Topic Editors

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

Theoretical Foundations and Applications of Deep Learning Techniques

Abstract submission deadline
31 December 2025
Manuscript submission deadline
31 March 2026
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854

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
3.1 7.2 2020 18.9 Days CHF 1600 Submit
Algorithms
algorithms
1.8 4.1 2008 18.9 Days CHF 1600 Submit
Applied Sciences
applsci
2.5 5.3 2011 18.4 Days CHF 2400 Submit
BioMedInformatics
biomedinformatics
- 1.7 2021 22 Days CHF 1000 Submit
Computers
computers
2.6 5.4 2012 15.5 Days CHF 1800 Submit
Electronics
electronics
2.6 5.3 2012 16.4 Days CHF 2400 Submit
Big Data and Cognitive Computing
BDCC
3.7 7.1 2017 25.3 Days CHF 1800 Submit

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Published Papers (1 paper)

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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 589
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
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