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Electronics
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Trends and Prospects in AI-Empowered Information Systems and Technologies
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Trends and Prospects in AI-Empowered Information Systems and Technologies

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

Deadline for manuscript submissions: 15 January 2026 | Viewed by 17838

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

E-Mail Website
Guest Editor
School of Information and Communication Technologies, University of Piraeus, 18534 Piraeus, Greece
Interests: computer education; artificial intelligence; software engineering
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Prof. Dr. Hironori Washizaki

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Guest Editor
Department of Computer Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku City, Tokyo 169-8555, Japan
Interests: smart systems and software engineering for business and society; education technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gloria Phillips-Wren

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Guest Editor
Sellinger School of Business and Management, Loyola University of Maryland, Baltimore, MD 21210, USA
Interests: decision support; decision making; big data; analytics; business intelligence
Dr. Miltiadis (Miltos) Alamaniotis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249-0670, USA
Interests: applied artificial intelligence; smart electric power systems and smart grids; machine learning in national security applications; artificial intelligence in smart cities; intelligent control methods in power systems; intelligent systems for signal processing and detection algorithms; machine learning and pattern recognition; nuclear security and nonproliferation; AI in radiation detection; AI in nuclear power applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Contemporary Information Systems and Technologies (ISTs) increasingly incorporate Artificial Intelligence (AI) technologies, enhancing their trustworthiness and robustness of autonomy and expanding their applicability across various new domains. These AI-empowered approaches prove to be very effective and include (1) intelligent systems in which expertise, knowledge, and AI are integrated; (2) support via tailored recommendations that provide personalized and group recommender systems; (3) dynamic support to address the complexities of limited and imbalanced data; (4) transparent and ethical support, ensuring explainability, privacy, data protection, trust, and responsibility; and (5) novel holistic software engineering development, verification and evaluation methodologies. By employing AI methodologies in knowledge-based software development, ISTs can effectively analyze vast datasets and extract valuable insights to support IST processes across diverse domains. Furthermore, this AI-driven enhancement extends the applicability of IST to novel domains as advancements in machine learning, deep learning, fuzzy logic  and paradigm fusion technologies enable contemporary IST to support various industries and sectors. To efficiently develop and use AI-empowered ISTs, further research is required in the following topics:

  1. Incorporating AI enhancements into ISTs;
  2. Developing AI tools for engineering autonomous ISTs;
  3. Software engineering for AI technologies;
  4. Explainable, trustworthy, and responsible AI-empowered ISTs, including addressing privacy and security issues;
  5. Innovative applications of AI-empowered ISTs in various fields, including the energy, environment, medical, financial sector, management, education, and engineering disciplines.

This proposed SI addresses some of the most significant recent advances in AI-empowered ISTs. It is aimed at professors, researchers, scientists, engineers, and students in all computer science, AI, and IST disciplines. It is also directed towards readers from other disciplines interested in becoming versed in some of the most recent AI technologies as they empower IST.

Prof. Dr. George A. Tsihrintzis
Prof. Dr. Maria Virvou
Prof. Dr. Hironori Washizaki
Prof. Dr. Gloria Phillips-Wren
Dr. Miltiadis (Miltos) Alamaniotis
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 250 words) can be sent to the Editorial Office for assessment.

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
  • fuzzy logic
  • information system
  • explainable artificial intelligence
  • trustworthy and responsible artificial intelligence
  • artificial intelligence-empowered system

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Published Papers (6 papers)

