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Application of Data Science, Artificial Intelligence, and Blockchain for Smart Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 1019

Special Issue Editors


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Guest Editor
Faculty of Logistics, Molde University College, Specialized University in Logistics, P.O. Box 2110, NO-6402 Molde, Norway
Interests: blockchain; ERP; information systems; Internet of Things; supply chain management; reliable systems

Special Issue Information

Dear Colleagues,

In recent years, the integration of data science, artificial intelligence (AI), and blockchain in smart systems has gained significant momentum, transforming industries such as healthcare, transportation, finance, manufacturing, and urban infrastructure. AI-driven smart systems enhance automation, efficiency, and adaptability, while blockchain ensures data integrity, security, and decentralized decision-making. The convergence of these technologies fosters new opportunities for trust-driven, autonomous, and intelligent environments. 

We are pleased to invite you to contribute to this Special Issue, which focuses on the latest research, developments, and applications of data science, AI, and blockchain in smart systems. The aim is to explore innovative methodologies, theoretical advancements, and real-world implementations that harness the power of these technologies to improve automation, security, transparency, and system intelligence.

Suggested Themes and Article Types for Submissions:

In this Special Issue, we welcome original research articles and review papers covering, but not limited to, the following areas:

  • AI-driven Smart Systems and Applications;
  • Intelligent automation in smart cities, healthcare, logistics, and Industry 4.0;
  • AI-powered predictive maintenance and fault detection;
  • Smart energy management and optimization;
  • Data Science Techniques for Smart Systems.
  • Machine learning and deep learning models for smart applications;
  • Big data analytics and real-time data processing;
  • Explainable AI (XAI) for transparent decision-making;
  • Blockchain and distributed ledger technologies (DLT) for smart systems.
  • Blockchain-based security and privacy in AI-driven applications;
  • Smart contracts for automation in logistics, finance, and healthcare;
  • Decentralized identity management and access control;
  • Integration of AI and Blockchain for trust-based smart systems.
  • Edge and Cloud Computing for AI and Blockchain-enabled Smart Systems;
  • AI at the edge: federated learning and on-device intelligence;
  • Blockchain-enabled IoT for secure and scalable smart environments;
  • Scalable architectures for cloud-based AI and blockchain applications.
  • Human-Centric AI, Ethical Considerations, and Governance;
  • Fairness, bias, and ethical implications in AI-driven smart systems;
  • Human-AI collaboration and adaptive interfaces;
  • Blockchain governance models for decentralized smart systems;
  • Regulatory and policy frameworks for responsible AI and blockchain adoption.

This Special Issue aims to showcase state-of-the-art research and practical applications that leverage AI, data science, and blockchain to enhance smart systems across various domains. We encourage contributions from academia, industry, and interdisciplinary research groups to foster discussions on emerging trends and future directions in this evolving field.

We look forward to your submissions!

Prof. Dr. Joao C Ferreira
Prof. Dr. Bjørn Jæger
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. Applied Sciences 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 (AI)
  • data science
  • smart systems
  • federated learning
  • blockchain
  • explainable AI (XAI)
  • decision-making systems
  • AI security and privacy
  • AI-driven healthcare
  • smart energy management
  • AI for logistics and transportation

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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

21 pages, 9886 KiB  
Article
A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
by Marcin Matczuk, Grzegorz Kozieł and Sławomir Cięszczyk
Appl. Sci. 2025, 15(13), 7246; https://doi.org/10.3390/app15137246 - 27 Jun 2025
Viewed by 231
Abstract
The utilisation of 3D models in low-cost devices, such as the internet of things, virtual reality, and augmented reality, is expanding. The challenge lies in the lack of lightweight solutions for verifying the authenticity of models in the graphics library transmission format (glTF) [...] Read more.
The utilisation of 3D models in low-cost devices, such as the internet of things, virtual reality, and augmented reality, is expanding. The challenge lies in the lack of lightweight solutions for verifying the authenticity of models in the graphics library transmission format (glTF) on devices with limited resources. The glTF standard, which allows storage in glb format, is the leading standard for representing 3D assets. Despite its popularity, research on watermarking glTF models remains limited. This paper proposes a novel method for authenticating 3D models in glb format based on fragile watermarking. Additionally, an analysis was conducted to determine the impact of embedding the watermark in vertex attributes other than position on the integrity and visual quality of the model. The methodology is as follows: (1) embedding the watermark, (2) applying model modification or omitting it, and (3) verifying authenticity based on the recovered watermark. The proposed algorithm attaches a 512-bit hash-based message authentication code (HMAC) to a 3D model using the least significant bits (LSBs) modification method. The use of HMAC and LSBs has resulted in a computationally efficient algorithm that can be implemented in low-cost devices. Full article
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28 pages, 3098 KiB  
Article
Proactive Complaint Management in Public Sector Informatics Using AI: A Semantic Pattern Recognition Framework
by Marco Esperança, Diogo Freitas, Pedro V. Paixão, Tomás A. Marcos, Rafael A. Martins and João C. Ferreira
Appl. Sci. 2025, 15(12), 6673; https://doi.org/10.3390/app15126673 - 13 Jun 2025
Viewed by 552
Abstract
The digital transformation of public services has led to a surge in the volume and complexity of informatics-related complaints, often marked by ambiguous language, inconsistent terminology, and fragmented reporting. Conventional keyword-based approaches are inadequate for detecting semantically similar issues expressed in diverse ways. [...] Read more.
The digital transformation of public services has led to a surge in the volume and complexity of informatics-related complaints, often marked by ambiguous language, inconsistent terminology, and fragmented reporting. Conventional keyword-based approaches are inadequate for detecting semantically similar issues expressed in diverse ways. This study proposes an AI-powered framework that employs BERT-based sentence embeddings, semantic clustering, and classification algorithms, structured under the CRISP-DM methodology, to standardize and automate complaint analysis. Leveraging real-world interaction logs from a public sector agency, the system harmonizes heterogeneous complaint narratives, uncovers latent issue patterns, and enables early detection of technical and usability problems. The approach is deployed through a real-time dashboard, transforming complaint handling from a reactive to a proactive process. Experimental results show a 27% reduction in repeated complaint categories and a 32% increase in classification efficiency. The study also addresses ethical concerns, including data governance, bias mitigation, and model transparency. This work advances citizen-centric service delivery by demonstrating the scalable application of AI in public sector informatics. Full article
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