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Advancing Information Systems Through Artificial Intelligence: Innovative Approaches and Applications
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Advancing Information Systems Through Artificial Intelligence: Innovative Approaches and Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 11167

Special Issue Editors

Dr. Konstantinos Giotopoulos

E-Mail Website
Guest Editor
Department of Management Science and Technology, University of Patras, Patras, Greece
Interests: information systems; artificial intelligence; information management; computational intelligence; digital transformation
Prof. Dr. Spyros Sioutas

E-Mail Website
Guest Editor
Department of Computer Engineering and Informatics, University of Patras, 26504 Rio Achaia, Greece
Interests: multidimensional data structures; decentralized systems for big data management; indexing; query processing and query optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The MDPI journal Information invites submissions to a Special Issue on “Advancing Information Systems Through Artificial Intelligence: Innovative Approaches and Applications”.

In continuous rapid technological evolution, artificial intelligence (AI) has been integrated into the development and advancement of information systems (ISs). The objective of this Special Issue is to explore the synergies between AI and ISs, with a special emphasis on how AI-driven innovations become the transformative factors of basic aspects of ISs, such as information management, decision making, and overall IS functionality.

AI techniques and methodologies such as machine learning, large language models, neuro-fuzzy systems, and intelligent data analysis have been the vaulting horses of the radical changes in data management, data interpretation, and data utilization. The integration of AI into ISs enables advanced data processing, predictive analytics, and efficient system interoperability, leading to smarter and more responsive systems.

This Special Issue seeks novel research contributions showcasing the utilization of AI as a transformative factor of ISs, demonstrating important insights and boosting innovation in the AI–IS blending field. Towards this direction, we invite the research community to contribute original research, case studies, and reviews that highlight the impact of AI on ISs.

Topics of interest include, but are not limited to, the following:

  • AI-driven data management.
  • AI-driven decision support systems.
  • Applications of neuro-fuzzy systems in ISs.
  • Intelligent data analysis and knowledge extraction.
  • Utilization of AI in e-governance and enterprise systems.
  • AI-driven information retrieval and processing.
  • Case studies on the practical implementation of AI in ISs.

Dr. Konstantinos Giotopoulos
Prof. Dr. Spyros Sioutas
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. Information is an international peer-reviewed open access monthly 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 1600 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
  • information systems
  • data management
  • decision support systems
  • neuro-fuzzy systems
  • intelligent data analysis
  • e-governance
  • enterprise systems

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

Published Papers (2 papers)

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Research

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19 pages, 361 KiB  
Article
Neural Network-Based Parameter Estimation in Dynamical Systems
by Dimitris Kastoris, Kostas Giotopoulos and Dimitris Papadopoulos
Information 2024, 15(12), 809; https://doi.org/10.3390/info15120809 - 16 Dec 2024
Cited by 2 | Viewed by 1439
Abstract
Mathematical models are designed to assist decision-making processes across various scientific fields. These models typically contain numerous parameters, the values’ estimation of which often comes under analysis when evaluating the strength of these models as management tools. Advanced artificial intelligence software has proven [...] Read more.
Mathematical models are designed to assist decision-making processes across various scientific fields. These models typically contain numerous parameters, the values’ estimation of which often comes under analysis when evaluating the strength of these models as management tools. Advanced artificial intelligence software has proven to be highly effective in estimating these parameters. In this research work, we use the Lotka–Volterra model to describe the dynamics of a telecommunication sector in Greece, and then we propose a methodology that employs a feed-forward neural network (NN). The NN is used to estimate the parameter’s values of the Lotka–Volterra system, which are later applied to solve the system using a fourth-algebraic-order Runge–Kutta method. The application of the proposed architecture to the specific case study reveals that the model fits well to the experiential data. Furthermore, the results of our method surpassed the other three methods used for comparison, demonstrating its higher accuracy and effectiveness. The implementation of the proposed feed-forward neural network and the fourth-algebraic-order Runge–Kutta method was accomplished using MATLAB. Full article
(This article belongs to the Special Issue Advancing Information Systems Through Artificial Intelligence: Innovative Approaches and Applications)
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Review

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42 pages, 2381 KiB  
Review
AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors
by Attila Kovari
Information 2024, 15(11), 725; https://doi.org/10.3390/info15110725 - 11 Nov 2024
Cited by 3 | Viewed by 8718
Abstract
This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, [...] Read more.
This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards. Full article
(This article belongs to the Special Issue Advancing Information Systems Through Artificial Intelligence: Innovative Approaches and Applications)
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