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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: closed (20 April 2025) | Viewed by 4743

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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hironori Washizaki

E-Mail Website
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

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

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Published Papers (2 papers)

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Research

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23 pages, 2964 KiB  
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
Viewed by 965
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 KiB  
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 3 | Viewed by 3610
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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