Artificial Intelligence and Smart Information Systems: Trends and Innovations

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 6855

Special Issue Editors


E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Interests: AI; control systems; robotics; chaos; machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Center for Language Research, School of Computer Science and Engineering, University of Aizu, Fukushima 965-0006, Japan
Interests: AI, data visualization, technical communication, usability, systems design

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the latest trends and innovations in the application of artificial intelligence (AI) in information systems emerging from the 7th ETLTC International Conference on ICT Integration in Technical Education and the 4th International Conference on Entertainment Technology and Management (ICETM2025, https://www.ent-tech-management.org/etltc2025) that will be held on January 21–26, 2025, at the University of Aizu, Aizuwakamatsu, Japan. The best articles from the entire ETLTC2025 conference series will also be considered.

We aim to showcase cutting-edge research emerging from the intersection of AI and information systems, highlighting advancements, challenges, and future directions in this rapidly evolving field. We invite contributions that explore theoretical, empirical, and practical aspects of AI in information systems, including, but not limited to, the following topics:

  • AI in data management and big data analytics;
  • Machine learning applications in information retrieval;
  • AI-driven decision support systems;
  • AI and entertainment technology and management
  • Natural language processing and understanding in information systems;
  • AI in cybersecurity and privacy protection;
  • Intelligent agents and multi-agent systems;
  • AI in business process management and automation;
  • AI for enhancing user experience and human–computer interactions;
  • Ethical and societal implications of AI in information systems;
  • AI in cloud computing and distributed information systems;
  • AI for IoT and smart environments;
  • AI for legal frameworks;
  • AI in healthcare information systems;
  • AI in educational technologies and e-learning;
  • AI-driven innovation and entrepreneurship in information systems;
  • Technology-assisted language learning and educational technologies;
  • AI for data analysis, big data, the cloud, and robotics;
  • Technology and smart environments;
  • Health informatics;
  • Technical communication and knowledge management.

We will also welcome original research and review papers that are not part of the conference but align with the above-mentioned topics. We look forward to receiving your contributions and advancing the understanding and implementation of AI in information systems.

Prof. Dr. George F. Fragulis
Prof. Dr. Debopriyo Roy
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. Technologies 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
  • machine learning
  • big data analytics
  • natural language processing
  • cybersecurity
  • smart environments
  • legal frameworks and governance
  • health informatics
  • educational technologies
  • technical communication
  • business process management

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

21 pages, 655 KiB  
Article
Generative Models for Source Code: Fine-Tuning Techniques for Structured Pattern Learning
by Valentina Franzoni, Silvia Tagliente and Alfredo Milani
Technologies 2024, 12(11), 219; https://doi.org/10.3390/technologies12110219 - 4 Nov 2024
Viewed by 1505
Abstract
This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding [...] Read more.
This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding standards, essential for integration into existing application development projects and long-term software maintenance. By training the model on specific code structures, including a dataset with Italian annotations, the proposed methodology ensures that the generated code is compliant with both the functional requirements and the pre-defined coding standards. The methodology proposed in this study applies transfer learning techniques on the DeepSeek Coder model, to refine pre-trained models to generate code that integrates additional structuring constraints. By training the model on specific code structures, including a dataset with Italian comments, the proposed methodology ensures that the generated code meets both functional requirements and coding structure. Experimental results, evaluated using the perplexity metric, demonstrate the effectiveness of the proposed approach, which impacts the goals of reducing errors, and ultimately improves software development quality. Full article
Show Figures

Graphical abstract

Review

Jump to: Research

19 pages, 3048 KiB  
Review
Integrating Building Information Modelling and Artificial Intelligence in Construction Projects: A Review of Challenges and Mitigation Strategies
by Ayaz Ahmad Khan, Abdulkabir Opeyemi Bello, Mohammad Arqam and Fahim Ullah
Technologies 2024, 12(10), 185; https://doi.org/10.3390/technologies12100185 - 2 Oct 2024
Viewed by 4403
Abstract
Artificial intelligence (AI), including machine learning and decision support systems, can deploy complex algorithms to learn sufficiently from the large corpus of building information modelling (BIM) data. An integrated BIM-AI system can leverage the insights to make smart and informed decisions. Hence, the [...] Read more.
Artificial intelligence (AI), including machine learning and decision support systems, can deploy complex algorithms to learn sufficiently from the large corpus of building information modelling (BIM) data. An integrated BIM-AI system can leverage the insights to make smart and informed decisions. Hence, the integration of BIM-AI offers vast opportunities to extend the possibilities of innovations in the design and construction of projects. However, this synergy suffers unprecedented challenges. This study conducted a systematic literature review of the challenges and constraints to BIM-AI integration in the construction industry and categorise them into different taxonomies. It used 64 articles, retrieved from the Scopus database using the PRISMA protocol, that were published between 2015 and July 2024. The findings revealed thirty-nine (39) challenges clustered into six taxonomies: technical, knowledge, data, organisational, managerial, and financial. The mean index score analysis revealed financial (µ = 30.50) challenges are the most significant, followed by organisational (µ = 23.86), and technical (µ = 22.29) challenges. Using Pareto analysis, the study highlighted the twenty (20) most important BIM-AI integration challenges. The study further developed strategic mitigation maps containing strategies and targeted interventions to address the identified challenges to the BIM-AI integration. The findings provide insights into the competing issues stifling BIM-AI integration in construction and provide targeted interventions to improve synergy. Full article
Show Figures

Figure 1

Back to TopTop