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Mathematics
Special Issues
Intelligent Mathematics and Applications
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Intelligent Mathematics and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1657

Special Issue Editors

Prof. Dr. Guorong Chen

E-Mail Website
Guest Editor
School of Computer Science and Engineering (School of Artificial Intelligence), Chongqing University of Science and Technology, Chongqing 401331, China
Interests: intelligent mathematics; image and video processing; image anti-counterfeiting; safety production and informationization
Dr. Xiao Huang

E-Mail Website
Guest Editor
Department of Computing, The Hong Kong Polytechnic University, Hong Kong
Interests: graph representation learning; knowledge graphs; large language models; retrieval-augmented generation; recommender systems; text-to-SQL
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yingxu Wang

E-Mail Website
Guest Editor
Research Institute of Intelligent Mathematics and Autonomous AI (RI-IM.AI∗), CQUST, Chongqing 401331, China
Interests: the initiator of intelligent mathematics; autonomous AI; intelligent science; software science; cognitive robots; cognitive computing; autonomous system; knowledge science

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to promoting the advancement of intelligent mathematical methods in artificial intelligence technology and their industry applications. In the past decade, new technologies such as artificial intelligence (AI), big data, and the Internet-of-Things have developed rapidly, promoting the intelligent work of safety production and generating positive benefits. However, this development is constrained by the mathematical foundations of various information systems, methods, and models, particularly in intelligent mathematics and cognitive computing problems. The theme of this special issue covers various algorithms, methods, and applications in promoting safety production informatization from the perspective of intelligent mathematics. The topics of interest include, but are not limited to, the following:

Intelligent mathematics and cognitive computing;

Safety production and informationization;

Internet-of-Things engineering;

Industrial internet security;

Big model of emergency management;

Artificial intelligence technology and applications;

Big data processing technology for safety production.

Prof. Dr. Guorong Chen
Dr. Xiao Huang
Prof. Dr. Yingxu Wang
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 250 words) can be sent to the Editorial Office for assessment.

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

  • safe production and informationization
  • informatization and computing science
  • IoT and network computing
  • artificial intelligence and electrical automation

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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

22 pages, 2204 KB  
Article
A Lightweight YOLOv8-Based Network for Efficient Corn Disease Detection
by Deao Song, Yiran Peng, Xinyuan Gu and KinTak U
Mathematics 2025, 13(24), 4002; https://doi.org/10.3390/math13244002 - 16 Dec 2025
Cited by 1 | Viewed by 402
Abstract
To address the pressing need for accurate and efficient detection of corn diseases, we propose a novel, lightweight object detection framework, CBS-YOLOv8 (C2f-BiFPN-SCConv YOLOv8), which builds upon the YOLOv8 architecture to enhance performance for corn disease detection. The model incorporates two key components, [...] Read more.
To address the pressing need for accurate and efficient detection of corn diseases, we propose a novel, lightweight object detection framework, CBS-YOLOv8 (C2f-BiFPN-SCConv YOLOv8), which builds upon the YOLOv8 architecture to enhance performance for corn disease detection. The model incorporates two key components, the GhostNetV2 block and SCConv (Selective Convolution). The GhostNetV2 block improves feature representation by reducing computational complexity, while SCConv optimizes convolution operations dynamically, adjusting based on the input to ensure minimal computational overhead. Together, these features maintain high detection accuracy while keeping the network lightweight. Additionally, the model integrates the C2f-GhostNetV2 module to eliminate redundancy, and the SimAM attention mechanism improves lesion-background separation, enabling more accurate disease detection. The Bi-directional Feature Pyramid Network (BiFPN) enhances feature representation across multiple scales, strengthening detection across varying object sizes. Evaluated on a custom dataset of over 6000 corn leaf images across six categories, CBS-YOLOv8 achieves improved accuracy and reliability in object detection. With a lightweight architecture of just 8.1M parameters and 21 GFLOPs, it enables real-time deployment on edge devices in agricultural settings. CBS-YOLOv8 offers high detection performance while maintaining computational efficiency, making it ideal for precision agriculture. Full article
(This article belongs to the Special Issue Intelligent Mathematics and Applications)
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17 pages, 10635 KB  
Article
Hybrid Convolutional Transformer with Dynamic Prompting for Adaptive Image Restoration
by Jinmei Zhang, Guorong Chen, Junliang Yang, Qingru Zhang, Shaofeng Liu and Weijie Zhang
Mathematics 2025, 13(20), 3329; https://doi.org/10.3390/math13203329 - 19 Oct 2025
Viewed by 764
Abstract
High-quality image restoration (IR) is a fundamental task in computer vision, aiming to recover a clear image from its degraded version. Prevailing methods typically employ a static inference pipeline, neglecting the spatial variability of image content and degradation, which makes it difficult for [...] Read more.
High-quality image restoration (IR) is a fundamental task in computer vision, aiming to recover a clear image from its degraded version. Prevailing methods typically employ a static inference pipeline, neglecting the spatial variability of image content and degradation, which makes it difficult for them to adaptively handle complex and diverse restoration scenarios. To address this issue, we propose a novel adaptive image restoration framework named Hybrid Convolutional Transformer with Dynamic Prompting (HCTDP). Our approach introduces two key architectural innovations: a Spatially Aware Dynamic Prompt Head Attention (SADPHA) module, which performs fine-grained local restoration by generating spatially variant prompts through real-time analysis of image content and a Gated Skip-Connection (GSC) module that refines multi-scale feature flow using efficient channel attention. To guide the network in generating more visually plausible results, the framework is optimized with a hybrid objective function that combines a pixel-wise L1 loss and a feature-level perceptual loss. Extensive experiments on multiple public benchmarks, including image deraining, dehazing, and denoising, demonstrate that our proposed HCTDP exhibits superior performance in both quantitative and qualitative evaluations, validating the effectiveness of the adaptive restoration framework while utilizing fewer parameters than key competitors. Full article
(This article belongs to the Special Issue Intelligent Mathematics and Applications)
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