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Mathematics
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Advanced Image Processing and Computational Intelligence: Methodologies and Applications
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Advanced Image Processing and Computational Intelligence: Methodologies and Applications

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 2745

Special Issue Editors

Dr. Qi Zhang

E-Mail Website
Guest Editor
Faculty of Data Science, City University of Macau, Macau SAR 999078, China
Interests: machine learning; smart healthcare; pattern recognition; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Jianhang Zhou

E-Mail Website
Guest Editor
Department of Intelligent Media, Institute of Scientific and Industrial Research (SANKEN), Osaka University, Suita 565-0871, Japan
Interests: sparse representation; subspace learning; graph learning; medical biometrics

Special Issue Information

Dear Colleagues,

Image processing is a broad area, including image enhancement, feature extraction, registration, segmentation, classification, etc. It is widely used in our daily lives. For instance, we can utilize image denoising techniques to process photos taken from mobile phones and then enhance the image quality of these photos. Generally, image processing involves many mathematical approaches, which are investigated to develop various new applications. Computational intelligence typically employs nature-inspired computational approaches to solve complex real-world issues. Examples include using neural network models to perform specific classification tasks and applying evolutionary computation for optimization. The aim of this topic is to highlight recent advanced methodologies and applications in image processing and computational intelligence.

Topics of interest include, but are not limited to, the following:

  1. Advanced machine learning methods and applications for image processing;
  2. Advanced deep learning methods and applications for image processing;
  3. Advanced pattern recognition methods and applications for image processing;
  4. Image super-resolution/denoising/enhancing/restoration;
  5. Advanced techniques in image processing;
  6. Advanced methods and applications in neural networks;
  7. Advanced methods and applications in evolutionary computation;
  8. Advanced techniques in computational intelligence;
  9. Multi-view/modal learning and fusion;
  10. Subspace learning and clustering.

Dr. Qi Zhang
Dr. Jianhang Zhou
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
  • image processing
  • pattern recognition
  • computational intelligence
  • deep learning

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Published Papers (5 papers)

