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Electronics
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New Advances in Machine Learning and Its Applications
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New Advances in Machine Learning and Its Applications

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 5659

Special Issue Editor

Prof. Dr. Phillip Bradford

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, The University of Connecticut, Stamford, CT 06901, USA
Interests: machine learning; optimization; advanced machine learning; algorithms; security; artificial intelligence; banking; it security; computer security; heuristics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues，

Machine learning technologies have quickly developed and have proven substantial practical value in various application domains. For years, such technology has solved numerous complex industrial problems in the AI community, such as predictive maintenance, process optimization, task scheduling, quality improvement, supply and demand forecasting, defect detection, vibration signal recognition, etc.

This Special Issue invites original and breakthrough research in machine learning. Topics include, but are not limited to, the following:

  • Machine learning and its applications for predictive maintenance, quality control, and process optimization;
  • Machine learning and its applications for smart manufacturing process monitoring and control;
  • Machine learning and its application for intelligent manufacturing diagnostics, prognostics, and asset health management;
  • Machine learning and its applications for scheduling and supply chain management;
  • Machine learning and its applications for robotics and human–machine interaction;
  • Machine learning algorithms and approaches to handling big data, data imbalance, uncertainty, data fusion, etc.

Prof. Dr. Phillip Bradford
Guest Editor

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
  • robotics and human–machine interaction
  • intelligent manufacturing diagnostics
  • smart manufacturing
  • big data

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

Published Papers (5 papers)

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Research

18 pages, 626 KiB  
Article
Exploring Factors Influencing Depression: Socioeconomic Perspectives Using Machine Learning Analytics
by Cheong Kim
Electronics 2025, 14(3), 487; https://doi.org/10.3390/electronics14030487 - 25 Jan 2025
Viewed by 507
Abstract
Depression is a widespread mental health disorder with significant societal impacts, and while socioeconomic status (SES) is a well-established determinant, limited research has explored the unique factors influencing depression in South Korea, such as educational pressure, long working hours, and traditional gender roles. [...] Read more.
Depression is a widespread mental health disorder with significant societal impacts, and while socioeconomic status (SES) is a well-established determinant, limited research has explored the unique factors influencing depression in South Korea, such as educational pressure, long working hours, and traditional gender roles. Using data from the Korean National Health and Nutrition Examination Survey (KNHANES) collected in 2014, 2016, 2018, 2020, and 2022, this study analyzed 24,308 participants to examine the relationship between SES and depression. Depression was measured using the Patient Health Questionnaire-9 (PHQ-9), and twelve socioeconomic variables, including income, education, marital status, and working hours, were assessed using logistic regression models. The findings revealed that monthly income, age, marital status, and weekly working hours were significant predictors of depression, with higher income levels unexpectedly associated with greater depression scores, potentially due to increased stress. Gender, household size, and educational attainment were also notable contributors. This study underscores the complex interplay of SES factors and depression in South Korea’s distinct sociocultural context and highlights the need for mental health policies addressing both economic and psychological stressors, particularly for higher income individuals and women. Future research should further explore these dynamics to develop culturally sensitive mental health interventions. Full article
(This article belongs to the Special Issue New Advances in Machine Learning and Its Applications)
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19 pages, 4770 KiB  
Article
Forecasting Flower Prices by Long Short-Term Memory Model with Optuna
by Chieh-Huang Chen, Ying-Lei Lin and Ping-Feng Pai
Electronics 2024, 13(18), 3646; https://doi.org/10.3390/electronics13183646 - 13 Sep 2024
Viewed by 955
Abstract
The oriental lily ‘Casa Blanca’ is one of the most popular and high-value flowers. The period for keeping these flowers refrigerated is limited. Therefore, forecasting the prices of oriental lilies is crucial for determining the optimal planting time and, consequently, the profits earned [...] Read more.
The oriental lily ‘Casa Blanca’ is one of the most popular and high-value flowers. The period for keeping these flowers refrigerated is limited. Therefore, forecasting the prices of oriental lilies is crucial for determining the optimal planting time and, consequently, the profits earned by flower growers. Traditionally, the prediction of oriental lily prices has primarily relied on the experience and domain knowledge of farmers, lacking systematic analysis. This study aims to predict daily oriental lily prices at wholesale markets in Taiwan using many-to-many Long Short-Term Memory (MMLSTM) models. The determination of hyperparameters in MMLSTM models significantly influences their forecasting performance. This study employs Optuna, a hyperparameter optimization technique specifically designed for machine learning models, to select the hyperparameters of MMLSTM models. Various modeling datasets and forecasting time windows are used to evaluate the performance of the designed many-to-many Long Short-Term Memory with Optuna (MMLSTMOPT) models in predicting daily oriental lily prices. Numerical results indicate that the developed MMLSTMOPT model achieves highly satisfactory forecasting accuracy with an average mean absolute percentage error value of 12.7%. Thus, the MMLSTMOPT model is a feasible and promising alternative for forecasting the daily oriental lily prices. Full article
(This article belongs to the Special Issue New Advances in Machine Learning and Its Applications)
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24 pages, 5863 KiB  
Article
Utilizing TGAN and ConSinGAN for Improved Tool Wear Prediction: A Comparative Study with ED-LSTM, GRU, and CNN Models
by Milind Shah, Himanshu Borade, Vipul Dave, Hitesh Agrawal, Pranav Nair and Vinay Vakharia
Electronics 2024, 13(17), 3484; https://doi.org/10.3390/electronics13173484 - 2 Sep 2024
Cited by 2 | Viewed by 1309
Abstract
Developing precise deep learning (DL) models for predicting tool wear is challenging, particularly due to the scarcity of experimental data. To address this issue, this paper introduces an innovative approach that leverages the capabilities of tabular generative adversarial networks (TGAN) and conditional single [...] Read more.
Developing precise deep learning (DL) models for predicting tool wear is challenging, particularly due to the scarcity of experimental data. To address this issue, this paper introduces an innovative approach that leverages the capabilities of tabular generative adversarial networks (TGAN) and conditional single image GAN (ConSinGAN). These models are employed to generate synthetic data, thereby enriching the dataset and enhancing the robustness of the predictive models. The efficacy of this methodology was rigorously evaluated using publicly available milling datasets. The pre-processing of acoustic emission data involved the application of the Walsh-Hadamard transform, followed by the generation of spectrograms. These spectrograms were then used to extract statistical attributes, forming a comprehensive feature vector for model input. Three DL models—encoder-decoder long short-term memory (ED-LSTM), gated recurrent unit (GRU), and convolutional neural network (CNN)—were applied to assess their tool wear prediction capabilities. The application of 10-fold cross-validation across these models yielded exceptionally low RMSE and MAE values of 0.02 and 0.16, respectively, underscoring the effectiveness of this approach. The results not only highlight the potential of TGAN and ConSinGAN in mitigating data scarcity but also demonstrate significant improvements in the accuracy of tool wear predictions, paving the way for more reliable and precise predictive maintenance in manufacturing processes. Full article
(This article belongs to the Special Issue New Advances in Machine Learning and Its Applications)
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23 pages, 4106 KiB  
Article
Machine Learning-Based Beam Pointing Error Reduction for Satellite–Ground FSO Links
by Nilesh Maharjan and Byung Wook Kim
Electronics 2024, 13(17), 3466; https://doi.org/10.3390/electronics13173466 - 31 Aug 2024
Viewed by 1228
Abstract
Free space optical (FSO) communication, which has the potential to meet the demand for high-data-rate communications between satellites and ground stations, requires accurate alignment between the transmitter and receiver to establish a line-of-sight channel link. In this paper, we propose a machine learning [...] Read more.
Free space optical (FSO) communication, which has the potential to meet the demand for high-data-rate communications between satellites and ground stations, requires accurate alignment between the transmitter and receiver to establish a line-of-sight channel link. In this paper, we propose a machine learning (ML)-based approach to reduce beam pointing errors in FSO satellite-to-ground communications subjected to satellite vibration and weak atmospheric turbulence. ML models are utilized to find the optimal gain, which plays a crucial role in reducing pointing error displacement in a closed-loop FSO system. In designing the FSO environment, we employ several system model parameters, including control and system matrix components of the transmitter and receiver, noise parameters for the optical channel, irradiance, and the scintillation index of the signal. To predict the gain matrix of the closed-loop system, ML methods, such as tree-based algorithms, and a 1D convolutional neural network (Conv1D) are applied. Experimental results show that the Conv1D model outperforms other ML methods in gain value prediction, helping to maintain the beam position centered on the receiver aperture, minimizing beam pointing errors. When constructing a closed-loop system based on the Conv1D model, the error variance of the pointing error displacement was obtained as 0.012 and 0.015 in clear weather and light fog conditions, respectively. In addition, this research analyzes the impact of input features in a closed-loop FSO system, and compares the pointing error performance of the closed-loop setup to the conventional open-loop setup under weak turbulence. Full article
(This article belongs to the Special Issue New Advances in Machine Learning and Its Applications)
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19 pages, 635 KiB  
Article
False Data Injection Attack Detection, Isolation, and Identification in Industrial Control Systems Based on Machine Learning: Application in Load Frequency Control
by Sohrab Mokhtari and Kang K. Yen
Electronics 2024, 13(16), 3239; https://doi.org/10.3390/electronics13163239 - 15 Aug 2024
Viewed by 1001
Abstract
The integration of advanced information and communication technology in smart grids has exposed them to increased cyber attacks. Traditional model-based fault detection systems rely on mathematical models to identify malicious activities but struggle with the complexity of modern systems. This paper explores the [...] Read more.
The integration of advanced information and communication technology in smart grids has exposed them to increased cyber attacks. Traditional model-based fault detection systems rely on mathematical models to identify malicious activities but struggle with the complexity of modern systems. This paper explores the application of artificial intelligence, specifically machine learning, to develop fault detection mechanisms that do not depend on these models. We focus on operational technology for fault detection, isolation, and identification (FDII) within smart grids, specifically examining a load frequency control (LFC) system. Our proposed approach uses sensor data to accurately identify threats, demonstrating promising results in simulated environments. Full article
(This article belongs to the Special Issue New Advances in Machine Learning and Its Applications)
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