Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Artificial Intelligence on the Edge for Industry 4.0
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence on the Edge for Industry 4.0

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4807

Special Issue Editors

Prof. Dr. Chrysostomos Stylios

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Ioannina, GR-47100 Arta, Greece
Interests: Industry 4.0; artificial intelligence computational intelligence, fuzzy cognitive maps; fuzzy logic, neural networks; support vector machines; knowledge-based systems; modeling complex systems; intelligent systems; medical decision support systems; biosignal processing and analysis; hierarchical systems and supervisory control; intelligent manufacturing systems
Special Issues, Collections and Topics in MDPI journals
Dr. Dorota Stadnicka

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, 35-959 Rzeszów, Poland
Interests: production engineering; lean production; intelligent manufacturing systems; human–robot collaboration; sustainable development; human-centric manufacturing systems.
Special Issues, Collections and Topics in MDPI journals
Dr. Joan Navarro

E-Mail Website
Guest Editor
Engineering Department of the La Salle, Universitat Ramon Llull, 08022 Barcelona, Spain
Interests: big data; cloud computing; edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Daniele Mazzei

E-Mail Website
Guest Editor
Computer Science Department, University of Pisa, 56126 Pisa, PI, Italy
Interests: industrial IoT; embedded programming; industrial innovation; AI on the edge for industrial applications; human–machine and human–robot interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue is based on the conference "Industry 4.0 and Beyond - The Final Conference of the PLANET4 Project", which is planned for the 2nd October in Pisa, Italy, as the final event of the PLANET4 project. It will provide an excellent international forum for disseminating original research results, new ideas and practical development experiences concentrating on both the theory and practices of academics, researchers, engineers and industry professionals. It aims to bring together researchers and scientists from academia, industry and research centers to present the results of ongoing research, exchange ideas and identify future research directions in the following areas: Industry 4.0; data science, artificial intelligence and machine learning; internet of things and industrial internet of things; and cloud and edge computing.

Prof. Dr. Chrysostomos Stylios
Prof. Dr. Dorota Stadnicka
Dr. Joan Navarro
Dr. Daniele Mazzei
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

  • Industry 4.0 and beyond
  • smart manufacturing and automation
  • digital twins
  • augmented and virtual reality
  • cybersecurity in Industry 4.0
  • blockchain technology
  • sustainable manufacturing
  • human-machine collaboration
  • additive manufacturing
  • Industry 4.0 and the digital transformation of manufacturing
  • collaborative robots (cobots)
  • education, innovation and change management in the industrial sector
  • data science
  • artificial intelligence and machine learning
  • deep learning
  • explainable AI and trustworthiness
  • computer vision
  • supply chain optimization
  • quality control and defect detection
  • energy management and optimization
  • robotics and automation
  • natural language processing for customer service
  • fraud detection and prevention
  • cybersecurity and threat detection
  • big data analytics for industry
  • optimization of industrial processes
  • precision agriculture
  • predictive maintenance Internet of Things and Industrial Internet of Things
  • smart cities and IoT
  • industrial automation and the IIoT
  • IoT-enabled smart homes
  • IoT and environmental monitoring
  • IoT and supply chain management
  • IoT and cybersecurity in industrial environments
  • IoT analytics and machine learning
  • 5G and the IoT
  • IoT and IIoT connectivity technologies and standards
  • IoT and IIoT interoperability and integration with legacy systems and protocols
  • cloud and edge computing
  • edge computing use cases and applications across different industries
  • cloud and edge security challenges and solutions
  • distributed systems and data management in the cloud and at the edge
  • edge computing hardware and software platforms and their integration with the cloud
  • cloud and edge performance and scalability challenges and solutions
  • edge computing architectures and their integration with cloud providers
  • edge computing and cloud computing interoperability and integration
  • edge computing and cloud computing for real-time and mission-critical applications
  • fog computing and its relationship to cloud and edge computing
  • edge computing for mobile and wireless networks

