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Information Industry and Intelligence Innovation
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Information Industry and Intelligence Innovation

A special issue of Applied System Innovation (ISSN 2571-5577). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 May 2026 | Viewed by 4055

Special Issue Editors

Prof. Dr. Shih-Pang Tseng

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Guest Editor
Department of Computer Science and Engineering, Tajen University, Yanpu Township, Pingtung County 907101, Taiwan
Interests: artificial intelligence; Internet of Things; learning technology
Prof. Dr. Jhing-Fa Wang

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Guest Editor
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan
Interests: artificial intelligence; VLSI design and application

Special Issue Information

Dear Colleagues,

As everyone knows, the information industry is entering a new era driven by digitalization, intellectualization, and innovation for its transformation. Emerging information technologies, including artificial intelligence, big data processing, cloud computing, blockchain, etc., are reshaping the structure of all industries and giving rise to new business models. In this context, “intelligent innovation”—a combination of technological breakthroughs, industrial applications, and human creativity—is a significant approach to promoting sustainable economic growth and enhance industrial competitiveness.

This Special Issue aims to provide an open platform for scholars, practitioners, and policymakers to exchange ideas, share research findings, and explore cutting-edge innovations at the intersection of the information industry and intelligent technologies. We encourage authors to submit original research articles that include crucial innovative concepts, methodologies, applications, trends, and knowledge in terms of intelligent innovation. In addition, review articles that present the current state of the art are also warmly welcomed.

The potential topics include, but are not limited to, the following:

  • Digital Transformation of Industries;
  • AI-Driven Innovation;
  • Big Data and Knowledge Intelligence;
  • Industry Internet;
  • Information and Intelligence;
  • Artificial Intelligence for Industry;
  • Intelligent Quality Control;
  • Smart Manufacturing;
  • Human–Computer Interaction;
  • Smart Robotics;
  • ESG computing for Manufacturing;
  • Next-generation Education;
  • Digital Twins for Industry;
  • Tiny Machine Learning.

Prof. Dr. Shih-Pang Tseng
Prof. Dr. Jhing-Fa Wang
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 250 words) can be sent to the Editorial Office for assessment.

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 System Innovation 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

  • digital transformation of industries
  • AI-driven innovation
  • big data and knowledge intelligence
  • industry internet
  • information and intelligence
  • artificial intelligence for industry
  • intelligent quality control
  • smart manufacturing
  • human–computer interaction
  • smart robotics
  • ESG computing for manufacturing
  • next-generation education
  • digital twins for industry
  • tiny machine learning

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

Published Papers (3 papers)

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Research

23 pages, 9755 KB  
Article
ABC Classification as Business Intelligence Method Based on a Novel Sales Segmentation and Feature Extraction Proposal
by Roberto Baeza-Serrato and Jorge Manuel Barrios-Sánchez
Appl. Syst. Innov. 2026, 9(4), 74; https://doi.org/10.3390/asi9040074 - 30 Mar 2026
Viewed by 978
Abstract
Daily, monthly, and annual multi-product sales records are stored in databases, but due to the massive amounts of data, they are not used for decision-making when updating product catalogs. Meanwhile, the use of artificial intelligence in business is increasing across all sectors of [...] Read more.
Daily, monthly, and annual multi-product sales records are stored in databases, but due to the massive amounts of data, they are not used for decision-making when updating product catalogs. Meanwhile, the use of artificial intelligence in business is increasing across all sectors of the economy. Large-scale data handling can be achieved using artificial intelligence techniques. Specifically, ABC inventory classification currently employs artificial intelligence techniques, including neural networks, fuzzy systems, and genetic algorithms. However, a state-of-the-art review has not found any research using vision techniques to classify ABC inventories. To address this gap, this research presents a novel approach to the intelligent classification of a company’s multiple products, using ABC. Recent vision system research often uses the Otsu method or its variants to determine the optimum threshold for binary image segmentation. Unlike this approach, our research does not use a single threshold value; instead, it uses the full binary frequency histogram as an image representation. From this, eight invariant characteristics are extracted from translation, rotation, and scale. The results show that the classification is accurate, clear, and simple as a decision-making tool. The proposed method is general and can be used in any production sector and at any enterprise size. Full article
(This article belongs to the Special Issue Information Industry and Intelligence Innovation)
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22 pages, 6190 KB  
Article
Machine Learning Operations on ZYNQ FPGA Board for Real-Time Face Recognition
by Bouchra Kouach, Mohcin Mekhfioui and Rachid El Gouri
Appl. Syst. Innov. 2026, 9(4), 71; https://doi.org/10.3390/asi9040071 - 26 Mar 2026
Viewed by 1583
Abstract
Nowadays, MLOps approaches are gaining popularity thanks to their ability to apply DevOps best practices to machine learning models. They enable the automation and optimization of model training, deployment, and monitoring in various environments, while ensuring effective Continuous Integration/Continuous Deployment (CI/CD). These approaches [...] Read more.
Nowadays, MLOps approaches are gaining popularity thanks to their ability to apply DevOps best practices to machine learning models. They enable the automation and optimization of model training, deployment, and monitoring in various environments, while ensuring effective Continuous Integration/Continuous Deployment (CI/CD). These approaches thus promote real-time applications that can react quickly and improve continuously. This paper examines the feasibility of implementing MLOps practices in embedded systems, specifically on the Zynq-7000 FPGA board. We present a comprehensive MLOps architecture that enables the automated deployment and monitoring of a convolutional neural network model for face recognition on an embedded hardware platform for datacenter physical access control scenarios. This architecture integrates GitLab CI/CD for version control and pipeline automation, MLflow for experiment tracking and model lifecycles management, Prometheus and Grafana for monitoring, and data storage in an S3 Bucket cloud connected to DVC for dataset versioning. The results demonstrate that the proposed pipeline can be effectively deployed on a Zynq-7000 FPGA board enabling automated model retraining, redeployment, and performance monitoring. This approach reduces operational complexity and supports faster adaptation to dataset changes. Full article
(This article belongs to the Special Issue Information Industry and Intelligence Innovation)
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23 pages, 376 KB  
Article
INTELLECTUM: A Hybrid AR-VR Metaverse Framework for Smart Cities
by Andrey Nechesov and Janne Ruponen
Appl. Syst. Innov. 2026, 9(3), 61; https://doi.org/10.3390/asi9030061 - 17 Mar 2026
Viewed by 898
Abstract
This work presents INTELLECTUM as a reference architecture and design-time evaluation framework for multi-entity XR–AI–digital twin systems. Rather than optimizing a specific implementation, the paper formalizes architectural invariants, event semantics, and coordination mechanisms that precede and inform system realization. INTELLECTUM provides a conceptual [...] Read more.
This work presents INTELLECTUM as a reference architecture and design-time evaluation framework for multi-entity XR–AI–digital twin systems. Rather than optimizing a specific implementation, the paper formalizes architectural invariants, event semantics, and coordination mechanisms that precede and inform system realization. INTELLECTUM provides a conceptual framework for structuring interactions across physical and virtual environments, emphasizing human-centered design, immersive digital twins, and collaborative extended-reality workspaces. The technical specification defines core architectural components, human integration modalities via WebXR and heterogeneous sensor networks, and representative usage scenarios within smart city ecosystems. By enabling AI-assisted urban planning, interactive simulation, and multi-actor coordination, INTELLECTUM positions itself as an XR-based architectural foundation for next-generation smart city platforms. Full article
(This article belongs to the Special Issue Information Industry and Intelligence Innovation)
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