Submit to ASI Review for ASI Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

AI-Driven Decision Support for Systemic Innovation

Print Special Issue Flyer
Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A Special Issue of Applied System Innovation (ISSN 2571-5577) belonging to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2026) | Viewed by 15466

Share This Special Issue

Editors

Dr. Yong Qin

E-Mail
Guest Editor

Business School, Sichuan University, Chengdu 610064, China
Interests: data mining; decision analysis; applications of artificial intelligence; smart tourism

Prof. Dr. Yuyan Luo

E-Mail
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

E-Mail Website
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-anonymized 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

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 (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

16 pages, 2629 KB

Open AccessArticle

Multi-Task Time Series Forecasting of Plant Load Losses: A Comparative Study of a Temporal Fusion Transformer and LSTM

by Bathandekile Boshoma, Oluwole Akinola and Peter Olukanmi

Appl. Syst. Innov. 2026, 9(9), 195; https://doi.org/10.3390/asi9090195 (registering DOI) - 17 Sep 2026

Viewed by 46

Abstract

Accurate load loss forecasting is important to prevent plant failures and improve power station reliability. While the Temporal Fusion Transformer and LSTM have demonstrated state-of-the-art performance in modelling complex temporal patterns, their effectiveness remains strongly influenced by the scale of the available data, and despite their promise, their application to predict load losses in power stations is underexplored, particularly for medium-term forecast horizons. We evaluated the TFT relative to an LSTM to forecast load losses 16 weeks ahead and predict the plant that will likely fail, across three data scales: 2000, 10,000, and 50,000 derived from a 5-year secondary load loss dataset from six power stations. The TFT substantially outperformed the LSTM on all data sizes, with the 50,000-sample size achieving the best results of 0.9789 prediction accuracy, 12.3853 MSE, 0.5539 MAE, 3.5193 RMSE, and 0.9858 for R². Collectively, these findings uncover the empirical boundaries of transformer-based models, illustrating that data volume serves as a pivotal determinant for the effective activation of advanced self-attention mechanisms of the TFT. Full article

(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)

► Show Figures

Figure 1

31 pages, 3447 KB

Open AccessArticle

An ESCO-Based Skill Gap Detection Framework for SMEs: A Design Science Prototype of an Intelligent Learning Management System

by Angelo Leogrande, Mauro di Molfetta, Nicola Magaletti, Valeria Notarnicola and Maria Giovanna Trotta

Appl. Syst. Innov. 2026, 9(8), 162; https://doi.org/10.3390/asi9080162 - 30 Jul 2026

Viewed by 835

Abstract

The misalignment between workforce competences and the requirements of digitally evolving occupations is a critical barrier to SME competitiveness. This study’s primary contribution is theoretical and methodological: it reconceptualizes the workforce skill gap as a firm-level human-capital–technology complementarity constraint rendered observable and commensurable through the ESCO taxonomy, and abstracts four transferable design principles—commensurability, macro–micro integration, a transferable metric, and modular extraction. Drawing on human capital theory, the knowledge-based view, and skill-biased technical change, the framework maps anonymized employee CVs to ESCO occupational requirements through a deterministic natural language processing procedure and computes a Skill Gap Indicator as the complement of evidenced competence coverage. A prototype Intelligent Learning Management System, developed within the LUCE project, instantiates the framework as a proof of concept, translating identified gaps into targeted training recommendations. Applied to a convenience sample of publicly available professional profiles, the indicator has a mean of 0.956, interpreted as a conservative upper-bound estimate rather than a literal deficit. The empirical results are an exploratory demonstration that motivates, rather than confirms, the posited link between skill gaps and firm performance; a cross-sectional test found no significant association, which the design cannot adjudicate. Confirmatory testing would require sample expansion, employer-provided workforce records, and a longitudinal design, identified as priorities for future research. The study thus contributes a standardised, interoperable, and transferable approach to measuring and comparing workforce skill gaps in SMEs. Full article

(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)

► Show Figures

Figure 1

20 pages, 445 KB

Open AccessArticle

Quantitative Modeling and Standardized Representation of Hierarchical Product Gene Structures for New Energy Vehicles

by Huiyong Yi and Yong Qin

Appl. Syst. Innov. 2026, 9(6), 125; https://doi.org/10.3390/asi9060125 - 12 Jun 2026

