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AI-Enhanced Decision Support Systems
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AI-Enhanced Decision Support Systems

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

Deadline for manuscript submissions: 20 January 2027 | Viewed by 3768

Editor

Dr. Lu Meng

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang, China
Interests: artificial intelligence; image processing; computer-aided diagnosis; blockchain

Special Issue Information

Dear Colleagues,

The field of decision support is undergoing a fundamental transformation driven by the convergence of artificial intelligence and an increase in heterogeneous data. Traditional systems, which often rely on structured data and pre-defined rules, are increasingly inadequate for complex, real-world scenarios, particularly those that involve uncertainty and multi-modal information streams, including, for instance, medical images paired with clinical notes or industrial sensor data alongside maintenance logs. The core challenge, then, lies in moving beyond siloed data analysis and merely passive information delivery, with the aim of creating integrated, prescriptive systems that can effectively synthesize diverse evidence, reason under uncertainty, and provide actionable, context-aware recommendations to human experts. Promising solutions are emerging at the intersection of several key AI disciplines. In particular, the fusion of deep learning for perception (e.g., image processing), causal reasoning for understanding, and, importantly, the contextual capabilities of Large Language Models (LLMs) provides a pathway for building next-generation Decision Support Systems (DSSs). These systems are positioned to act not merely as tools, but also as collaborative partners that help enhance human expertise across critical domains such as healthcare and industrial operations.

This Special Issue will curate pioneering research that demonstrates this paradigm shift, with a specific focus on the following interconnected areas:

  • Image processing & computer vision for DSSs;
  • Computer-aided diagnosis & clinical decision support;
  • Large Language Models (LLMs) in decision-centric workflows;
  • Industrial predictive maintenance (PDM) & operational intelligence;
  • Multi-modal data fusion: the architectural backbone.

We welcome contributions that address the integration of two or more of these focus areas, presenting end-to-end AI-DSS solutions that are validated on real-world challenges and include critical discussions on deployment, scalability, and human–AI collaboration.

Dr. Lu Meng
Guest Editor

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

  • decision support systems
  • medical image processing
  • computer-aided diagnosis
  • deep learning
  • clinical decision making
  • large language models
  • financial risk assessment
  • industrial predictive maintenance
  • smart city management
  • multi-modal data fusion

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

Published Papers (4 papers)

