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Electronics
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Machine Learning Techniques for Image Processing
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Machine Learning Techniques for Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 6406

Special Issue Editors

Dr. Tahir Cetin Akinci

E-Mail Website
Guest Editor
Winston Chung Global Energy Center (WCGEC), University of California Riverside, Riverside, CA 92521, USA
Interests: machine learning; image processing; signal processing; computer sciences; artificial intelligence; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Mustafa Ilhan Akbas

E-Mail Website
Guest Editor
Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Interests: computer engineering; cyber–physical systems; software defined networks
Special Issues, Collections and Topics in MDPI journals
Dr. Gokhan Erdemir

E-Mail Website
Guest Editor
College of Engineering and Computer Science, University of Tennessee at Chattanooga, 615 McCallie Ave, Chattanooga, United States
Interests: robotics; mobile robotics; control systems; intelligent algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Oguzhan Topsakal

E-Mail Website
Guest Editor
Department of Computer Science, Florida Polytechnic University, Lakeland, FL 33805, USA
Interests: machine learning; augmented reality

Special Issue Information

Dear Colleagues,

Today, machine learning and image processing are applied in every field of science and technology.

In the real world, in addition to traditional image processing methods, there are machine learning algorithms for complex and diverse image data, data-driven approaches, and applications where models can learn from large amounts of labeled or unlabeled image data to automatically discover patterns, properties, and relationships.

Various machine learning techniques, such as convolutional neural networks, which are widely used in image processing and perform outstandingly in tasks such as image classification, object detection, and semantic segmentation, find wide-ranging applications in industry, health, agriculture, finance, and social sciences. Studies related to industrial, biomedical data applications, autonomous vehicle and drone technologies, agriculture and cartography, computational sciences, finance, marketing and advertising are within the scope of this Special Issue, which hopes to be a platform to explore all machine learning-based applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Medical science;
  • Biomedical science;
  • Statistics and mathematics;
  • Engineering/industrial systems;
  • Computer and computational sciences;
  • Electric, electronic, and mechatronic systems;
  • Autonomous vehicles;
  • Aviation and drone technologies;
  • Energy systems;
  • Material science;
  • Finance;
  • Marketing, advertising, and management;
  • Psychology;
  • Social media.

We look forward to receiving your contributions.

Dr. Tahir Cetin Akinci
Dr. Mustafa Ilhan Akbas
Dr. Gokhan Erdemir
Dr. Oguzhan Topsakal
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
  • deep learning
  • artificial neural networks
  • artificial intelligence

