Signal and Image Processing Applications in Artificial Intelligence, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2756

Special Issue Editors


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Faculty of Exact Sciences and Engineering, University of Madeira, 9020-105 Funchal, Portugal
Interests: signal processing; sleep analysis; machine learning; biomedical analysis
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Guest Editor
Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Interests: CNN; deep learning; sleep apnea; sensors for sleep apnea; RNN; deep neural network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the general application of artificial intelligence using signal and image processing, covering a broad range of research topics in which data-driven decision-making is employed. In addition, it aims to evaluate innovative research in which artificial intelligence provides a novel perspective.

The relevance of these topics has increased significantly in recent years, as machine-learning-based components become the backbone of modern systems and the Internet of Things (IoT) confers intelligence to our daily activities. While these intelligent devices provide outstanding data collection capabilities, there is a need to develop sophisticated data-driven algorithms that are capable of processing the information generated. Likewise, this Special Issue would like to address the impact of machine learning and novel methods based on deep learning, and study how these new techniques can advance the field.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Data-driven algorithms based on IoT-system-generated data;
  • Applications of transfer learning for image processing;
  • Object detection using machine learning;
  • Feature selection techniques;
  • IoT-based applications with data analysis;
  • Real-world applications of machine learning.

Dr. Fabio Mendonca
Dr. Morgado Dias
Dr. Sheikh Shanawaz Mostafa
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • image classification
  • big data
  • signal processing
  • explainable machine learning
  • Internet of Things
  • model-agnostic techniques
  • imaging analytics
  • machine learning and deep learning
  • feature selection

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Related Special Issue

Published Papers (2 papers)

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Research

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22 pages, 5125 KB  
Article
A Steganographic Message Transmission Method Based on Style Transfer and Denoising Diffusion Probabilistic Model
by Yen-Hui Lin, Chin-Pan Huang and Ping-Sheng Huang
Electronics 2025, 14(16), 3258; https://doi.org/10.3390/electronics14163258 - 16 Aug 2025
Viewed by 513
Abstract
This study presents a new steganography method for message transmission based on style transfer and denoising diffusion probabilistic model (DDPM) techniques. Different types of object images are used to represent the messages and are arranged in order from left to right and top [...] Read more.
This study presents a new steganography method for message transmission based on style transfer and denoising diffusion probabilistic model (DDPM) techniques. Different types of object images are used to represent the messages and are arranged in order from left to right and top to bottom to generate a secret image. Then, the style transfer technique is employed to embed the secret image (content image) into the cover image (style image) to create a stego image. To reveal the messages, the DDPM technique is first used to inpaint the secret image from the stego image. Then, the YOLO (You Only Look Once) technique is utilized to detect objects in the secret image for the message decoding. Two security mechanisms are included: one uses object images for the message encoding, and the other hides them in a customizable public image. To obtain the messages, both mechanisms need to be cracked at the same time. Therefore, this method provides highly secure information protection. Experimental results show that our method has good confidential information transmission performance. Full article
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Review

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34 pages, 9273 KB  
Review
Multi-Task Deep Learning for Lung Nodule Detection and Segmentation in CT Scans: A Review
by Runhan Li and Barmak Honarvar Shakibaei Asli
Electronics 2025, 14(15), 3009; https://doi.org/10.3390/electronics14153009 - 28 Jul 2025
Viewed by 1805
Abstract
Lung nodule detection and segmentation are essential tasks in computer-aided diagnosis (CAD) systems for early lung cancer screening. With the growing availability of CT data and deep learning models, researchers have explored various strategies to improve the performance of these tasks. This review [...] Read more.
Lung nodule detection and segmentation are essential tasks in computer-aided diagnosis (CAD) systems for early lung cancer screening. With the growing availability of CT data and deep learning models, researchers have explored various strategies to improve the performance of these tasks. This review focuses on Multi-Task Learning (MTL) approaches, which unify or cooperatively integrate detection and segmentation by leveraging shared representations. We first provide an overview of traditional and deep learning methods for each task individually, then examine how MTL has been adapted for medical image analysis, with a particular focus on lung CT studies. Key aspects such as network architectures and evaluation metrics are also discussed. The review highlights recent trends, identifies current challenges, and outlines promising directions toward more accurate, efficient, and clinically applicable CAD solutions. The review demonstrates that MTL frameworks significantly enhance efficiency and accuracy in lung nodule analysis by leveraging shared representations, while also identifying critical challenges such as task imbalance and computational demands that warrant further research for clinical adoption. Full article
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