Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
2.3
Journals
Mathematics
Special Issues
Intelligent Mathematics and Applications
share announcement

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: 28 February 2027 | Viewed by 4231

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-anonymized 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

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

30 pages, 27596 KB  
Article
A Multibody Dynamic Modeling and GAN–CNN Fusion Framework for Small-Sample Fault Diagnosis of Open-Pit Coal Mine Reducers
by Guanghe Zhu and Haijun Zhang
Mathematics 2026, 14(11), 2008; https://doi.org/10.3390/math14112008 - 4 Jun 2026
Viewed by 360
Abstract
To address fault diagnosis under limited sample conditions, this paper proposes a small-sample diagnosis framework integrating multibody dynamic modeling and a GAN–CNN fusion strategy. First, a rigid–flexible coupled multibody dynamic model of the reducer is established to simulate vibration responses under typical fault [...] Read more.
To address fault diagnosis under limited sample conditions, this paper proposes a small-sample diagnosis framework integrating multibody dynamic modeling and a GAN–CNN fusion strategy. First, a rigid–flexible coupled multibody dynamic model of the reducer is established to simulate vibration responses under typical fault modes, including broken gear tooth, gear wear, and bearing outer ring fault, thereby generating representative simulation samples. Second, to reduce the distribution discrepancy between simulated and measured data, the simulated samples are introduced into a generative adversarial learning framework for feature enhancement, with limited measured samples used as references. Cosine similarity is employed to evaluate the consistency between the enhanced simulated data and the measured data in the feature space. Finally, the enhanced simulated samples are fused with measured samples to construct a hybrid dataset for convolutional neural network training and fault classification. Experimental results show that the proposed framework improves the similarity between simulated and measured data, with cosine similarity increasing from below 0.65 to above 0.80. Under small-sample conditions, the mean diagnosis accuracy reaches 83.81%, which is 17.33 percentage points higher than that obtained using the original small-sample dataset. The proposed framework provides an effective modeling and algorithmic approach for reducer fault diagnosis under data-scarce conditions. Full article
(This article belongs to the Special Issue Intelligent Mathematics and Applications)
►▼ Show Figures

Figure 1

25 pages, 3702 KB  
Article
MELT: Optimization-Driven Music Emotion Learning with Temporal Token-Level Fusion
by Yihe Yin, Zhen Tian and Junming Chen
Mathematics 2026, 14(10), 1690; https://doi.org/10.3390/math14101690 - 15 May 2026
Viewed by 387
Abstract
Music emotion recognition (MER) can be formulated as a multimodal optimization problem that predicts an emotion label from coupled audio and lyric sequences. Existing methods typically perform unimodal learning or coarse global fusion, which overlooks fine-grained temporal-token correspondences between musical dynamics and lyric [...] Read more.
Music emotion recognition (MER) can be formulated as a multimodal optimization problem that predicts an emotion label from coupled audio and lyric sequences. Existing methods typically perform unimodal learning or coarse global fusion, which overlooks fine-grained temporal-token correspondences between musical dynamics and lyric semantics. We propose MELT (Music Emotion Learning with Temporal token-level fusion), an optimization-driven framework with four modules: a BERT-based lyrics semantic encoder (LSE), a segment temporal encoder (STE) that models audio-segment dependencies via a Transformer, a token-level temporal fusion (TTF) module with gated cross-attention, and an emotion mood head (EMH) for four-class prediction. Training is conducted end-to-end by jointly minimizing a supervised classification term and an auxiliary cross-modal contrastive alignment term, yielding a unified objective that improves both class separability and representation consistency. On the MoodyLyrics benchmark, MELT achieves 87.6% weighted F1 for four-class emotion recognition (angry, happy, relaxed, sad), outperforming unimodal baselines and representative early/late fusion strategies. Ablation results further verify that temporal encoding, gated token-level fusion, and joint optimization each contribute to the final performance. Full article
(This article belongs to the Special Issue Intelligent Mathematics and Applications)
►▼ Show Figures

Figure 1

27 pages, 4167 KB  
Article
OptiNeRF: A Spatially Optimized Neural Rendering Framework for Complex Scene Reconstruction
by Xinyuan Gu, Yanbo Chang, Junyue Xia, Yue Yu, Zhen Tian and Junming Chen
Mathematics 2026, 14(5), 842; https://doi.org/10.3390/math14050842 - 1 Mar 2026
Viewed by 666
Abstract
Neural rendering techniques aim to generate photorealistic images and accurate 3D geometries from multi-view images but often struggle with efficiency and geometric consistency in complex or dynamic scenes. Optimized Neural Radiance Fields (OptiNeRF) addresses these challenges through several innovations. It uses spatially optimized [...] Read more.
Neural rendering techniques aim to generate photorealistic images and accurate 3D geometries from multi-view images but often struggle with efficiency and geometric consistency in complex or dynamic scenes. Optimized Neural Radiance Fields (OptiNeRF) addresses these challenges through several innovations. It uses spatially optimized sampling to focus on points near object surfaces, reducing computation while improving precision. Leveraging the pre-trained Marigold model, it generates depth and normal maps as geometric priors. Sampled points are processed through a hybrid network combining an MLP and a multi-resolution feature grid (MRF), capturing fine details and large-scale structures. To handle varying illumination and complex materials, OptiNeRF introduces adaptive volume rendering (AVR), dynamically adjusting light transparency and scattering. A progressive sampling strategy further focuses computation on regions with high geometric complexity. The loss function incorporates RGB, normal, depth, boundary, and lighting optimization losses, with adaptive weight modulation for geometric priors, ensuring both visual fidelity and geometric consistency even with inaccurate depth/normal estimates. Experiments on dynamic scenes show strong performance, with a PSNR of 32.10 dB, SSIM of 0.936, Chamfer distance of 1.28 × 10−3, training time of 12 h, and rendering speed of 25 FPS, demonstrating high geometric accuracy, realistic rendering, and computational efficiency over conventional methods. Full article
(This article belongs to the Special Issue Intelligent Mathematics and Applications)
►▼ Show Figures

Figure 1

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 3 | Viewed by 820
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)
►▼ Show Figures

Figure 1

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 1196
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop