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Electronics
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Machine/Deep Learning Applications and Intelligent Systems
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Machine/Deep Learning Applications and Intelligent Systems

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 2495

Special Issue Editors

Dr. Sungju Lee

E-Mail
Guest Editor
Department of Software, Sangmyung University, Cheonan 31066, Republic of Korea
Interests: image processing; computer vision; meta learning; smart agriculture; fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dong-Sung Pae

E-Mail Website
Guest Editor
Department of Software, Sangmyung University, Cheonan 31066, Republic of Korea
Interests: artificial intelligence; machine learning; computer vision; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Guest Editors are inviting submissions for a Special Issue of Electronics on the subject of “Learning Applications and Intelligent Systems”. In recent decades, research using machine learning and deep learning in artificial intelligence has been active in ICT technology and various other fields. Recently, artificial intelligence technology has rapidly developed with the advent of Super-Giant AI and foundation models such as Large Language Models for specialized domains. In addition, to apply this, research is required on generalized knowledge transfer methods and improved learning applications for on-device integration via lightweighting techniques. This Special Issue mainly focuses on the diversity of learning applications and intelligent systems across various artificial intelligence technologies including Super-Giant AI and on-device issues. Topics of interest for publication include, but are not limited to, the following:

  • Foundation learning models for various applications;
  • On-device learning models for various applications;
  • Intelligent systems for various applications;
  • AI robotics;
  • AI healthcare;
  • AI smart city.

Dr. Sungju Lee
Prof. Dr. Dong-Sung Pae
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

  • foundation models
  • on-device models
  • intelligent systems
  • AI robotics
  • AI healthcare
  • AI smart city

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

Published Papers (5 papers)

