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Electronics
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Advances in Information, Intelligence, Systems and Applications
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Advances in Information, Intelligence, Systems and Applications

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 2651

Special Issue Editors

Prof. Dr. George A. Tsihrintzis

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Guest Editor
Department of Informatics, University of Piraeus, 18534 Piraeus, Greece
Interests: artificial intelligence; computational intelligence; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maria Virvou

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Guest Editor
Head of the Department of Informatics, University of Piraeus, 18534 Piraeus, Greece
Interests: computer education; artificial intelligence; software engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Vangelis Marinakis

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Guest Editor
School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, 15780 Athens, Greece
Interests: intelligent energy management; smart cities; smart homes; decision support systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nikolaos G. Bourbakis

E-Mail Website
Guest Editor
Biological & Artificial Intelligence Foundation (BAIF), 9834 Country Creek Way, Centerville, OH 45458, USA
Interests: assistive technologies; applied artificial intelligence; bioengineering; information security; parallel/distributed processing

Special Issue Information

Information is widely available and accessible, but frequently leads to information overload and overexposure, and the effort for coding, storing, hiding, securing, transmitting, and retrieving it may be excessive. Intelligence is required to manage information and extract knowledge from it, inspired by biological and other paradigms. Information and multimedia systems are being developed, which incorporate these advances and demonstrate an increasing level of intelligence. As a result, new technologies, protocols, and novel applications are emerging. In turn, the novel applications lead to the collection of new information, with the information circle being repeated at ever higher levels.

This SI will include works which cover a broad spectrum of topics, from both novelties in theory and advanced methodologies, to applications in the energy, agriculture, medicine, forensics, healthcare, education, tourism, inspection, traffic, transportation, and software engineering.

Prof. Dr. George A. Tsihrintzis
Prof. Dr. Maria Virvou
Dr. Vangelis Marinakis
Prof. Dr. Nikolaos G. Bourbakis
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

  • artificial intelligence
  • machine learning
  • deep learning
  • explainable artificial intelligence
  • trustworthy and responsible artificial intelligence
  • artificial intelligence-empowered system

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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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

22 pages, 1544 KiB  
Article
Protocol for Evaluating Explainability in Actuarial Models
by Catalina Lozano-Murcia, Francisco P. Romero and Mᵃ Concepción Gonzalez-Ramos
Electronics 2025, 14(8), 1561; https://doi.org/10.3390/electronics14081561 - 11 Apr 2025
Viewed by 155
Abstract
This paper explores the use of explainable artificial intelligence (XAI) techniques in actuarial science to address the opacity of advanced machine learning models in financial contexts. While technological advancements have enhanced actuarial models, their black box nature poses challenges in highly regulated environments. [...] Read more.
This paper explores the use of explainable artificial intelligence (XAI) techniques in actuarial science to address the opacity of advanced machine learning models in financial contexts. While technological advancements have enhanced actuarial models, their black box nature poses challenges in highly regulated environments. This study proposes a protocol for selecting and applying XAI techniques to improve interpretability, transparency, and regulatory compliance. It categorizes techniques based on origin, target, and interpretative capacity, and introduces a protocol to identify the most suitable method for actuarial models. The proposed protocol is tested in a case study involving two classification algorithms, gradient boosting and random forest, with accuracy of 0.80 and 0.79, focusing on two explainability objectives. Several XAI techniques are analyzed, with results highlighting partial dependency variance (PDV) and local interpretable model-agnostic explanations (LIME) as effective tools for identifying key variables. The findings demonstrate that the protocol aids in model selection, internal audits, regulatory compliance, and enhanced decision-making transparency. These advantages make it particularly valuable for improving model governance in the financial sector. Full article
(This article belongs to the Special Issue Advances in Information, Intelligence, Systems and Applications)
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22 pages, 8643 KiB  
Article
A Comparison of Deep Learning Techniques for Pose Recognition in Up-and-Go Pole Walking Exercises Using Skeleton Images and Feature Data
by Wan-Chih Lin, Yu-Chen Tu, Hong-Yi Lin and Ming-Hseng Tseng
Electronics 2025, 14(6), 1075; https://doi.org/10.3390/electronics14061075 - 7 Mar 2025
Viewed by 736
Abstract
This study evaluates the performance of seven deep learning methods for recognizing motion patterns in Up-and-Go pole walking exercises, aiming to improve rehabilitation technologies for the elderly population. For the ageing population, improving the accuracy of movement posture for elderly people is crucial [...] Read more.
This study evaluates the performance of seven deep learning methods for recognizing motion patterns in Up-and-Go pole walking exercises, aiming to improve rehabilitation technologies for the elderly population. For the ageing population, improving the accuracy of movement posture for elderly people is crucial in obtaining better rehabilitation outcomes. Up-and-Go pole walking exercises offer significant health benefits, but attaining the correct pose in motion is essential for achieving these benefits. The dataset includes skeleton images generated by OpenPose 1.7.0 and 2D and 3D skeleton images extracted through MediaPipe 0.10.21. Two sets of feature data were developed for model evaluation: one that comprises 12 features representing the key coordinates of the hands and feet and another consisting of 30 features derived from subdivided full-body skeletons. The study compares the accuracy and performance of each method, examining the impact of different combinations and representations on motion patterns. The experimental results indicate that the Swin model based on MediaPipe 2D skeleton images achieved the highest accuracy (99.7%), demonstrating superior performance in recognizing motion patterns of Up-and-Go pole walking exercises. The study summarizes the advantages and limitations of each approach, highlighting the contributions of different features and data representations to recognition outcomes. This research provides scientific evidence to advance elderly rehabilitation technologies by accurately recognizing poses. Full article
(This article belongs to the Special Issue Advances in Information, Intelligence, Systems and Applications)
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34 pages, 361 KiB  
Article
Model Checking Using Large Language Models—Evaluation and Future Directions
by Sotiris Batsakis, Ilias Tachmazidis, Matthew Mantle, Nikolaos Papadakis and Grigoris Antoniou
Electronics 2025, 14(2), 401; https://doi.org/10.3390/electronics14020401 - 20 Jan 2025
Viewed by 1142
Abstract
Large language models (LLMs) such as ChatGPT have risen in prominence recently, leading to the need to analyze their strengths and limitations for various tasks. The objective of this work was to evaluate the performance of large language models for model checking, which [...] Read more.
Large language models (LLMs) such as ChatGPT have risen in prominence recently, leading to the need to analyze their strengths and limitations for various tasks. The objective of this work was to evaluate the performance of large language models for model checking, which is used extensively in various critical tasks such as software and hardware verification. A set of problems were proposed as a benchmark in this work and three LLMs (GPT-4, Claude, and Gemini) were evaluated with respect to their ability to solve these problems. The evaluation was conducted by comparing the responses of the three LLMs with the gold standard provided by model checking tools. The results illustrate the limitations of LLMs in these tasks, identifying directions for future research. Specifically, the best overall performance (ratio of problems solved correctly) was 60%, indicating a high probability of reasoning errors by the LLMs, especially when dealing with more complex scenarios requiring many reasoning steps, and the LLMs typically performed better when generating scripts for solving the problems rather than solving them directly. Full article
(This article belongs to the Special Issue Advances in Information, Intelligence, Systems and Applications)
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