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Mathematics
Special Issues
Applications of Deep Learning and Convolutional Neural Network
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Applications of Deep Learning and Convolutional Neural Network

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 December 2025 | Viewed by 2345

Special Issue Editor

Dr. NIwat Angkawisittpan

E-Mail Website
Guest Editor
Research Unit for Electrical and Computer Engineering Technology (RECENT), Faculty of Engineering, Mahasarakham University, Kham Riang 44150, Thailand
Interests: electrical engineering

Special Issue Information

Dear Colleagues,

The Special Issue, titled "Applications of Deep Learning and Convolutional Neural Networks", explores the transformative impacts of these technologies across diverse fields, showcasing cutting-edge research and innovative applications. Deep learning and CNNs have revolutionized the way we approach complex data problems, offering powerful tools for pattern recognition, image and speech processing, natural language understanding, etc.

In this Special Issue, we feature articles that highlight the versatility and adaptability of deep learning models. We delve into the use of CNNs for medical image analysis, where they significantly enhance diagnostic accuracy and enable early detection of diseases. In the realm of autonomous vehicles, deep learning algorithms facilitate real-time object detection and decision-making, paving the way for safer transportation systems.

Moreover, this Special Issue explores the role of deep learning in financial technology, where predictive models are employed for risk assessment and fraud detection, optimizing financial operations. Another key focus is the application of CNNs in environmental monitoring, where satellite imagery is analyzed to track climate change and natural resource management.

Overall, this Special Issue will provide a comprehensive overview of how deep learning and CNNs are leveraged to address pressing global challenges, highlighting both achievements and ongoing challenges in this rapidly evolving field.

Dr. NIwat Angkawisittpan
Guest Editor

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

  • deep learning
  • convolutional neural networks (CNNs)
  • pattern recognition
  • medical image analysis
  • diagnostic accuracy
  • autonomous vehicles
  • real-time object detection
  • decision-making financial technology (FinTech)
  • risk assessment
  • fraud detection
  • environmental monitoring
  • satellite imagery
  • climate change tracking
  • natural resource management
  • artificial intelligence (AI)
  • image and speech processing
  • natural language understanding
  • predictive models
  • global challenges in AI

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

Published Papers (1 paper)

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35 pages, 10185 KB  
Article
Int.2D-3D-CNN: Integrated 2D and 3D Convolutional Neural Networks for Video Violence Recognition
by Wimolsree Getsopon, Sirawan Phiphitphatphaisit, Emmanuel Okafor and Olarik Surinta
Mathematics 2025, 13(16), 2665; https://doi.org/10.3390/math13162665 - 19 Aug 2025
Viewed by 307
Abstract
Intelligent video analysis tools have advanced significantly, with numerous cameras installed in various locations to enhance security and monitor unusual events. However, the effective detection and monitoring of violent incidents often depend on manual effort and time-consuming analysis of recorded footage, which can [...] Read more.
Intelligent video analysis tools have advanced significantly, with numerous cameras installed in various locations to enhance security and monitor unusual events. However, the effective detection and monitoring of violent incidents often depend on manual effort and time-consuming analysis of recorded footage, which can delay timely interventions. Deep learning has emerged as a powerful approach for extracting critical features essential to identifying and classifying violent behavior, enabling the development of accurate and scalable models across diverse domains. This study presents the Int.2D-3D-CNN architecture, which integrates a two-dimensional convolutional neural network (2D-CNN) and 3D-CNNs for video-based violence recognition. Compared to traditional 2D-CNN and 3D-CNN models, the proposed Int.2D-3D-CNN model presents improved performance on the Hockey Fight, Movie, and Violent Flows datasets. The architecture captures both static and dynamic characteristics of violent scenes by integrating spatial and temporal information. Specifically, the 2D-CNN component employs lightweight MobileNetV1 and MobileNetV2 to extract spatial features from individual frames, while a simplified 3D-CNN module with a single 3D convolution layer captures motion and temporal dependencies across sequences. Evaluation results highlight the robustness of the proposed model in accurately distinguishing violent from non-violent videos under diverse conditions. The Int.2D-3D-CNN model achieved accuracies of 98%, 100%, and 98% on the Hockey Fight, Movie, and Violent Flows datasets, respectively, indicating strong potential for violence recognition applications. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Convolutional Neural Network)
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