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Theoretical Foundations and Applications of Deep Learning Techniques
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Instituto de Diseño y Fabricación, Universitat Politècnica de València, 46022 Valencia, Spain
Prof. Dr. Adolfo Muñoz García
Instituto de Diseño y Fabricación, Universitat Politècnica de València, 46022 València, Spain
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Theoretical Foundations and Applications of Deep Learning Techniques

Abstract submission deadline
31 December 2025
Manuscript submission deadline
31 March 2026
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4379

Topic Information

Dear Colleagues, 

Deep learning has revolutionized artificial intelligence, enabling breakthroughs in tasks such as image recognition, natural language processing, and autonomous systems. By leveraging complex neural network architectures, deep learning uncovers patterns and representations in data that traditional methods often fail to capture.

This Topic, “Theoretical Foundations and Applications of Deep Learning Techniques”, explores both the theoretical foundations and practical applications of deep learning techniques. From a theoretical perspective, we welcome studies on mathematical frameworks, optimization strategies, novel architectures, and interpretability. These contributions aim to advance our understanding of the mechanisms driving the success of deep learning.

On the application side, we seek innovative research showcasing the use of deep learning across diverse fields, including healthcare, robotics, finance, and climate science. Submissions addressing scalability, computational efficiency, and generalization challenges are particularly encouraged.

We invite original research papers and reviews, providing a platform to advance both the science and real-world impact of deep learning. We look forward to receiving your contributions and fostering a rich dialog in this dynamic field.

Prof. Dr. Juan Ernesto Solanes Galbis
Prof. Dr. Adolfo Muñoz García
Topic Editors

Keywords

  • deep generative models
  • transfer learning
  • natural language processing (NLP)
  • recurrent neural networks (RNNs)
  • convolutional neural networks (CNNs)
  • adversarial learning
  • data augmentation
  • supervised learning
  • unsupervised learning
  • deep learning
  • semi-supervised learning
  • neural networks
  • explainable AI (XAI)
  • reinforcement learning

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai 		5.0 6.9 2020 20.7 Days CHF 1600 Submit
Algorithms
algorithms 		2.1 4.5 2008 17.8 Days CHF 1800 Submit
Applied Sciences
applsci 		2.5 5.5 2011 19.8 Days CHF 2400 Submit
BioMedInformatics
biomedinformatics 		- 3.4 2021 22.9 Days CHF 1000 Submit
Computers
computers 		4.2 7.5 2012 16.3 Days CHF 1800 Submit
Electronics
electronics 		2.6 6.1 2012 16.8 Days CHF 2400 Submit
Big Data and Cognitive Computing
BDCC 		4.4 9.8 2017 24.5 Days CHF 1800 Submit

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Published Papers (2 papers)

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25 pages, 3632 KiB  
Article
Air Battlefield Time Series Data Augmentation Model Based on a Lightweight Denoising Diffusion Probabilistic Model
by Bo Cao, Qinghua Xing, Longyue Li, Junjie Shi and Weijie Lin
AI 2025, 6(8), 192; https://doi.org/10.3390/ai6080192 - 18 Aug 2025
Abstract
The uncertainty and confrontational nature of war itself pose significant challenges to the collection and storage of aerial battlefield temporal data. To address the issue of insufficient training of intelligent models caused by the scarcity of air battlefield situation data, this paper designs [...] Read more.
The uncertainty and confrontational nature of war itself pose significant challenges to the collection and storage of aerial battlefield temporal data. To address the issue of insufficient training of intelligent models caused by the scarcity of air battlefield situation data, this paper designs an air battlefield time series data augmentation model based on a lightweight denoising diffusion probabilistic model (LDMKD-DA). Considering the advantages of a denoising diffusion probabilistic model (DDPM) in processing images, this paper transforms 1D time series data into image data. 1D univariate time series data, such as High-resolution Range Profile dataset, are transformed by Gramian angular fields and Markov transition fields. Multivariate time series data, such as the air target intention dataset, are transformed by matrix expansion. Then, the data augmentation model is constructed based on the denoising diffusion probabilistic model. Considering the need for miniaturization and intelligence in future combat platforms, the depthwise separable convolution is introduced to lighten the DDPM, and, at the same time, the improved knowledge distillation method is introduced to accelerate the sampling process. The experimental results show that LDMKD-DA is capable of generating synthetic data similar to real data with high quality while significantly reducing FLOPs and params, while having significant advantages in univariate and multivariate time series data amplification. Full article
(This article belongs to the Topic Theoretical Foundations and Applications of Deep Learning Techniques)
27 pages, 584 KiB  
Review
Survey of Architectural Floor Plan Retrieval Technology Based on 3ST Features
by Hongxing Ling, Guangsheng Luo, Nanrun Zhou and Xiaoyan Jiang
AI 2025, 6(4), 67; https://doi.org/10.3390/ai6040067 - 26 Mar 2025
Viewed by 3583
Abstract
Feature retrieval technology for building floor plans has garnered significant attention in recent years due to its critical role in the efficient management and execution of construction projects. This paper presents a comprehensive exploration of four primary features essential for the retrieval of [...] Read more.
Feature retrieval technology for building floor plans has garnered significant attention in recent years due to its critical role in the efficient management and execution of construction projects. This paper presents a comprehensive exploration of four primary features essential for the retrieval of building floor plans: semantic features, spatial features, shape features, and texture features (collectively referred to as 3ST features). The extraction algorithms and underlying principles associated with these features are thoroughly analyzed, with a focus on advanced methods such as wavelet transforms and Fourier shape descriptors. Furthermore, the performance of various retrieval algorithms is evaluated through rigorous experimental analysis, offering valuable insights into optimizing the retrieval of building floor plans. Finally, this study outlines prospective directions for the advancement of feature retrieval technology in the context of floor plans. Full article
(This article belongs to the Topic Theoretical Foundations and Applications of Deep Learning Techniques)
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