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Research

21 pages, 1619 KiB  
Article
OWNC: Open-World Node Classification on Graphs with a Dual-Embedding Interaction Framework
by Yuli Chen and Chun Wang
Mathematics 2025, 13(9), 1475; https://doi.org/10.3390/math13091475 - 30 Apr 2025
Viewed by 55
Abstract
Traditional node classification is typically conducted in a closed-world setting, where all labels are known during training, enabling graph neural network methods to achieve high performance. However, in real-world scenarios, the constant emergence of new categories and updates to existing labels can result [...] Read more.
Traditional node classification is typically conducted in a closed-world setting, where all labels are known during training, enabling graph neural network methods to achieve high performance. However, in real-world scenarios, the constant emergence of new categories and updates to existing labels can result in some nodes no longer fitting into any known category, rendering closed-world classification methods inadequate. Thus, open-world classification becomes essential for graph data. Due to the inherent diversity of graph data in the open-world setting, it is common for the number of nodes with different labels to be imbalanced, yet current models are ineffective at handling such imbalance. Additionally, when there are too many or too few nodes from unseen classes, classification performance typically declines. Motivated by these observations, we propose a solution to address the challenges of open-world node classification and introduce a model named OWNC. This model incorporates a dual-embedding interaction training framework and a generator–discriminator architecture. The dual-embedding interaction training framework reduces label loss and enhances the distinction between known and unseen samples, while the generator–discriminator structure enhances the model’s ability to identify nodes from unseen classes. Experimental results on three benchmark datasets demonstrate the superior performance of our model compared to various baseline algorithms, while ablation studies validate the underlying mechanisms and robustness of our approach. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computational Intelligence: Methodologies and Applications)
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35 pages, 38034 KiB  
Article
MSTransBTS—A Novel Integration of Mamba with Swin Transformer for 3D Brain Tumour Segmentation
by Jia Qin Ngu, Humaira Nisar and Chi-Yi Tsai
Mathematics 2025, 13(7), 1117; https://doi.org/10.3390/math13071117 - 28 Mar 2025
Viewed by 313
Abstract
This study focuses on the major challenges in ensuring the timely assessment and accurate diagnosis of brain tumors (BTs), which are essential for effective patient treatment. Hence, in this paper, a time-efficient, automated, and advanced deep learning (DL) solution, the Mamba Swin Transformer [...] Read more.
This study focuses on the major challenges in ensuring the timely assessment and accurate diagnosis of brain tumors (BTs), which are essential for effective patient treatment. Hence, in this paper, a time-efficient, automated, and advanced deep learning (DL) solution, the Mamba Swin Transformer BT Segmentation (MSTransBTS) model, is introduced. This model employs the advanced Swin Transformer architecture, which is renowned for capturing long-range information and incorporates the latest Mamba approach for efficient long-range dependency modelling. Through meticulous customization and fine-tuning, the MSTransBTS achieves notable improvements in Dice scores, with scores of 89.53% for whole tumours (WTs), 80.09% for enhancing tumours (ETs), and 84.75% for tumour cores (TCs), resulting in an overall average Dice score of 84.79%. The employment of Test-Time Augmentation (TTA) further enhances performance and marks a significant advancement in BT segmentation accuracy. These findings not only address the critical need for timely assessment and diagnosis, but also emphasize the potential to enhance patient care through the automation of BT detection. By combining the features of Swin Transformer and Mamba techniques, this approach delivers a promising solution for accurate and efficient BT segmentation, which contributes to advancements in medical imaging. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computational Intelligence: Methodologies and Applications)
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16 pages, 920 KiB  
Article
Towards Robust Speech Models: Mitigating Backdoor Attacks via Audio Signal Enhancement and Fine-Pruning Techniques
by Heyan Sun, Qi Zhong, Minfeng Qi, Uno Fang, Guoyi Shi and Sanshuai Cui
Mathematics 2025, 13(6), 984; https://doi.org/10.3390/math13060984 - 17 Mar 2025
Viewed by 499
Abstract
The widespread adoption of deep neural networks (DNNs) in speech recognition has introduced significant security vulnerabilities, particularly from backdoor attacks. These attacks allow adversaries to manipulate system behavior through hidden triggers while maintaining normal operation on clean inputs. To address this challenge, we [...] Read more.
The widespread adoption of deep neural networks (DNNs) in speech recognition has introduced significant security vulnerabilities, particularly from backdoor attacks. These attacks allow adversaries to manipulate system behavior through hidden triggers while maintaining normal operation on clean inputs. To address this challenge, we propose a novel defense framework that combines speech enhancement with neural architecture optimization. Our approach consists of three key steps. First, we use a ComplexMTASS-based enhancement network to isolate and remove backdoor triggers by leveraging their unique spectral characteristics. Second, we apply an adaptive fine-pruning algorithm to selectively deactivate malicious neurons while preserving the model’s linguistic capabilities. Finally, we fine-tune the pruned model using clean data to restore and enhance recognition accuracy. Experiments on the AISHELL dataset demonstrate the effectiveness of our method against advanced steganographic attacks, such as PBSM and VSVC. The results show a significant reduction in attack success rate to below 1.5%, while maintaining 99.4% accuracy on clean inputs. This represents a notable improvement over existing defenses, particularly under varying trigger intensities and poisoning rates. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computational Intelligence: Methodologies and Applications)
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24 pages, 5163 KiB  
Article
Learning Spatial Density Functions of Random Waypoint Mobility over Irregular Triangles and Convex Quadrilaterals
by Yiming Feng, Wanxin Gao, Lefeng Zhang, Minfeng Qi, Qi Zhong and Ningran Li
Mathematics 2025, 13(6), 927; https://doi.org/10.3390/math13060927 - 11 Mar 2025
Viewed by 483
Abstract
For the optimization and performance evaluation of mobile ad hoc networks, a beneficial but challenging act is to derive from nodal movement behavior the steady-state spatial density function of nodal locations over a given finite area. Such derivation, however, is often intractable when [...] Read more.
For the optimization and performance evaluation of mobile ad hoc networks, a beneficial but challenging act is to derive from nodal movement behavior the steady-state spatial density function of nodal locations over a given finite area. Such derivation, however, is often intractable when any assumption of the mobility model is not basic, e.g., when the movement area is irregular in shape. As the first endeavor, we address this density derivation problem for the classic random waypoint mobility model over irregular convex polygons including triangles (i.e., 3-gons) and quadrilaterals (i.e., 4-gons). By mixing multiple Dirichlet distributions, we first devise a mixture density neural network tailored for density approximation over triangles and then extend this model to accommodate convex quadrilaterals. Experimental results show that our Dirichlet mixture model (DMM) can accurately capture the irregularity of ground-truth density distributions at low training cost, markedly outperforming the classic Gaussian mixture model (GMM). Full article
(This article belongs to the Special Issue Advanced Image Processing and Computational Intelligence: Methodologies and Applications)
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21 pages, 1911 KiB  
Article
Optimizing Water Use in Maize Irrigation with Reinforcement Learning
by Muhammad Alkaff, Abdullah Basuhail and Yuslena Sari
Mathematics 2025, 13(4), 595; https://doi.org/10.3390/math13040595 - 11 Feb 2025
Viewed by 808
Abstract
As global populations grow and environmental constraints intensify, improving agricultural water management is essential for sustainable food production. Traditional irrigation methods often lack adaptability, leading to inefficient water use. Reinforcement learning (RL) offers a promising solution for developing dynamic irrigation strategies that balance [...] Read more.
As global populations grow and environmental constraints intensify, improving agricultural water management is essential for sustainable food production. Traditional irrigation methods often lack adaptability, leading to inefficient water use. Reinforcement learning (RL) offers a promising solution for developing dynamic irrigation strategies that balance productivity and resource conservation. However, agricultural RL tasks are characterized by sparse actions—irrigation only when necessary—and delayed rewards realized at the end of the growing season. This study integrates RL with AquaCrop-OSPy simulations in the Gymnasium framework to develop adaptive irrigation policies for maize. We introduce a reward mechanism that penalizes incremental water usage while rewarding end-of-season yields, encouraging resource-efficient decisions. Using the Proximal Policy Optimization (PPO) algorithm, our RL-driven approach outperforms fixed-threshold irrigation strategies, reducing water use by 29% and increasing profitability by 9%. It achieves a water use efficiency of 76.76 kg/ha/mm, a 40% improvement over optimized soil moisture threshold methods. These findings highlight RL’s potential to address the challenges of sparse actions and delayed rewards in agricultural management, delivering significant environmental and economic benefits. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computational Intelligence: Methodologies and Applications)
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