Viewed by 539

Abstract

Complex products continue to face low iterative-design efficiency and poor cross-generation data compatibility, while existing product-gene research is still constrained by the predominance of qualitative approaches, ambiguous representations of hierarchical associations, and insufficient standardization. Based on the principles of decomposition and reconstruction and the systems thinking of genetic engineering, this study develops a generic three-level framework for product genes at the platform, assembly, and component levels. Hierarchical mapping functions and parameter-constraint equations are introduced to enable quantitative representation, and a quantitative product-gene information system is established, including a core-parameter quantification model and inter-/intra-level association-strength models. By integrating multiple international standards, the study further constructs a tripartite standardized description system covering metadata, semantics, and format, and proposes a mathematical mapping method from product information to standardized formats. A case study of Company A’s Platform B and Concept Vehicle C shows that the association-strength model achieves the required adaptation threshold, thereby validating the proposed framework. This study provides quantitative theoretical support for the platform-based and intelligent development of complex products and offers an implementable technical solution for product-gene reuse and data sharing, particularly in the new energy vehicle industry. Full article

(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)

► Show Figures

Figure 1

57 pages, 3137 KB

Open AccessArticle

An Intelligent Decision-Support Framework Based on Fuzzy BWM–TOPSIS with Interdependent Criteria for Alternative Selection in Complex Construction Projects

by Luong Duc Long, Vo Thi Dinh Khanh, Nguyen Quang Trung and Truong Ngoc Son

Appl. Syst. Innov. 2026, 9(6), 108; https://doi.org/10.3390/asi9060108 - 26 May 2026

Cited by 1 | Viewed by 1159

Abstract

This study proposes an intelligent decision-support framework for alternative selection in complex construction projects, where evaluation processes are affected by uncertainty, multiple decision-makers, and interdependent criteria. The framework integrates the fuzzy group best–worst method with fuzzy TOPSIS into a unified structure that explicitly captures cross-criterion influence effects. First, triangular fuzzy judgments from multiple experts are used to derive criterion weights, while interdependencies among criteria are represented through a fuzzy influence-intensity matrix and incorporated into fuzzy nonlinear optimization models. This process enables the systematic estimation of both independent and interdependency-adjusted criterion weights. Second, the resulting weights are used in a fuzzy ranking procedure to evaluate alternatives according to their relative closeness to fuzzy ideal solutions. To enhance transparency, reproducibility, and practical usability, the proposed method is implemented in Python as an automated computational workflow for decision analysis. Its applicability is demonstrated through a real-world case study on access platform system selection for mechanical, electrical, and plumbing installation in an airport terminal subject to safety, productivity, workspace, and elevation-related constraints. The results show that explicitly modeling criterion interdependencies provides a more realistic evaluation structure and enhances the robustness and reliability of alternative selection in complex construction management contexts. Full article

(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)

► Show Figures

Figure 1

23 pages, 2608 KB

Open AccessArticle

An AI-Driven Decision Support System for Sustainable Smart Clothing Design Based on Flexible Material Properties and Environmental Metrics

by Fang Zheng, Yanping Lu, Junghee Lee, Hongyan Liu, Dandan Wang and Myun Kim

Appl. Syst. Innov. 2026, 9(5), 104; https://doi.org/10.3390/asi9050104 - 20 May 2026

Cited by 1 | Viewed by 830

Abstract

With the rapid expansion of the smart clothing market, designers face increasing pressure to balance functional performance, material suitability, environmental impact, and development efficiency. Conventional design workflows and rule-based assistance methods often struggle to provide adaptive and data-driven support for multi-constraint decision-making. To address this issue, this study proposes an AI-driven decision support system for sustainable smart clothing design based on a multi-scale dynamic graph convolutional network (MDGCN). The proposed system integrates material properties, environmental indicators, and user-oriented design requirements into a unified decision-support framework and further enhances feature extraction through an attention mechanism. Two datasets, the Wearable Technology Material Properties Dataset (WTMPD) and the Environmental Impact Assessment Dataset (EIAD), were used to validate the model and system effectiveness. Experimental results showed that the MDGCN-based model achieved accuracies of 0.964 and 0.943, with recalls of 0.923 and 0.920 on the WTMPD and EIAD datasets, respectively. In system-level evaluation, the proposed decision support system reduced design time from 120 h to 60 h, improved material selection accuracy to 90.2%, and achieved superior operational performance in terms of resource utilization (77.45%), energy consumption (115.25 kWh), and response time (1.56 s). These results demonstrate that the proposed framework can effectively support complex design decision-making while improving efficiency, sustainability, and adaptability in smart clothing development. The study provides a practical AI-enabled system innovation approach for sustainable smart clothing design by linking flexible material selection, environmental impact prediction, and designer-oriented decision support. In addition, the prototype deployment demonstrates the feasibility of applying the proposed system as a design-stage wearable AI tool for mediating human, technological, and environmental considerations in smart clothing development. Full article

(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)

