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Electronics
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Advancements in Artificial Intelligence (AI) for Engineering Applications
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Advancements in Artificial Intelligence (AI) for Engineering Applications

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 3675

Special Issue Editor

Dr. Hubert Zarzycki

E-Mail Website
Guest Editor
Faculty of Computer Science and Technology, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Interests: artificial intelligence; optimization techniques; fuzzy logic; natural language processing; reinforcement learning

Special Issue Information

Dear Colleagues,

We are excited to issue a call for papers for a Special Issue of Electronics focusing on the intersection of artificial intelligence (AI) with engineering applications. This Special Issue aims to explore the latest advancements and practical implementations within the realms of swarm intelligence, optimization, fuzzy logic, natural language processing (NLP), computer vision, and reinforcement learning.

The overall focus, scope, and purpose of this Special Issue are delineated as follows:

(a) Focus: The Special Issue will concentrate on exploring innovative methodologies and algorithms within the fields of swarm intelligence, optimization, fuzzy logic, NLP, computer vision, and reinforcement learning, with a specific emphasis on their application in engineering domains.

(b) Scope: We welcome contributions that present novel research findings, methodologies, case studies, and applications related to the aforementioned AI fields in engineering contexts. Topics of interest include, but are not limited to, the following:

  • Development of advanced AI-based optimization techniques for engineering problems;
  • Integration of fuzzy logic principles into engineering systems to enhance adaptability and decision-making;
  • Utilization of NLP for improving human–computer interaction in engineering applications;
  • Application of computer vision techniques for object recognition, image analysis, and visual perception in engineering tasks;
  • Implementation of reinforcement learning algorithms for autonomous decision-making and control in engineering systems.

(c) Purpose: The purpose of this Special Issue is to provide a platform for researchers to disseminate their latest findings, exchange insights, and foster collaborations for advancing the state of the art in AI-driven engineering applications. By showcasing practical implementations and case studies, we aim to bridge the gap between theoretical advancements in AI and their real-world applications in engineering.

This Special Issue will complement the existing literature by taking the following steps:

  • Offering a comprehensive overview of the latest trends and advancements in AI techniques as applied to engineering problems;
  • Providing in-depth discussions and analyses of practical case studies and applications, thereby offering valuable insights for both academia and industry practitioners;
  • Stimulating further research and innovation in this field by identifying emerging challenges and potential areas for future exploration.

We encourage researchers from academia, industry, and other relevant sectors to contribute their original research articles for publication in this Special Issue.

We eagerly anticipate your contributions to this Special Issue and advance our collective understanding of AI-driven engineering applications.

Dr. Hubert Zarzycki
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

  • artificial intelligence
  • swarm intelligence
  • optimization
  • fuzzy logic
  • natural language processing (NLP)
  • computer vision
  • reinforcement learning
  • engineering applications

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

Published Papers (3 papers)

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Research

30 pages, 1455 KiB  
Article
Automated Formative Feedback for Algorithm and Data Structure Self-Assessment
by Lourdes Araujo, Fernando Lopez-Ostenero, Laura Plaza and Juan Martinez-Romo
Electronics 2025, 14(5), 1034; https://doi.org/10.3390/electronics14051034 - 5 Mar 2025
Viewed by 571
Abstract
Self-evaluation empowers students to progress independently and adapt their pace according to their unique circumstances. A critical facet of self-assessment and personalized learning lies in furnishing learners with formative feedback. This feedback, dispensed following their responses to self-assessment questions, constitutes a pivotal component [...] Read more.
Self-evaluation empowers students to progress independently and adapt their pace according to their unique circumstances. A critical facet of self-assessment and personalized learning lies in furnishing learners with formative feedback. This feedback, dispensed following their responses to self-assessment questions, constitutes a pivotal component of formative assessment systems. We hypothesize that it is possible to generate explanations that are useful as formative feedback using different techniques depending on the type of self-assessment question under consideration. This study focuses on a subject taught in a computer science program at a Spanish distance learning university. Specifically, it delves into advanced data structures and algorithmic frameworks, which serve as overarching principles for addressing complex problems. The generation of these explanatory resources hinges on the specific nature of the question at hand, whether theoretical, practical, related to computational cost, or focused on selecting optimal algorithmic approaches. Our work encompasses a thorough analysis of each question type, coupled with tailored solutions for each scenario. To automate this process as much as possible, we leverage natural language processing techniques, incorporating advanced methods of semantic similarity. The results of the assessment of the feedback generated for a subset of theoretical questions validate the effectiveness of the proposed methods, allowing us to seamlessly integrate this feedback into the self-assessment system. According to a survey, students found the resulting tool highly useful. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence (AI) for Engineering Applications)
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20 pages, 5610 KiB  
Article
Graph Neural Network (GNN) for Joint Detection–Decoder MAP–LDPC in Bit-Patterned Media Recording Systems
by Thien An Nguyen and Jaejin Lee
Electronics 2024, 13(23), 4811; https://doi.org/10.3390/electronics13234811 - 5 Dec 2024
Cited by 1 | Viewed by 1092
Abstract
With its high area density, bit-patterned media recording (BPMR) is emerging as a leading technology for next-generation storage systems. However, as area density increases, magnetic islands are positioned closer together, causing significant two-dimensional (2D) interference. To address this, detection methods are used to [...] Read more.
With its high area density, bit-patterned media recording (BPMR) is emerging as a leading technology for next-generation storage systems. However, as area density increases, magnetic islands are positioned closer together, causing significant two-dimensional (2D) interference. To address this, detection methods are used to interpret the received signal and mitigate 2D interference. Recently, the maximum a posteriori (MAP) detection algorithm has shown promise in improving BPMR performance, though it requires extrinsic information to effectively reduce interference. In this paper, to solve the 2D interference and improve the performance of BPMR systems, a model using low-density parity-check (LDPC) coding was introduced to supply the MAP detector with the needed extrinsic information, enhancing detection in a joint decoding model we call MAP–LDPC. Additionally, leveraging similarities between LDPC codes and graph neural networks (GNNs), we replace the traditional sum–product algorithm in LDPC decoding with a GNN, creating a new model, MAP–GNN. The simulation results demonstrate that MAP–GNN achieves superior performance, particularly when using the deep learning-based GNN approach over conventional techniques. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence (AI) for Engineering Applications)
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23 pages, 770 KiB  
Article
Computationally Efficient Inference via Time-Aware Modular Control Systems
by Dmytro Shchyrba and Hubert Zarzycki
Electronics 2024, 13(22), 4416; https://doi.org/10.3390/electronics13224416 - 11 Nov 2024
Viewed by 1090
Abstract
Control in multi-agent decision-making systems is an important issue with a wide variety of existing approaches. In this work, we offer a new comprehensive framework for distributed control. The main contributions of this paper are summarized as follows. First, we propose PHIMEC (physics-informed [...] Read more.
Control in multi-agent decision-making systems is an important issue with a wide variety of existing approaches. In this work, we offer a new comprehensive framework for distributed control. The main contributions of this paper are summarized as follows. First, we propose PHIMEC (physics-informed meta control)—an architecture for learning optimal control by employing a physics-informed neural network when the state space is too large for reward-based learning. Second, we offer a way to leverage impulse response as a tool for system modeling and control. We propose IMPULSTM, a novel approach for incorporating time awareness into recurrent neural networks designed to accommodate irregular sampling rates in the signal. Third, we propose DIMAS, a modular approach to increasing computational efficiency in distributed control systems via domain-knowledge integration. We analyze the performance of the first two contributions on a set of corresponding benchmarks and then showcase their combined performance as a domain-informed distributed control system. The proposed approaches show satisfactory performance both individually in their respective applications and as a connected system. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence (AI) for Engineering Applications)
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