New Advances and Challenges in Neural Networks and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1689

Special Issue Editors


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Guest Editor
Texas A&M University-San Antonio, San Antonio, TX 78224, USA
Interests: deep neural network; multimodal integration; representation learning; model calibration; neural network vulnerability; uncertainty estimation; computer vision; medical imaging; remote sensing

E-Mail Website
Guest Editor
College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
Interests: deep learning; graph neural networks; image segmentation/classification/detection; medical imaging; medical computing

Special Issue Information

Dear Colleagues,

Neural networks have become one of the most popular tools, with promising results in a variety of domains ranging from general computer vision to domain-specific tasks, from remote sensing to astrophysics and astronomy, and from natural language processing to multimodal integration systems. Without a doubt, neural networks are one of the most exciting research fields. However, as neural networks become more popular, new challenges emerge.

We are delighted to invite you to contribute an original research article focused on new advances and challenges in modern neural networks and their applications to this Special Issue entitled “New Advances and Challenges in Neural Networks and Applications”.

The broad topics include, but are not limited to, the following:

  • Existing and potential challenges of neural networks, such as vulnerability, robustness, calibration, interpretability, etc.
  • Emerging issues of neural networks, such as potential threats to cyber security and their defense (e.g., deep fakes and large-scale information generation).
  • Novel neural network algorithms, applications, usage cases, and datasets in various domains, such as medical imaging, autonomous driving, remote sensing, smart assistants, language–vision integration, agriculture, and pest control.

Dr. Gongbo Liang
Prof. Dr. LiangLiang Liu
Guest Editors

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Keywords

  • deep learning
  • computer vision
  • natural language processing
  • medical imaging, remote sensing, agriculture, and domain-specific applications
  • model calibration, vulnerability, robustness, interpretability, etc.
  • classification, segmentation, and detection
  • language-vision integration
  • cyber security

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Published Papers (1 paper)

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Research

21 pages, 784 KiB  
Article
Robust Stability Analysis of Switched Neural Networks with Application in Psychological Counseling Evaluation System
by Yajuan Li and Huanbin Xue
Mathematics 2024, 12(13), 2097; https://doi.org/10.3390/math12132097 - 3 Jul 2024
Viewed by 1050
Abstract
In this work, the effectiveness and stability of psychological counseling are evaluated using the switched complex-valued neural networks (SCVNN) model, which includes parameter disturbances, impulsive perturbations, variable and continuously distributed delays in the system state, and impulsive delay. How to analyze and judge [...] Read more.
In this work, the effectiveness and stability of psychological counseling are evaluated using the switched complex-valued neural networks (SCVNN) model, which includes parameter disturbances, impulsive perturbations, variable and continuously distributed delays in the system state, and impulsive delay. How to analyze and judge the stability of the network simply and effectively is the primary prerequisite for its successful application. Therefore, we explore the dynamic behavior of SCVNN with both variable and distributed delays along with impulsive effect. Initially, the proposed conditions for the existence and uniqueness of equilibrium in SCVNN are presented. Subsequently, employing the inequality technique and impulsive average dwell time approach, sufficient conditions for the robust exponential stability of SCVNN under both arbitrary and restricted switching are obtained. Lastly, the psychological counseling evaluation system (PCES) is established, and a simulation example is used to verify the correctness and effectiveness of the presented findings. Full article
(This article belongs to the Special Issue New Advances and Challenges in Neural Networks and Applications)
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