► Show Figures

Figure 1

24 pages, 1425 KB

Open AccessArticle

AI-Driven Decision Support Beneath Uncertainty: A Hybrid Bayesian–PLS Model for Systemic Sustainability Innovation

by Mostafa Aboulnour Salem

Appl. Syst. Innov. 2026, 9(5), 99; https://doi.org/10.3390/asi9050099 - 12 May 2026

Cited by 5 | Viewed by 1051

Abstract

This study examines Responsible Decision-Making (RADM) in AI-enabled sustainability within tertiary education under conditions of uncertainty and complex interdependence. Conventional analytical approaches are limited in such settings because they typically explain behavioural relationships without adequately modelling uncertainty. To address this limitation, the study proposes an AI-driven Decision Support System (DSS) based on a hybrid probabilistic framework integrating PLS-SEM with Bayesian Network (BN) inference. The framework combines structural analysis with probabilistic reasoning in a unified, interpretable system capable of modelling conditional dependencies among decision variables. Data were collected from 713 academic leaders in tertiary education institutions in Saudi Arabia. The model examines the effects of AI-Driven Sustainable Value (AISV), Responsible AI Ease of Use (RAIU), Institutional Sustainability Support (ISS), Ethical Leadership Norms (ELN), Responsible AI Competence (RAC), and AI Risk and Hallucination Awareness (ARHA) on Responsible Decision-Making and Sustainability Impact Performance (GGIP). The results indicate that ELN and ARHA have significant positive effects on RADM, while AISV and RAIU also contribute positively to decision quality. In contrast, ISS and RAC do not demonstrate significant direct effects on RADM. However, ISS shows indirect effects through contextual and cognitive pathways. The findings further suggest that awareness of uncertainty and AI-related risks plays a more influential role in decision quality than technical competence alone. The model demonstrates strong explanatory power (R² = 0.64) and acceptable predictive capability (R² = 0.48). Bayesian inference further indicates that sustainability outcomes improve under favourable institutional and cognitive conditions. Overall, the framework provides an interpretable and scalable DSS that supports scenario-based evaluation and probabilistic decision analysis under uncertainty. The findings are specific to the institutional context examined in this study. Although the framework may have relevance to other organisational environments characterised by uncertainty and complex decision structures, no external or cross-contextual validation was conducted. Therefore, the findings should be interpreted with appropriate contextual caution. Full article

(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)

► Show Figures

Figure 1

38 pages, 24838 KB

Open AccessArticle

LLM-Driven Modeling and Decision Support Methods for Cross-Domain Collaborative Mission Systems

by Han Li, Dongji Li, Yunxiao Liu, Jinyu Ma, Guangyao Wang and Jianliang Ai

Appl. Syst. Innov. 2026, 9(4), 80; https://doi.org/10.3390/asi9040080 - 17 Apr 2026

Cited by 1 | Viewed by 1972

Abstract

Cross-domain formations composed of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vessels (USVs) are critical for maritime defense but face significant challenges in countering complex aerial threats and developing flexible, collaborative strategies. Addressing the limitations of traditional decision support systems in semantic understanding and dynamic adaptation, this paper proposes a novel Large Language Model (LLM)-driven decision support framework grounded in the Department of Defense Architecture Framework (DoDAF). By integrating Retrieval-Augmented Generation (RAG) with a domain-specific knowledge base, the framework enhances the LLM’s ability to align natural-language directives with standardized DoDAF view models, effectively mitigating hallucinations in tactical generation. The proposed framework coordinates a closed-loop process, using Petri net-based static logic verification to ensure structural consistency and Monte Carlo-based dynamic effectiveness evaluation to optimize the selection of kill chains. Experimental validations in a simulated UAV-USV maritime defense scenario demonstrate that the framework achieves 96.6% entity accuracy and 100% format compliance in model generation. In comparison, the generated cooperative kill chains significantly outperform non-cooperative methods by improving interception efficacy by approximately 26.08% under saturation attack conditions. This study develops an automated, interpretable workflow that transforms unstructured situational understanding into decision reporting, significantly enhancing the efficiency and reliability of cross-domain collaborative mission planning. Full article

(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)

► Show Figures

Figure 1

28 pages, 14898 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1501

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 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)

► Show Figures

Figure 1

21 pages, 3516 KB

Open AccessArticle

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 1390

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, 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)

► Show Figures

Figure 1

15 pages, 1041 KB

Open AccessArticle

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 1930

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 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)

► Show Figures

Figure 1

Review

Jump to: Research

38 pages, 6506 KB

Open AccessEditor’s ChoiceReview

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

Cited by 1 | Viewed by 2534

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. 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)

► Show Figures

Journal Menu

Journal Browser

AI-Driven Decision Support for Systemic Innovation

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI