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AI-Driven Decision Support for Systemic Innovation
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AI-Driven Decision Support for Systemic Innovation

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

Deadline for manuscript submissions: 20 September 2026 | Viewed by 3949

Special Issue Editors

Dr. Yong Qin

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Guest Editor
Business School, Sichuan University, Chengdu 610064, China
Interests: data mining; decision analysis; applications of artificial intelligence; smart tourism
Prof. Dr. Yuyan Luo

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Guest Editor
College of Management Science, Chengdu University of Technology, Chengdu 610059, China
Interests: green innovation; system simulation; digital cultural tourism; artificial intelligence
Dr. Xinxin Wang

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Guest Editor
Business School, Sichuan University, Chengdu, China
Interests: decision analysis; information fusion; health management

Special Issue Information

Dear Colleagues,

These days, artificial intelligence has expanded into the decision support domain, allowing for more data-driven, intelligent, and adaptive solutions to intricate systemic issues. Even if they worked well in structured settings in the past, traditional decision support systems (DSSs) frequently found it difficult to handle the multidisciplinary, dynamic, and uncertain character of contemporary technology. The rapid advancement of artificial intelligence domains, including machine learning, natural language processing, and generative models in the AI era, has created new opportunities for DSS, supporting digital, industrial, engineering, and data ecosystems.

The Special Issue aims to showcase cutting-edge research that combines artificial intelligence technology with decision models, system architecture, and application system innovation. We welcome submissions that highlight methodological breakthroughs, hybrid computing frameworks, and practical applications that utilize artificial intelligence to improve decision-making efficiency, transparency, and adaptability. Potential topics include optimization and simulation empowered by artificial intelligence, intelligent human–machine collaboration, IoT and blockchain-enhanced decision support systems (DSSs), applications of artificial intelligence in healthcare and medical informatics, as well as in engineering design, industrial systems, and sustainable intelligent environments.

We cordially invite researchers, engineers, and practitioners to submit original research articles and reviews for this Special Issue.

Dr. Yong Qin
Prof. Dr. Yuyan Luo
Dr. Xinxin 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

  • AI-driven decision support
  • AI for smart tourism
  • systemic innovation and intelligent design
  • machine learning and data-driven DSS
  • AI in education and learning analytics
  • human–AI collaboration
  • simulation and optimization with AI
  • IoT- and blockchain-enhanced DSS
  • AI for medical informatics and healthcare innovation
  • engineering and industrial applications of AI-driven DSS
  • sustainable and smart systems innovation
  • explainable AI for DSS

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Published Papers (4 papers)