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 1160 KB  
Article
Near Real-Time Ethereum Fraud Detection Using Explainable AI in Blockchain Networks
by Fatih Ertam
Appl. Sci. 2025, 15(19), 10841; https://doi.org/10.3390/app151910841 - 9 Oct 2025
Viewed by 515
Abstract
Blockchain technologies have profoundly transformed information systems by providing decentralized infrastructures that enhance transparency, security, and traceability. Ethereum, in particular, supports smart contracts and facilitates the development of decentralized finance (DeFi), non-fungible tokens (NFTs), and Web3 applications. However, its openness also enables illicit [...] Read more.
Blockchain technologies have profoundly transformed information systems by providing decentralized infrastructures that enhance transparency, security, and traceability. Ethereum, in particular, supports smart contracts and facilitates the development of decentralized finance (DeFi), non-fungible tokens (NFTs), and Web3 applications. However, its openness also enables illicit activities, including fraud and money laundering, through anonymous wallets. Identifying wallets involved in large transfers or abnormal transactional patterns is therefore critical to ecosystem security. This study proposes an AI-based framework employing XGBoost, LightGBM, and CatBoost to detect suspicious Ethereum wallets, achieving test accuracies between 95.83% and 96.46%. The system provides near real-time predictions for individual or recent wallet addresses using a pre-trained XGBoost model. To improve interpretability, SHAP (SHapley Additive exPlanations) visualizations are integrated, highlighting the contribution of each feature. The results demonstrate the effectiveness of AI-driven methods in monitoring and securing Ethereum transactions against fraudulent activities. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge for Industry 4.0)
►▼ Show Figures

Figure 1

26 pages, 3434 KB  
Article
Defect Detection in Source Code via Multimodal Feature Fusion
by Shuchu Xiong, Lu Yin, Haozhan Gu and Chengquan Zhang
Appl. Sci. 2025, 15(17), 9358; https://doi.org/10.3390/app15179358 - 26 Aug 2025
Viewed by 788
Abstract
To address the limitation of existing static defect detection methods in capturing code semantics and structural relationships—which leads to incomplete feature representation—we propose a multimodal feature fusion approach for source code defect detection. First, semantic features are extracted from code character sequences while [...] Read more.
To address the limitation of existing static defect detection methods in capturing code semantics and structural relationships—which leads to incomplete feature representation—we propose a multimodal feature fusion approach for source code defect detection. First, semantic features are extracted from code character sequences while structural features are derived from Abstract Syntax Trees (ASTs). Second, a structural attention mechanism dynamically models interdependencies between these two modalities and fuses them into comprehensive representation vectors. Finally, defect detection is performed based on the integrated representations. Experimental results on the Sard dataset demonstrate: Compared to baseline methods using single representations (semantic or structural), our approach improves F1-score by 1.96% to 11.76%. Against other feature fusion methods, it achieves 1.36% to 1.66% higher F1-score. The method demonstrates good stability when dealing with imbalanced defect category data. By effectively fusing multimodal code information, this approach significantly enhances the accuracy and adaptability of code defect detection in open-source environments. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge for Industry 4.0)
►▼ Show Figures

Figure 1

22 pages, 1281 KB  
Article
SCRAM: A Scenario-Based Framework for Evaluating Regulatory and Fairness Risks in AI Surveillance Systems
by Kadir Kesgin, Selahattin Kosunalp and Ivan Beloev
Appl. Sci. 2025, 15(16), 9038; https://doi.org/10.3390/app15169038 - 15 Aug 2025
Viewed by 644
Abstract
As artificial intelligence systems increasingly govern public safety operations, concerns over algorithmic fairness and legal compliance intensify. This study introduces a scenario-based evaluation framework (SCRAM) that simultaneously measures regulatory conformity and bias risks in AI-enabled surveillance. Using license plate recognition (LPR) systems in [...] Read more.
As artificial intelligence systems increasingly govern public safety operations, concerns over algorithmic fairness and legal compliance intensify. This study introduces a scenario-based evaluation framework (SCRAM) that simultaneously measures regulatory conformity and bias risks in AI-enabled surveillance. Using license plate recognition (LPR) systems in Türkiye as a case study, we simulate multiple operational configurations that vary decision thresholds and data retention periods. Each configuration is assessed through fairness metrics (SPD, DIR) and a compliance score derived from KVKK (Türkiye’s Personal Data Protection Law) and constitutional jurisprudence. Our findings show that technical performance does not guarantee normative acceptability: several configurations with high detection accuracy fail to meet legal and fairness thresholds. The SCRAM model offers a modular and adaptable approach to align AI deployments with ethical and legal standards and highlights how policy-sensitive parameters critically shape risk landscapes. We conclude with implications for real-time audit systems and cross-jurisdictional AI governance. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge for Industry 4.0)
►▼ Show Figures

