graph feature extraction as well as improving the effectiveness of downstream tasks such as node classification, link prediction, and community detection. Although LLMs have demonstrated their great potential in handling graph-structured data, their high computational requirements and complexity remain challenges. Future research needs to continue to explore how to efficiently fuse LLMs and GNNs to achieve more powerful graph learning and reasoning capabilities and provide new impetus for the development of graph mining techniques. Full article
(This article belongs to the Special Issue Advances in Algorithm Design and Machine Learning)
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