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Research

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20 pages, 10457 KB  
Article
Deep Learning-Based Side-Channel Attacks on Secure and Conventional Cryptographic Circuits Using FinFET and TFET Technologies
by Muyu Yang and Erdal Oruklu
Electronics 2026, 15(1), 18; https://doi.org/10.3390/electronics15010018 (registering DOI) - 20 Dec 2025
Abstract
Electronic devices are now ubiquitous across both professional and personal domains, often containing sensitive information that should remain undisclosed to untrustworthy third parties. Consequently, there is an increased demand for effective security measures to prevent the leakage of confidential data. While some devices [...] Read more.
Electronic devices are now ubiquitous across both professional and personal domains, often containing sensitive information that should remain undisclosed to untrustworthy third parties. Consequently, there is an increased demand for effective security measures to prevent the leakage of confidential data. While some devices utilize mathematically secure algorithms to safeguard sensitive information, there remains a vulnerability to informational leaks through Side-Channel Attacks (SCAs) targeting hardware platforms. Non-profiled SCAs, including Correlation Power Analysis (CPA), are particularly practical since they require access only to the target device. In this study, we propose and investigate the use of Deep Learning (DL) techniques to enhance the effectiveness of non-profiled SCAs through an optimized Deep Learning Power Analysis (DLPA) algorithm. Optimized DLPA attacks are implemented using Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) models, and are applied to the PRIDE SBox-4 block across conventional CMOS-style circuits and secure Sense Amplifier-Based Logic (SABL) Dual Precharge Logic (DPL) structure circuits. Both FinFET and TFET device technologies are evaluated. The experimental results show that the optimized DLPA approach consistently outperforms traditional CPA attacks. The optimized DLPA method succeeds even against TFET-based SABL-DPL circuits, which are resistant to conventional techniques. These findings demonstrate the increased threat posed by DL-based SCAs and highlight the need for evaluating hardware security against advanced machine learning-based methods. Full article
(This article belongs to the Special Issue Trends and Prospects in AI-Empowered Information Systems and Technologies)
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28 pages, 7627 KB  
Article
Explainable Optimization of Extreme Value Analysis for Photovoltaic Prediction: Introducing Dynamic Correlation Shifts and Weighted Benchmarking
by Dimitrios P. Panagoulias, Elissaios Sarmas, Vangelis Marinakis, Maria Virvou and George A. Tsihrintzis
Electronics 2025, 14(22), 4484; https://doi.org/10.3390/electronics14224484 - 17 Nov 2025
Viewed by 317
Abstract
We present an enhanced Extreme Value Analysis (EVA) framework designed to improve the forecasting of extremely low-production events in photovoltaic (PV) systems and to reveal the key inter-variable relationships governing performance under extreme conditions. The proposed Extreme Value Dynamic Benchmarking Method (EVDBM) extends [...] Read more.
We present an enhanced Extreme Value Analysis (EVA) framework designed to improve the forecasting of extremely low-production events in photovoltaic (PV) systems and to reveal the key inter-variable relationships governing performance under extreme conditions. The proposed Extreme Value Dynamic Benchmarking Method (EVDBM) extends classical EVA by integrating the Dynamic Identification of Significant Correlation (DISC)-thresholding algorithm and explainable AI (XAI) mechanisms, enabling dynamic identification and quantification of correlation shifts during extreme scenarios. Through a combination of grid and Bayesian optimization, EVDBM adaptively fine-tunes variable weights to improve fit, interpretability, and benchmarking consistency. By transforming return values predicted via EVA into dynamic benchmarking scores, EVDBM evolves static tail modeling into a data-driven, explainable benchmarking system capable of identifying critical vulnerabilities and resilience patterns in real time. Applied to real PV production datasets, EVDBM achieved an average improvement of 13.2% in correlation-based Rcorr2 and demonstrated statistically significant reductions in residual error (pt<0.01) in the João dataset, confirming its robustness and generalizability. Quantile-to-quantile analyses further showed improved alignment between modeled and empirical extremes, validating the method’s stability across distributional tails. Ablation studies revealed cumulative gains in interpretability and predictive stability in the EVA → EVDBM → EVDBM + XAI progression, while computational complexity remained near-linear with respect to input dimensionality. Overall, EVDBM delivers a transparent, statistically validated, and operationally interpretable framework for extreme event modeling. Its explainable benchmarking structure supports actionable insights for risk management, infrastructure resilience, and strategic energy planning, establishing EVDBM as a generalizable approach for understanding and managing extremes across diverse application domains. Full article
(This article belongs to the Special Issue Trends and Prospects in AI-Empowered Information Systems and Technologies)
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29 pages, 549 KB  
Article
Catch Me If You Can: Rogue AI Detection and Correction at Scale
by Fatemeh Stodt, Jan Stodt, Mohammed Alshawki, Javad Salimi Sratakhti and Christoph Reich
Electronics 2025, 14(20), 4122; https://doi.org/10.3390/electronics14204122 - 21 Oct 2025
Viewed by 1032
Abstract
Modern AI systems can strategically misreport information when incentives diverge from truthfulness, posing risks for oversight and deployment. Prior studies often examine this behavior within a single paradigm; systematic, cross-architecture evidence under a unified protocol has been limited. We introduce the Strategy Elicitation [...] Read more.
Modern AI systems can strategically misreport information when incentives diverge from truthfulness, posing risks for oversight and deployment. Prior studies often examine this behavior within a single paradigm; systematic, cross-architecture evidence under a unified protocol has been limited. We introduce the Strategy Elicitation Battery (SEB), a standardized probe suite for measuring deceptive reporting across large language models (LLMs), reinforcement-learning agents, vision-only classifiers, multimodal encoders, state-space models, and diffusion models. SEB uses Bayesian inference tasks with persona-controlled instructions, schema-constrained outputs, deterministic decoding where supported, and a probe mix (near-threshold, repeats, neutralized, cross-checks). Estimates use clustered bootstrap intervals, and significance is assessed with a logistic regression by architecture; a mixed-effects analysis is planned once the per-round agent/episode traces are exported. On the latest pre-correction runs, SEB shows a consistent cross-architecture pattern in deception rates: ViT 80.0%, CLIP 15.0%, Mamba 10.0%, RL agents 10.0%, Stable Diffusion 10.0%, and LLMs 5.0% (20 scenarios/architecture). A logistic regression on per-scenario flags finds a significant overall architecture effect (likelihood-ratio test vs. intercept-only: χ2(5)=41.56, p=7.22×10−8). Holm-adjusted contrasts indicate ViT is significantly higher than all other architectures in this snapshot; the remaining pairs are not significant. Post-correction acceptance decisions are evaluated separately using residual deception and override rates under SEB-Correct. Latency varies by architecture (sub-second to minutes), enabling pre-deployment screening broadly and real-time auditing for low-latency classes. Results indicate that SEB-Detect deception flags are not confined to any one paradigm, that distinct architectures can converge to similar levels under a common interface, and that reporting interfaces and incentive framing are central levers for mitigation. We operationalize “deception” as reward-sensitive misreport flags, and we separate detection from intervention via a correction wrapper (SEB-Correct), supporting principled acceptance decisions for deployment. Full article
(This article belongs to the Special Issue Trends and Prospects in AI-Empowered Information Systems and Technologies)
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20 pages, 1840 KB  
Article
A Hybrid Long Short-Term Memory with a Sentiment Analysis System for Stock Market Forecasting
by Konstantinos Liagkouras and Konstantinos Metaxiotis
Electronics 2025, 14(14), 2753; https://doi.org/10.3390/electronics14142753 - 8 Jul 2025
Cited by 1 | Viewed by 3528
Abstract
Addressing the stock market forecasting as a classification problem, where the model predicts the direction of stock price movement, is crucial for both traders and investors, as it can help them to allocate limited resources to the most promising investment opportunities. In this [...] Read more.
Addressing the stock market forecasting as a classification problem, where the model predicts the direction of stock price movement, is crucial for both traders and investors, as it can help them to allocate limited resources to the most promising investment opportunities. In this study, we propose a hybrid system that uses a Long Short-Term Memory (LSTM) network and sentiment analysis for predicting the direction of the movement of the stock price. The proposed hybrid system is fed with historical stock data and regulatory news announcements for producing more reliable responses. LSTM networks are well suited to handling time series data with long-term dependencies, while the sentiment analyser provides insights into how news impacts stock price movements by classifying business news into classes. By integrating both the LSTM network and the sentiment classifier, the proposed hybrid system delivers more accurate forecasts. Our experiments demonstrate that the proposed hybrid system outperforms other competing configurations. Full article
(This article belongs to the Special Issue Trends and Prospects in AI-Empowered Information Systems and Technologies)
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23 pages, 2964 KB  
Article
FuturesNet: Capturing Patterns of Price Fluctuations in Domestic Futures Trading
by Qingyi Pan, Suyu Sun, Pei Yang and Jingyi Zhang
Electronics 2024, 13(22), 4482; https://doi.org/10.3390/electronics13224482 - 15 Nov 2024
Cited by 1 | Viewed by 1906
Abstract
Futures trading analysis plays a pivotal role in the development of macroeconomic policies and corporate strategy planning. High-frequency futures data, typically presented as time series, contain valuable historical patterns. To address challenges such as non-stationary in modeling futures prices, we propose a novel [...] Read more.
Futures trading analysis plays a pivotal role in the development of macroeconomic policies and corporate strategy planning. High-frequency futures data, typically presented as time series, contain valuable historical patterns. To address challenges such as non-stationary in modeling futures prices, we propose a novel architecture called FuturesNet, which uses an InceptionTime module to capture the short-term fluctuations between ask and bid orders, as well as a long-short-term-memory (LSTM) module with skip connections to capture long-term temporal dependencies. We evaluated the performance of FuturesNet using datasets numbered 50, 300, and 500 from the domestic financial market. The comprehensive experimental results show that FuturesNet outperforms other competitive baselines in most settings. Additionally, we conducted ablation studies to interpret the behaviors of FuturesNet. Our code and collected futures datasets are released. Full article
(This article belongs to the Special Issue Trends and Prospects in AI-Empowered Information Systems and Technologies)
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Review