Figure 1

60 pages, 5139 KB  
Article
Implementing Sensible Algorithmic Decisions in Manufacturing
by Luis Asunción Pérez-Domínguez, Dynhora-Danheyda Ramírez-Ochoa, David Luviano-Cruz, Erwin-Adán Martínez-Gómez, Vicente García-Jiménez and Diana Ortiz-Muñoz
Appl. Sci. 2025, 15(16), 8885; https://doi.org/10.3390/app15168885 - 12 Aug 2025
Viewed by 935
Abstract
A significant component of making intelligent decisions is optimizing algorithms. In this context, it is imperative to develop algorithms that are more efficient in order to efficiently and accurately process large quantities of intricate data. In addition, the main contribution of this study [...] Read more.
A significant component of making intelligent decisions is optimizing algorithms. In this context, it is imperative to develop algorithms that are more efficient in order to efficiently and accurately process large quantities of intricate data. In addition, the main contribution of this study lies in the integration of optimization theory with swarm intelligence through multicriteria decision-making methods (MCDMs). This study indicates that combining dimensional analysis (DA) with particle swarm optimization (PSO) can smartly and efficiently improve analysis and decision making, resolving PSO’s shortcomings. A convergence investigation between the bat algorithm (BA), MOORA-PSO, TOPSIS-PSO, DA-PSO, and PSO is carried out to substantiate this assertion. Additionally, the ANOVA method is used to validate data dependability in order to evaluate the algorithms’ correctness. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge for Industry 4.0)
►▼ Show Figures

Figure 1

15 pages, 1241 KB  
Article
Triplet Spatial Reconstruction Attention-Based Lightweight Ship Component Detection for Intelligent Manufacturing
by Bocheng Feng, Zhenqiu Yao and Chuanpu Feng
Appl. Sci. 2025, 15(15), 8676; https://doi.org/10.3390/app15158676 - 5 Aug 2025
Viewed by 410
Abstract
Automatic component recognition plays a crucial role in intelligent ship manufacturing, but existing methods suffer from low recognition accuracy and high computational cost in industrial scenarios involving small samples, component stacking, and diverse categories. To address the requirements of shipbuilding industrial applications, a [...] Read more.
Automatic component recognition plays a crucial role in intelligent ship manufacturing, but existing methods suffer from low recognition accuracy and high computational cost in industrial scenarios involving small samples, component stacking, and diverse categories. To address the requirements of shipbuilding industrial applications, a Triplet Spatial Reconstruction Attention (TSA) mechanism that combines threshold-based feature separation with triplet parallel processing is proposed, and a lightweight You Only Look Once Ship (YOLO-Ship) detection network is constructed. Unlike existing attention mechanisms that focus on either spatial reconstruction or channel attention independently, the proposed TSA integrates triplet parallel processing with spatial feature separation–reconstruction techniques to achieve enhanced target feature representation while significantly reducing parameter count and computational overhead. Experimental validation on a small-scale actual ship component dataset demonstrates that the improved network achieves 88.7% mean Average Precision (mAP), 84.2% precision, and 87.1% recall, representing improvements of 3.5%, 2.2%, and 3.8%, respectively, compared to the original YOLOv8n algorithm, requiring only 2.6 M parameters and 7.5 Giga Floating-point Operations per Second (GFLOPs) computational cost, achieving a good balance between detection accuracy and lightweight model design. Future research directions include developing adaptive threshold learning mechanisms for varying industrial conditions and integration with surface defect detection capabilities to enhance comprehensive quality control in intelligent manufacturing systems. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge for Industry 4.0)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop