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Mathematics
Special Issues
Applications of Artificial Intelligence, Machine Learning and Data Science
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Applications of Artificial Intelligence, Machine Learning and Data Science

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 907

Special Issue Editors

Dr. Yufeng Yu

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Guest Editor
Department of Statistics, Guangzhou University, Guangzhou 510006, China
Interests: statistical machine learning; pattern recognition; data mining; computer vision; biomedical image processing
Dr. Guoxia Xu

E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjovik, Norway
Interests: pattern recognition; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hu Zhu

E-Mail Website
Guest Editor
School of Communication and Information Engineering, Nanjing University of Post and Tele Communications, Nanjing, China
Interests: tensor analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current Special Issue focuses on the applications of mathematical methods in artificial intelligence, machine learning, and data science. Over the past decade, mathematical theories and methods have driven the rapid development of new technologies in artificial intelligence, machine learning, and data science. These technologies can match or even exceed human performance levels in a variety of applications. These advancements have the potential to enable new high-impact applications in different fields. We encourage researchers to contribute to this Special Issue, including, but not limited to, the following subject areas: deep learning models and their applications, machine learning methodologies and theoretical analysis, image processing technologies and applications, computer vision methods and algorithms, and data mining and data analytics.

Dr. Yufeng Yu
Dr. Guoxia Xu
Prof. Dr. Hu Zhu
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

  • machine learning
  • deep learning
  • data mining
  • computer vision
  • artificial intelligence
  • image processing
  • pattern recognition

Published Papers (2 papers)

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Research

18 pages, 7887 KiB  
Article
A Two-Stage Method for Aerial Tracking in Adverse Weather Conditions
by Yuan Feng, Xinnan Xu, Nuoyi Chen, Quanjian Song and Lufang Zhang
Mathematics 2024, 12(8), 1216; https://doi.org/10.3390/math12081216 - 18 Apr 2024
Viewed by 285
Abstract
To tackle the issue of aerial tracking failure in adverse weather conditions, we developed an innovative two-stage tracking method, which incorporates a lightweight image restoring model DADNet and an excellent pretrained tracker. Our method begins by restoring the degraded image, which yields a [...] Read more.
To tackle the issue of aerial tracking failure in adverse weather conditions, we developed an innovative two-stage tracking method, which incorporates a lightweight image restoring model DADNet and an excellent pretrained tracker. Our method begins by restoring the degraded image, which yields a refined intermediate result. Then, the tracker capitalizes on this intermediate result to produce precise tracking bounding boxes. To expand the UAV123 dataset to various weather scenarios, we estimated the depth of the images in the dataset. Our method was tested on two famous trackers, and the experimental results highlighted the superiority of our method. The comparison experiment’s results also validated the dehazing effectiveness of our restoration model. Additionally, the components of our dehazing module were proven efficient through ablation studies. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence, Machine Learning and Data Science)
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16 pages, 1391 KiB  
Article
Dynamic Merging for Optimal Onboard Resource Utilization: Innovating Mission Queue Constructing Method in Multi-Satellite Spatial Information Networks
by Jun Long, Shangpeng Wang, Yakun Huo, Limin Liu and Huilong Fan
Mathematics 2024, 12(7), 986; https://doi.org/10.3390/math12070986 - 26 Mar 2024
Viewed by 414
Abstract
The purpose of constructing onboard observation mission queues is to improve the execution efficiency of onboard tasks and reduce energy consumption, representing a significant challenge in achieving efficient global military reconnaissance and target tracking. Existing research often focuses on the aspect of task [...] Read more.
The purpose of constructing onboard observation mission queues is to improve the execution efficiency of onboard tasks and reduce energy consumption, representing a significant challenge in achieving efficient global military reconnaissance and target tracking. Existing research often focuses on the aspect of task scheduling, aiming at optimizing the efficiency of single-task execution, while neglecting the complex dependencies that might exist between multiple tasks and payloads. Moreover, traditional task scheduling schemes are no longer suitable for large-scale tasks. To effectively reduce the number of tasks within the network, we introduce a network aggregation graph model based on multiple satellites and tasks, and propose a task aggregation priority dynamic calculation algorithm based on graph computations. Subsequently, we present a dynamic merging-based method for multi-satellite, multi-task aggregation, a novel approach for constructing onboard mission queues that can dynamically optimize the task queue according to real-time task demands and resource status. Simulation experiments demonstrate that, compared to baseline algorithms, our proposed task aggregation method significantly reduces the task size by approximately 25% and effectively increases the utilization rate of onboard resources. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence, Machine Learning and Data Science)
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