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Electronics
Special Issues
Machine Learning, Image Analysis and IoT Applications in Industry
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Machine Learning, Image Analysis and IoT Applications in Industry

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

Deadline for manuscript submissions: 15 May 2024 | Viewed by 1289

Special Issue Editors

Dr. Miguel García-Torres

E-Mail Website
Guest Editor
Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Interests: machine learning; bioinformatics; astrostatistics; big data
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francisco A. Gómez Vela

E-Mail Website
Guest Editor
School of Engineerings, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: computer vision; machine learning; image and signal processing; 5G communications; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technological advances in communications and storage devices have allowed the industry to generate vast amounts of data. Therefore, the use of artificial intelligence (AI) and the Internet of Things (IoT) technologies in industry has enabled for innovative solutions and industry to become a market leader. However, despite the recent advances made, industry still faces existing and new challenges that must be addressed with new technological solutions. In order to cope successfully with the needs of the industry, it is necessary to investigate multidisciplinary approaches. This Special Issue invites high-quality research papers to address challenges in industry.

This Special Issue “Machine Learning, Image Analysis and IoT Applications in Industry”, invites original research and comprehensive reviews, including but not limited to the following topics:

- machine learning

- image processing

- image segmentation

- deep learning

- data pre-processing

- real-time applications

- transfer learning

- IoT

- distributed machine learning

- big data.

Technical Committee Member

Name: Dr. Abdul Lateef Haroon
Affiliation: Ballari Institute of Technology and Management, Ballari, Karnataka, India
Email: abdul.lh@bitm.edu.in
Homepage: https://scholar.google.co.in/citations?user=m1PuLkAAAAAJ&hl=en
Research Interests: computer vision; machine learning; image and signal processing; 5G communications; IoT

Name: José Luis Vázquez Noguera
Affiliation: Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo, Paraguay
Email: jlvazquez@pol.una.py
Homepage: https://scholar.google.com.py/citations?user=jxBEu0cAAAAJ&hl=es
Research Interests: computer vision; image processing; image segmentation; deep learning

Name: Julio César Mello-Román
Affiliation: Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo, Paraguay
Email: juliomello@pol.una.py
Homepage: https://scholar.google.es/citations?user=j74AQ64AAAAJ&hl=es
Research Interests: artificial intelligence; machine learning; image processing and analysis; mathematical morphology

Name: Federico Divina
Affiliation: Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Email: fdivina@upo.es
Homepage: https://datalab.upo.es/profile/divina/
Research Interests: artificial intelligence; machine learning; evolutionary algorithms; soft computing

Name: Sebastián Alberto Grillo
Affiliation: Universidad Autónoma de Asunción, Asunción, Paraguay
Email: sgrillo@uaa.edu.py
Homepage: https://scholar.google.com/citations?user=pNm4FpsAAAAJ&hl=es
Research Interests: artificial intelligence; machine learning; computational complexity

Name: Dr. Perla Sosa de Wood
Affiliation: Universidad Nacional de Itapúa, Paraguay
Email: psosa@uni.edu.py
Homepage: https://scholar.google.es/citations?user=l2ABsbEAAAAJ&hl=en&oi=ao
Research Interests: machine learning; educational data mining; IoT

Dr. Miguel García-Torres
Dr. Francisco A. Gómez Vela
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. Electronics 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

  • machine learning
  • image processing
  • image segmentation
  • deep learning
  • data pre-processing
  • real-time applications
  • transfer learning
  • IoT
  • distributed machine learning
  • big data

Published Papers (2 papers)

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Research

21 pages, 19340 KiB  
Communication
Objective Video Quality Assessment Method for Object Recognition Tasks
by Mikołaj Leszczuk, Lucjan Janowski, Jakub Nawała and Atanas Boev
Electronics 2024, 13(9), 1750; https://doi.org/10.3390/electronics13091750 - 01 May 2024
Viewed by 100
Abstract
In the field of video quality assessment for object recognition tasks, accurately predicting the impact of different quality factors on recognition algorithms remains a significant challenge. Our study introduces a novel evaluation framework designed to address this gap by focussing on machine vision [...] Read more.
In the field of video quality assessment for object recognition tasks, accurately predicting the impact of different quality factors on recognition algorithms remains a significant challenge. Our study introduces a novel evaluation framework designed to address this gap by focussing on machine vision rather than human perceptual quality metrics. We used advanced machine learning models and custom Video Quality Indicators to enhance the predictive accuracy of object recognition performance under various conditions. Our results indicate a model performance, achieving a mean square error (MSE) of 672.4 and a correlation coefficient of 0.77, which underscores the effectiveness of our approach in real-world scenarios. These findings highlight not only the robustness of our methodology but also its potential applicability in critical areas such as surveillance and telemedicine. Full article
(This article belongs to the Special Issue Machine Learning, Image Analysis and IoT Applications in Industry)
21 pages, 11294 KiB  
Article
Aero-YOLO: An Efficient Vehicle and Pedestrian Detection Algorithm Based on Unmanned Aerial Imagery
by Yifan Shao, Zhaoxu Yang, Zhongheng Li and Jun Li
Electronics 2024, 13(7), 1190; https://doi.org/10.3390/electronics13071190 - 25 Mar 2024
Cited by 1 | Viewed by 768
Abstract
The cost-effectiveness, compact size, and inherent flexibility of UAV technology have garnered significant attention. Utilizing sensors, UAVs capture ground-based targets, offering a novel perspective for aerial target detection and data collection. However, traditional UAV aerial image recognition techniques suffer from various drawbacks, including [...] Read more.
The cost-effectiveness, compact size, and inherent flexibility of UAV technology have garnered significant attention. Utilizing sensors, UAVs capture ground-based targets, offering a novel perspective for aerial target detection and data collection. However, traditional UAV aerial image recognition techniques suffer from various drawbacks, including limited payload capacity, resulting in insufficient computing power, low recognition accuracy due to small target sizes in images, and missed detections caused by dense target arrangements. To address these challenges, this study proposes a lightweight UAV image target detection method based on YOLOv8, named Aero-YOLO. The specific approach involves replacing the original Conv module with GSConv and substituting the C2f module with C3 to reduce model parameters, extend the receptive field, and enhance computational efficiency. Furthermore, the introduction of the CoordAtt and shuffle attention mechanisms enhances feature extraction, which is particularly beneficial for detecting small vehicles from a UAV perspective. Lastly, three new parameter specifications for YOLOv8 are proposed to meet the requirements of different application scenarios. Experimental evaluations were conducted on the UAV-ROD and VisDrone2019 datasets. The results demonstrate that the algorithm proposed in this study improves the accuracy and speed of vehicle and pedestrian detection, exhibiting robust performance across various angles, heights, and imaging conditions. Full article
(This article belongs to the Special Issue Machine Learning, Image Analysis and IoT Applications in Industry)
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