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Big Data Analysis and Management Based on Deep Learning: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 August 2025 | Viewed by 1972

Special Issue Editors

Prof. Dr. Min Xia

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Information Science and Technology, 219 Ningliu Rd., Nanjing 210044, China
Interests: deep learning; remote sensing image analysis; change detection; semantic analysis; image segmentation
Special Issues, Collections and Topics in MDPI journals
Dr. Kai Hu

E-Mail Website
Guest Editor
Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: machine learning; image analysis; intelligent robot
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yoonsik Choe

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
Interests: statistical pattern recognition; dimensionality reduction in deep learning; sparse signal representation; big data analysis; statistical image signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of information society, the data scale is becoming larger and larger, and heterogeneous information is significantly expanding, including a series of cross media content, including video, image, remote sensing, audio, text, and other data. At present, the emergence of increasingly complex big data brings more challenges to the current big data analysis technology. Because of its multilayer nonlinear structure, the deep learning model has a strong feature learning ability, which provides an effective way to solve the above problems. For data-driven representation learning, such as speech recognition, target detection, image classification, and machine translation, deep learning shows unique advantages.

Therefore, this Special Issue aims to collate original research and review articles that emphasize the important role of deep learning for big data analysis. It calls for state-of-the-art research in the theory, algorithm, modeling, system, and application of deep learning-based big data analysis and aims to demonstrate the latest efforts of relevant researchers.

Prof. Dr. Min Xia
Dr. Kai Hu
Prof. Dr. Yoonsik Choe
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. Applied Sciences 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 2400 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 methods for analysis of big data
  • deep learning methods for the analysis of multisource time-series data
  • deep learning methods for semantic information extraction from complex image and video data
  • model acceleration for deep learning of big data
  • theory and novel application scenarios of cross media big data analysis
  • investigation of the latest progress in this field

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Related Special Issue

Published Papers (3 papers)

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Research

17 pages, 42731 KiB  
Article
ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network
by Yiming Chen, Hui Zhang, Chen Zhang and Yi Liu
Appl. Sci. 2025, 15(7), 3980; https://doi.org/10.3390/app15073980 - 4 Apr 2025
Viewed by 456
Abstract
In response to the issue that post-training quantization leads to performance degradation in mobile deployment, as well as the problem that the balanced consideration of quantization deviation by Clipping optimization techniques limits the improvement of quantization accuracy, this article proposes a novel clipping [...] Read more.
In response to the issue that post-training quantization leads to performance degradation in mobile deployment, as well as the problem that the balanced consideration of quantization deviation by Clipping optimization techniques limits the improvement of quantization accuracy, this article proposes a novel clipping optimization method named ClipQ, which pays different attention to the parameters, aiming to preferentially reduce the quantization deviation of important parameters. The attention of the weight is positively related to its absolute value. Channel information entropy and principal component analysis are used to characterize the channel attention and spatial attention of activations, respectively. In addition, the particle swarm algorithm is applied in weight clipping to adjust the search step size and direction adaptively. ClipQ achieves high-precision quantization with very few calibration samples (<=50) and low time cost. Meanwhile, it does not bring extra computation, which is friendly to hardware. The experimental evaluation on image classification, semantic segmentation, and object detection shows that ClipQ outperforms other state-of-the-art clipping techniques, such as KL, ACIQ, and MSE. In 8-bit quantization, the average precision loss is 0.31% for image classification and 0.22% for object detection. More notably, it achieves almost lossless accuracy in semantic segmentation tasks. Full article
(This article belongs to the Special Issue Big Data Analysis and Management Based on Deep Learning: 2nd Edition)
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18 pages, 802 KiB  
Article
Transformer Self-Attention Change Detection Network with Frozen Parameters
by Peiyang Cheng, Min Xia, Dehao Wang, Haifeng Lin and Zikai Zhao
Appl. Sci. 2025, 15(6), 3349; https://doi.org/10.3390/app15063349 - 19 Mar 2025
Cited by 2 | Viewed by 364
Abstract
The purpose of change detection is to recognize changed areas from a pair of two remote sensing images. However, since change areas often include multiple terrain features, this demands enhanced feature extraction capability from the model. This paper proposes a frozen-parameter Transformer self-attention [...] Read more.
The purpose of change detection is to recognize changed areas from a pair of two remote sensing images. However, since change areas often include multiple terrain features, this demands enhanced feature extraction capability from the model. This paper proposes a frozen-parameter Transformer self-attention change detection network (ZAQNet). The network integrates four innovative modules: a GIAU (Generalized Image Attention Unit) which can effectively fuse the features of two remote sensing images and accurately focus on changing areas; a GSAU (Global Spatial Attention Unit) which performs self attention processing in the image spatial dimension to enhance the model’s ability to capture global change information; a GSCU (Global Semantic Context Unit) which performs self-attention operations in the channel dimension to enhance the model’s attention to feature maps containing changing information; and a PRU (Patch Refinement Unit) which extracts and refines spatial position information from the underlying feature map, optimizing the restoration effect of the feature map. The experiments on the BTRS-CD and LEVIR-CD datasets show that ZAQNet performs excellently in change detection tasks. Among them, the change detection index F1 and IOU are better than the comparison model. These results fully demonstrate the superiority, robustness, and generalization ability of ZAQNet in change detection tasks and provide an efficient and reliable solution for remote sensing image analysis. Full article
(This article belongs to the Special Issue Big Data Analysis and Management Based on Deep Learning: 2nd Edition)
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23 pages, 673 KiB  
Article
Generative Adversarial Network Based on Self-Attention Mechanism for Automatic Page Layout Generation
by Peng Sun, Xiaomei Liu, Liguo Weng and Ziheng Liu
Appl. Sci. 2025, 15(5), 2852; https://doi.org/10.3390/app15052852 - 6 Mar 2025
Viewed by 811
Abstract
Automatic page layout generation is a challenging and promising research task, which improves the design efficiency and quality of various documents, web pages, etc. However, the current generation of layouts that are both reasonable and aesthetically pleasing still faces many difficulties, such as [...] Read more.
Automatic page layout generation is a challenging and promising research task, which improves the design efficiency and quality of various documents, web pages, etc. However, the current generation of layouts that are both reasonable and aesthetically pleasing still faces many difficulties, such as the shortcomings of existing methods in terms of structural rationality, element alignment, text and image relationship processing, and insufficient consideration of element details and mutual influence within the page. To address these issues, this article proposes a Transformer-based Generative Adversarial Network (TGAN). Generative Adversarial Networks (GANs) innovatively introduce the self-attention mechanism into the network, enabling the model to focus more on key local information that affects page layout. By introducing conditional variables in the generator and discriminator, more accurate sample generation and discrimination can be achieved. The experimental results show that the TGAN outperforms other methods in both subjective and objective ratings when generating page layouts. The generated layouts perform better in element alignment, avoiding overlap, and exhibit higher layout quality and stability, providing a more effective solution for automatic page layout generation. Full article
(This article belongs to the Special Issue Big Data Analysis and Management Based on Deep Learning: 2nd Edition)
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