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Research

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28 pages, 14898 KB  
Article
Deep Learning for Classification of Internal Defects in Fused Filament Fabrication Using Optical Coherence Tomography
by Valentin Lang, Qichen Zhu, Malgorzata Kopycinska-Müller and Steffen Ihlenfeldt
Appl. Syst. Innov. 2026, 9(2), 42; https://doi.org/10.3390/asi9020042 - 14 Feb 2026
Viewed by 648
Abstract
Additive manufacturing is increasingly adopted for the industrial production of small series of functional components, particularly in thermoplastic strand extrusion processes such as Fused Filament Fabrication. This transition relies on technological advances addressing key process limitations, including dimensional instability, weak interlayer bonding, extrusion [...] Read more.
Additive manufacturing is increasingly adopted for the industrial production of small series of functional components, particularly in thermoplastic strand extrusion processes such as Fused Filament Fabrication. This transition relies on technological advances addressing key process limitations, including dimensional instability, weak interlayer bonding, extrusion defects, moisture sensitivity, and insufficient melting. Process monitoring therefore focuses on early defect detection to minimize failed builds and costs, while ultimately enabling process optimization and adaptive control to mitigate defects during fabrication. For this purpose, a data processing pipeline for monitoring Optical Coherence Tomography images acquired in Fused Filament Fabrication is introduced. Convolutional neural networks are used for the automatic classification of tomographic cross-sections. A dataset of tomographic images passes semi-automatic labeling, preprocessing, model training and evaluation. A sliding window detects outlier regions in the tomographic cross-sections, while masks suppress peripheral noise, enabling label generation based on outlier ratios. Data are split into training, validation, and test sets using block-based partitioning to limit leakage. The classification model employs a ResNet-V2 architecture with BottleneckV2 modules. Hyperparameters are optimized, with N = 2, K = 2, dropout 0.5, and learning rate 0.001 yielding best performance. The model achieves 0.9446 accuracy and outperforms EfficientNet-B0 and VGG16 in accuracy and efficiency. Full article
(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)
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21 pages, 3516 KB  
Article
Visual Navigation Using Depth Estimation Based on Hybrid Deep Learning in Sparsely Connected Path Networks for Robustness and Low Complexity
by Huda Al-Saedi, Pedram Salehpour and Seyyed Hadi Aghdasi
Appl. Syst. Innov. 2026, 9(2), 29; https://doi.org/10.3390/asi9020029 - 27 Jan 2026
Viewed by 628
Abstract
Robot navigation refers to a robot’s ability to determine its position within a reference frame and plan a path to a target location. Visual navigation, which relies on visual sensors such as cameras, is one approach to this problem. Among visual navigation methods, [...] Read more.
Robot navigation refers to a robot’s ability to determine its position within a reference frame and plan a path to a target location. Visual navigation, which relies on visual sensors such as cameras, is one approach to this problem. Among visual navigation methods, Visual Teach and Repeat (VT&R) techniques are commonly used. To develop an effective robot navigation framework based on the VT&R method, accurate and fast depth estimation of the scene is essential. In recent years, event cameras have garnered significant interest from machine vision researchers due to their numerous advantages and applicability in various environments, including robotics and drones. However, the main gap is how these cameras are used in a navigation system. The current research uses the attention-based UNET neural network to estimate the depth of a scene using an event camera. The attention-based UNET structure leads to accurate depth detection of the scene. This depth information is then used, together with a hybrid deep neural network consisting of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), for robot navigation. Simulation results on the DENSE dataset yield an RMSE of 8.15, which is an acceptable result compared to other similar methods. This method not only provides good accuracy but also operates at high speed, making it suitable for real-time applications and visual navigation methods based on VT&R. Full article
(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)
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15 pages, 1041 KB  
Article
Implementation and Rollout of a Trusted AI-Based Approach to Identify Financial Risks in Transportation Infrastructure Construction Projects
by Michael Grims, Daniel Karas, Marina Ivanova, Gerhard Höfinger, Sebastian Bruchhaus, Marco X. Bornschlegl and Matthias L. Hemmje
Appl. Syst. Innov. 2025, 8(6), 161; https://doi.org/10.3390/asi8060161 - 24 Oct 2025
Viewed by 1325
Abstract
Using big data for risk analysis of construction projects is a largely unexplored area. In this traditional industry, risk identification is often based either on so-called domain expert knowledge, in other words on experience, or on different statistical and quantitative analysis of individual [...] Read more.
Using big data for risk analysis of construction projects is a largely unexplored area. In this traditional industry, risk identification is often based either on so-called domain expert knowledge, in other words on experience, or on different statistical and quantitative analysis of individual past projects. The motivation of this research is based on the implemented and evaluated data-driven and AI-based DARIA approach to identify financial risks in the execution phase of transportation infrastructure construction projects that shows exceptional results at an early stage of the project execution phase and has already been deployed into enterprise-wide production within the STRABAG group. Due to DARIA’s productive use, concern and doubts about the trustworthiness of its ML algorithm are certainly possible, especially when DARIA identifies risky projects while all conventional metrics within the STRABAG controlling system do not identify any problems. “If AI systems do not prove to be worthy of trust, their widespread acceptance and adoption will be hindered, and the potentially vast societal and economic benefits will not be fully realized”. Thus, and based on the results of a user study during DARIA’s successful deployment into enterprise-wide production, this paper focuses on the identification of suitable indicators to measure the trustworthiness of the DARIA ML algorithm in the interaction between individuals and systems as well as on the modeling of the reproducibility of the internal state of DARIA’s ML model. Full article
(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)
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Review

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38 pages, 6506 KB  
Review
Systemic Integration of Artificial Intelligence in Financial Project Management: A Systematic Literature Review and BERTopic-Based Analysis
by Styve L. Ndjonkin Simen, Simon P. Philbin and Gordon Hunter
Appl. Syst. Innov. 2026, 9(4), 68; https://doi.org/10.3390/asi9040068 - 24 Mar 2026
Viewed by 439
Abstract
Artificial Intelligence (AI) is increasingly embedded in project management within the financial sector, yet existing research remains fragmented and largely focused on isolated technical applications. A systemic understanding of how AI reshapes financial project management as an integrated socio-technical capability is still lacking. [...] Read more.
Artificial Intelligence (AI) is increasingly embedded in project management within the financial sector, yet existing research remains fragmented and largely focused on isolated technical applications. A systemic understanding of how AI reshapes financial project management as an integrated socio-technical capability is still lacking. This study addresses this gap through a systematic literature review of 62 peer-reviewed articles (2022–2025), combined with BERTopic-based thematic analysis supported by large language model-assisted topic representation. The findings reveal the emergence of Agentic AI as a dominant theme, marking a shift from analytical support tools toward autonomous and collaborative agents embedded in project processes. While predictive analytics and automation are relatively mature, governance-oriented and human-centric dimensions remain underdeveloped and weakly integrated. This study contributes by: (1) presenting a computationally enhanced systematic mapping study that integrates a systematic literature review with BERTopic-based topic modelling to map the evolving research landscape; (2) identifying Agentic AI as a pivotal interface between technical execution and strategic governance; and (3) proposing a socio-technical target architecture that offers a structured roadmap for AI-enabled transformation in financial project management systems. Full article
(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)
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