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39 pages, 413 KB  
Review
Federated Learning: Navigating the Landscape of Collaborative Intelligence
by Konstantinos Lazaros, Dimitrios E. Koumadorakis, Aristidis G. Vrahatis and Sotiris Kotsiantis
Electronics 2024, 13(23), 4744; https://doi.org/10.3390/electronics13234744 - 30 Nov 2024
Cited by 31 | Viewed by 10149
Abstract
As data become increasingly abundant and diverse, their potential to fuel machine learning models is increasingly vast. However, traditional centralized learning approaches, which require aggregating data into a single location, face significant challenges. Privacy concerns, stringent data protection regulations like GDPR, and the [...] Read more.
As data become increasingly abundant and diverse, their potential to fuel machine learning models is increasingly vast. However, traditional centralized learning approaches, which require aggregating data into a single location, face significant challenges. Privacy concerns, stringent data protection regulations like GDPR, and the high cost of data transmission hinder the feasibility of centralizing sensitive data from disparate sources such as hospitals, financial institutions, and personal devices. Federated Learning addresses these issues by enabling collaborative model training without requiring raw data to leave its origin. This decentralized approach ensures data privacy, reduces transmission costs, and allows organizations to harness the collective intelligence of distributed data while maintaining compliance with ethical and legal standards. This review delves into FL’s current applications and its potential to reshape IoT systems into more collaborative, privacy-centric, and flexible frameworks, aiming to enlighten and motivate those navigating the confluence of machine learning and IoT advancements. Full article
(This article belongs to the Special Issue Trends and Prospects in AI-Empowered Information Systems and Technologies)
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