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

23 pages, 4139 KiB  
Article
Estimation of Uncertain Parameters in Single and Double Diode Models of Photovoltaic Panels Using Frilled Lizard Optimization
by Süleyman Dal and Necmettin Sezgin
Electronics 2025, 14(4), 796; https://doi.org/10.3390/electronics14040796 - 18 Feb 2025
Viewed by 378
Abstract
Renewable energy sources are increasingly crucial for sustainable development. Photovoltaic (PV) systems, which convert solar energy into electricity, offer an environmentally friendly solution. Enhancing energy efficiency and minimizing environmental impacts in these systems heavily rely on parameter optimization. In this study, the Frilled [...] Read more.
Renewable energy sources are increasingly crucial for sustainable development. Photovoltaic (PV) systems, which convert solar energy into electricity, offer an environmentally friendly solution. Enhancing energy efficiency and minimizing environmental impacts in these systems heavily rely on parameter optimization. In this study, the Frilled Lizard Optimization (FLO) algorithm is proposed as a novel approach, integrating the newton-raphson method into the root mean square error (RMSE) objective function process to address nonlinear equations. Extensive analyses conducted on RTC France, STM6-40/36, and Photowatt PWP201 modules demonstrate the superior performance of the FLO algorithm using MATLAB R2022a software with Intel(R) Core(TM) i7-7500U CPU@ 2.70GHz 2.90 GHz 8 GB RAM. The RMSE values were calculated as 0.0030375 and 0.011538 for SDM and DDM in the RTC France dataset, 0.012036 for the STM6-40/36 dataset and 0.0097545 for the Photowatt-PWP201 dataset, respectively, indicating significantly lower error margins compared to other optimisation methods. Additionally, comprehensive evaluations were carried out using error metrics such as individual absolute error (IAE), relative error (RE) and mean absolute error (MAE), supported by detailed graphical representations of measured and predicted parameters. Current-voltage (I-V) and power-voltage (P-V) characteristic curves, as well as convergence behaviors, were systematically analyzed. This study introduces an innovative and robust solution for parameter optimization in PV systems, contributing to both theoretical and industrial applications. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Image Processing)
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26 pages, 7627 KiB  
Article
Revealing GLCM Metric Variations across a Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications
by Masud Kabir, Fatih Unal, Tahir Cetin Akinci, Alfredo A. Martinez-Morales and Sami Ekici
Electronics 2024, 13(12), 2299; https://doi.org/10.3390/electronics13122299 - 12 Jun 2024
Cited by 2 | Viewed by 1554
Abstract
This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through [...] Read more.
This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through a comprehensive examination of publicly available plant disease datasets, focusing on their performance as measured by GLCM metrics, this research identified dataset_2 (D2), a database of leaf images, as the top performer across all GLCM analyses. These datasets were then utilized to train the DarkNet19 deep learning model, with D2 exhibiting superior performance in both GLCM analysis and DarkNet19 training (achieving about 91% testing accuracy) according to performance metrics such as accuracy, precision, recall, and F1-score. The datasets other than dataset_1 and 2 exhibited significantly low classification performance, particularly in supporting GLCM analysis. The findings underscore the need for transparency and rigor in dataset selection, particularly given the abundance of similar datasets in the literature and the growing trend of utilizing deep learning methods in future scientific research. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Image Processing)
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14 pages, 5830 KiB  
Article
Detection of Dangerous Human Behavior by Using Optical Flow and Hybrid Deep Learning
by Laith Mohammed Salim and Yuksel Celik
Electronics 2024, 13(11), 2116; https://doi.org/10.3390/electronics13112116 - 29 May 2024
Cited by 2 | Viewed by 1536
Abstract
Dangerous human behavior in the driving sense may cause traffic accidents and even cause economic losses and casualties. Accurate identification of dangerous human behavior can prevent potential risks. To solve the problem of difficulty retaining the temporal characteristics of the existing data, this [...] Read more.
Dangerous human behavior in the driving sense may cause traffic accidents and even cause economic losses and casualties. Accurate identification of dangerous human behavior can prevent potential risks. To solve the problem of difficulty retaining the temporal characteristics of the existing data, this paper proposes a human behavior recognition model based on utilized optical flow and hybrid deep learning model-based 3D CNN-LSTM in stacked autoencoder and uses the abnormal behavior of humans in real traffic scenes to verify the proposed model. This model was tested using HMDB51 datasets and JAAD dataset and compared with the recent related works. For a quantitative test, the HMDB51 dataset was used to train and test models for human behavior. Experimental results show that the proposed model achieved good accuracy of about 86.86%, which outperforms recent works. For qualitative analysis, we depend on the initial annotations of walking movements in the JAAD dataset to streamline the annotating process to identify transitions, where we take into consideration flow direction, if it is cross-vehicle motion (to be dangerous) or if it is parallel to vehicle motion (to be of no danger). The results show that the model can effectively identify dangerous behaviors of humans and then test on the moving vehicle scene. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Image Processing)
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19 pages, 22377 KiB  
Article
Learning the Frequency Domain Aliasing for Real-World Super-Resolution
by Yukun Hao and Feihong Yu
Electronics 2024, 13(2), 250; https://doi.org/10.3390/electronics13020250 - 5 Jan 2024
Cited by 1 | Viewed by 1785
Abstract
Most real-world super-resolution methods require synthetic image pairs for training. However, the frequency domain gap between synthetic images and real-world images leads to artifacts and blurred reconstructions. This work points out that the main reason for the frequency domain gap is that aliasing [...] Read more.
Most real-world super-resolution methods require synthetic image pairs for training. However, the frequency domain gap between synthetic images and real-world images leads to artifacts and blurred reconstructions. This work points out that the main reason for the frequency domain gap is that aliasing exists in real-world images, but the degradation model used to generate synthetic images ignores the impact of aliasing on images. Therefore, a method is proposed in this work to assess aliasing in images undergoing unknown degradation by measuring the distance to their alias-free counterparts. Leveraging this assessment, a domain-translation framework is introduced to learn degradation from high-resolution to low-resolution images. The proposed framework employs a frequency-domain branch and loss function to generate synthetic images with aliasing features. Experiments validate that the proposed domain-translation framework enhances the visual quality and quantitative results compared to existing super-resolution models across diverse real-world image benchmarks. In summary, this work offers a practical solution to the real-world super-resolution problem by minimizing the frequency domain gap between synthetic and real-world images. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Image Processing)
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