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Research

25 pages, 8392 KiB  
Article
Assessing Urban Activity and Accessibility in the 20 min City Concept
by Tsetsentsengel Munkhbayar, Zolzaya Dashdorj, Hun-Hee Cho, Jun-Woo Lee, Tae-Koo Kang and Erdenebaatar Altangerel
Electronics 2025, 14(8), 1693; https://doi.org/10.3390/electronics14081693 - 21 Apr 2025
Viewed by 226
Abstract
The 20 min city concept ensures that essential services—such as work, education, healthcare, and recreation—are accessible within a 20 min walk or transit ride. This study evaluates urban accessibility in Ulaanbaatar by analyzing Points of Interest (POIs) and public bus transit networks using [...] Read more.
The 20 min city concept ensures that essential services—such as work, education, healthcare, and recreation—are accessible within a 20 min walk or transit ride. This study evaluates urban accessibility in Ulaanbaatar by analyzing Points of Interest (POIs) and public bus transit networks using spatial analytics and deep learning techniques. Our finding highlights that geographical area characterization is a good proxy for predicting ridership in transit networks. For instance, healthcare and medical areas show a strong correlation with similar ridership behaviors. However, some areas lack nearby bus stations, leading to poorly placed transit stops with low walking scores. To address this, we propose the use of a Quad-Bus approach to identify optimal bus station locations in urban and suburban areas, considering amenity density and deep learning ridership models to diagnose and remedy accessibility gaps. This approach is evaluated using walking and transit scores for distances ranging from 5 to 20 min in the case of Ulaanbaatar city. Results show a moderate overall link between amenity density and ridership (r = 0.44), rising to 0.53 around healthcare clusters. However, >500 high-activity partitions contain no bus stop, and 40% of the city scores below 50 on a 0–100 walking index. Half of urban areas lack a stop within 300 m, leaving 60% of residents beyond a 10 min walk. Quad-Bus reallocations close many of these gaps, boosting walk and transit scores simultaneously. This research offers valuable insights for enhancing mobility, reducing car dependency, and optimizing urban planning to create equitable and sustainable 20 min city models. Full article
(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)
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16 pages, 910 KiB  
Article
An Application of Explainable Multi-Agent Reinforcement Learning for Spectrum Situational Awareness
by Dominick J. Perini, Braeden P. Muller, Justin Kopacz and Alan J. Michaels
Electronics 2025, 14(8), 1533; https://doi.org/10.3390/electronics14081533 - 10 Apr 2025
Viewed by 254
Abstract
Allocating low-bandwidth radios to observe a wide portion of a spectrum is a key class of search-optimization problems that requires system designers to leverage limited resources and information efficiently. This work describes a multi-agent reinforcement learning system that achieves a balance between tuning [...] Read more.
Allocating low-bandwidth radios to observe a wide portion of a spectrum is a key class of search-optimization problems that requires system designers to leverage limited resources and information efficiently. This work describes a multi-agent reinforcement learning system that achieves a balance between tuning radios to newly observed energy while maintaining regular sweep intervals to yield detailed captures of both short- and long-duration signals. This algorithm, which we have named SmartScan, and system implementation have demonstrated live adaptations to dynamic spectrum activity, persistence of desirable sweep intervals, and long-term stability. The SmartScan algorithm was also designed to fit into a real-time system by guaranteeing a constant inference latency. The result is an explainable, customizable, and modular approach to implementing intelligent policies into the scan scheduling of a spectrum monitoring system. Full article
(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)
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21 pages, 723 KiB  
Article
RansomFormer: A Cross-Modal Transformer Architecture for Ransomware Detection via the Fusion of Byte and API Features
by Saleh Alzahrani, Yang Xiao, Sultan Asiri, Naif Alasmari and Tieshan Li
Electronics 2025, 14(7), 1245; https://doi.org/10.3390/electronics14071245 - 21 Mar 2025
Viewed by 340
Abstract
Ransomware remains one of the most significant cybersecurity threats. Techniques used by attackers have evolved to bypass traditional detection methods. Many existing detection systems rely on outdated datasets or complex behavioral analyses, which are resource-intensive and slow. This paper introduces RansomFormer, a Transformer-based [...] Read more.
Ransomware remains one of the most significant cybersecurity threats. Techniques used by attackers have evolved to bypass traditional detection methods. Many existing detection systems rely on outdated datasets or complex behavioral analyses, which are resource-intensive and slow. This paper introduces RansomFormer, a Transformer-based model that is designed to detect ransomware using Portable Executable (PE) byte data combined with Application Programming Interface (API) imports or API sequence calls. The evaluation is conducted to determine whether these static and dynamic features alone can achieve high accuracy. To test this hypothesis, the largest ransomware dataset to date is collected, consisting of more than 150 ransomware families. The limitations of existing datasets, which are outdated, lack family variants, or are too small, are addressed by this dataset. RansomFormer is trained and evaluated on the following two datasets: one using static analysis (PE bytes and API imports) and another combining static and dynamic analysis (PE bytes and API sequence calls). The results demonstrate that the model achieves high accuracy, with 99.25% on the static dataset and 99.50% on the combined dataset, making RansomFormer a promising approach for ransomware detection. Full article
(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)
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18 pages, 23703 KiB  
Article
Asymmetry Elliptical Likelihood Potential Field for Real-Time Three-Dimensional Collision Avoidance in Industrial Robots
by Ean-Gyu Han, Dong-Min Seo, Jun-Seo Lee, Ho-Young Kim, Shin-Yeob Kang, Ho-Joon Yang and Tae-Koo Kang
Electronics 2025, 14(6), 1102; https://doi.org/10.3390/electronics14061102 - 11 Mar 2025
Viewed by 416
Abstract
Industrial robots play a crucial role in modern manufacturing, but ensuring safe human–robot collaboration remains a challenge. Traditional collision avoidance methods, such as physical barriers and emergency stops, are limited in efficiency and flexibility. This study proposes the Asymmetry Elliptical Likelihood Potential Field [...] Read more.
Industrial robots play a crucial role in modern manufacturing, but ensuring safe human–robot collaboration remains a challenge. Traditional collision avoidance methods, such as physical barriers and emergency stops, are limited in efficiency and flexibility. This study proposes the Asymmetry Elliptical Likelihood Potential Field (AELPF) algorithm, a novel real-time collision avoidance system inspired by autonomous driving technologies. The AELPF method leverages LiDAR sensors to dynamically compute an asymmetric elliptical repulsive field, enabling precise obstacle detection and avoidance in 3D environments. Unlike conventional potential field approaches, the AELPF accounts for both vertical and horizontal deviations, allowing it to adapt to complex industrial settings. To quantify the performance of AELPF, we compare it to two commonly used algorithms: the Vector Field Histogram (VFH) and the Follow the Gap Method (FGM). In terms of processing time, the VFH algorithm requires 50 ms per cycle, while the FGM algorithm operates at 22 ms. In contrast, the the AELPF, when using only a single channel, processes at 12 ms, which is significantly faster than both the VFH and FGM. These results indicate that the AELPF not only provides faster decision-making but also ensures smoother, more responsive navigation in dynamic environments. Both simulation and physical experiments confirm that the AELPF significantly improves obstacle avoidance, particularly in the z-axis direction, reducing the risk of collisions while maintaining operational efficiency. Full article
(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)
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27 pages, 1244 KiB  
Article
HYLR-FO: Hybrid Approach Using Language Models and Rule-Based Systems for On-Device Food Ordering
by Subhin Yang, Donghwan Kim and Sungju Lee
Electronics 2025, 14(4), 775; https://doi.org/10.3390/electronics14040775 - 17 Feb 2025
Viewed by 585
Abstract
Recent research has explored combining large language models (LLMs) with speech recognition for various services, but such applications require a strong network environment for quality service delivery. For on-device services, which do not rely on networks, resource limitations must be considered. This study [...] Read more.
Recent research has explored combining large language models (LLMs) with speech recognition for various services, but such applications require a strong network environment for quality service delivery. For on-device services, which do not rely on networks, resource limitations must be considered. This study proposes HYLR-FO, an efficient model that integrates a smaller language model (LM) and a rule-based system (RBS) to enable fast and reliable voice-based order processing in resource-constrained environments, approximating the performance of LLMs. By considering potential error scenarios and leveraging flexible natural language processing (NLP) and inference validation, this approach ensures both efficiency and robustness in order execution. Smaller LMs are used instead of LLMs to reduce resource usage. The LM transforms speech input, received via automatic speech recognition (ASR), into a consistent form that can be processed by the RBS. The RBS then extracts the order and validates the extracted information. The experimental results show that HYLR-FO, trained and tested on 5000 order data samples, achieves up to 86% accuracy, comparable to the 90% accuracy of LLMs. Additionally, HYLR-FO achieves a processing speed of up to 55 orders per second, significantly outperforming LLM-based approaches, which handle only 1.14 orders per second. This results in a 48.25-fold improvement in processing speed in resource-constrained environments. This study demonstrates that HYLR-FO provides faster processing and achieves accuracy similar to LLMs in resource-constrained on-device environments. This finding has theoretical implications for optimizing LM efficiency in constrained settings and practical implications for real-time low-resource AI applications. Specifically, the design of HYLR-FO suggests its potential for efficient deployment in various commercial environments, achieving fast response times and low resource consumption with smaller models. Full article
(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)
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