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Research

18 pages, 8831 KB  
Article
Intelligent Fault Diagnosis and Maintenance Decision Support in Electrical Induction Generators Using Multi-CNN Extreme Ensemble Learning
by Majida Khaleel Ahmed, Ahmed Mohammed Mohsin Alzubaidi, Zeashan Hameed Khan, Ahmed Ali Farhan Ogaili, Alaa Abdulhady Jaber and Luttfi A. Al-Haddad
Appl. Syst. Innov. 2026, 9(9), 182; https://doi.org/10.3390/asi9090182 - 30 Aug 2026
Viewed by 379
Abstract
Electrical induction generators are vulnerable to winding faults that can degrade operational reliability and lead to unplanned maintenance. This study proposes a multiple-convolutional neural network (Multi-CNN) extreme ensemble learning framework for intelligent fault diagnosis and maintenance decision support using three-phase current and voltage [...] Read more.
Electrical induction generators are vulnerable to winding faults that can degrade operational reliability and lead to unplanned maintenance. This study proposes a multiple-convolutional neural network (Multi-CNN) extreme ensemble learning framework for intelligent fault diagnosis and maintenance decision support using three-phase current and voltage measurements. Experimental recordings representing healthy operation, inter-turn faults, and inter-winding faults were segmented into non-overlapping 200-sample windows. Hjorth activity, mobility, and complexity were calculated for the three-phase current signals and the three-phase voltage signals, producing 18 features for each of 900 instances. Four convolutional neural network architectures were trained, and their class-probability outputs were combined through an extreme learning machine. Stratified blocked five-fold cross-validation was used to evaluate the models while preserving the chronological structure of the data. The proposed ensemble achieved 98.111% accuracy, 98.146% precision, 98.111% recall, 98.108% F1-score, and 97.167% Matthews correlation coefficient, correctly classifying 883 of 900 out-of-fold instances. It also attained a macro-averaged area under the receiver operating characteristic curve of 0.995. These results demonstrate that Hjorth-based electrical-signal characterization and Multi-CNN ensemble fusion can provide accurate and computationally efficient support for fault identification and predictive maintenance decisions in electrical induction generators. Full article
(This article belongs to the Special Issue AI-Enhanced Decision Support Systems)
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31 pages, 1709 KB  
Article
Spatial Heterogeneity in Cancer Incidence: Assessing Behavioral and Environmental Associations Using Machine Learning and Multiscale Geographically Weighted Regression
by Yuhang Xie, Zhe Zhang, Chanam Lee, Marcia G. Ory, Ipek Nese Sener, Bahar Dadashova, Gisou Salkhi Khasraghi, Jinsil Hwaryoung Seo, Galen Newman, Chunwu Zhu, Wenjin Wang and Xuemei Zhu
Appl. Syst. Innov. 2026, 9(9), 181; https://doi.org/10.3390/asi9090181 - 30 Aug 2026
Viewed by 305
Abstract
Cancer incidence exhibits substantial spatial disparities associated with environmental, behavioral, built-environment, healthcare access, and socioeconomic conditions, yet the extent to which these county-level associations vary geographically and across spatial scales remains insufficiently understood. This study evaluates an explainable spatial epidemiology workflow that integrates [...] Read more.
Cancer incidence exhibits substantial spatial disparities associated with environmental, behavioral, built-environment, healthcare access, and socioeconomic conditions, yet the extent to which these county-level associations vary geographically and across spatial scales remains insufficiently understood. This study evaluates an explainable spatial epidemiology workflow that integrates Random Forest (RF), SHapley Additive exPlanations (SHAP), permutation importance, Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) to examine county-level incidence for all-site cancer as a composite benchmark, colorectal cancer, female breast cancer, and melanoma of the skin across Texas, USA. All outcomes were screened from a common leakage-safe pool of 44 predictors. RF-SHAP/permutation screening was conducted only in spatially separated discovery counties, and the retained predictors were subsequently evaluated using OLS, GWR, and MGWR in independent confirmation counties. The screening procedure retained 19–23 predictors across outcomes, reducing model dimensionality by approximately 48–57%. Although screening substantially reduced AICc, in-sample also decreased, indicating improved parsimony and complexity-adjusted fit rather than improved explanatory performance; same-cardinality random, correlation-based, and LASSO benchmarks further showed that the advantage of RF-based screening was outcome- and model-dependent. In independent confirmation analyses, GWR was preferred by AICc for all-site cancer (AICc = 442.79), whereas OLS was preferred for colorectal cancer (356.48), female breast cancer (367.50), and melanoma (234.56); MGWR was not AICc-preferred for any outcome. However, where estimation was feasible, MGWR provided complementary multiscale information by distinguishing fitted associations characterized by near-global versus more localized spatial bandwidths. Five-fold nested spatial-block cross-validation showed stronger geographic predictive performance for RF, with pooled values of 0.422, 0.235, 0.442, and 0.341 for all-site, colorectal, breast, and melanoma outcomes, respectively, whereas GWR produced negative held-out for all four outcomes. These findings support explainable-ML screening primarily as a transparent dimensionality reduction strategy and demonstrate complementary roles for spatial modeling: AICc evaluates whether additional spatial complexity is justified, MGWR characterizes predictor-specific spatial scales where feasible, and spatial-block validation evaluates geographic predictive generalization. The resulting associations and spatial scales are interpreted as ecological and descriptive rather than causal. Full article
(This article belongs to the Special Issue AI-Enhanced Decision Support Systems)
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21 pages, 391 KB  
Article
Towards Intelligent Fiscal Auditing: Integrating Network Analytics and Predictive Systems for Proactive Risk Detection
by Andrés F. Cifuentes-Perdomo, Carlos A. Rodado-Grijalba, Mauricio A. Vargas-Hernández, Lilibeth Aguilera-Pua, Rosse M. Villamil-Cañas, Jaime A. Restrepo-Carmona, Luis A. Fletscher and Hernán Felipe García
Appl. Syst. Innov. 2026, 9(6), 111; https://doi.org/10.3390/asi9060111 - 28 May 2026
Viewed by 657
Abstract
Public procurement systems are prone to risks such as collusion, contractual concentration, and irregular subcontracting, which undermine transparency and accountability. Traditional fiscal oversight approaches remain largely retrospective, limiting their ability to anticipate irregularities and prevent potential losses. Addressing the gap between theoretical machine [...] Read more.
Public procurement systems are prone to risks such as collusion, contractual concentration, and irregular subcontracting, which undermine transparency and accountability. Traditional fiscal oversight approaches remain largely retrospective, limiting their ability to anticipate irregularities and prevent potential losses. Addressing the gap between theoretical machine learning models and real-world institutional deployment, this study introduces an applied system innovation that integrates two complementary approaches at a national scale: a Contractual Network Model (Mallas Contractuales) and a Predictive Risk Model for Contractors. The first component uses graph-based analytics, employing an Entity–Link–Property schema to represent relationships among entities, contractors, and contracts, thereby enabling the detection of structural patterns associated with collusive or anomalous behavior. The second component implements supervised machine learning models, trained on more than 16 million contracts and 2.6 million contractors from sources such as SECOP, RUES, DIAN, and national sanction registries. Models, including Random Forests and Gradient Boosted Trees, were optimized via cross-validated hyperparameter search and evaluated on a separate hold-out set using ROC AUC and Gini metrics, achieving strong discriminatory performance under the available retrospective validation setting while maintaining operational interpretability. Both approaches were deployed in a modular architecture that integrated Databricks, i2 Analyst’s Notebook, and Power BI dashboards, providing interactive visualizations and risk scores at multiple levels. Together, these systems demonstrate how the convergence of graph analytics and predictive modeling enables proactive fiscal auditing, strengthens institutional capacity, and offers a replicable framework for public sector accountability. Full article
(This article belongs to the Special Issue AI-Enhanced Decision Support Systems)
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11 pages, 620 KB  
Article
Using Natural Language and Health Ontologies in Hope Recommender System: Evaluation of Use in Medicine
by Hans Eguia, Carlos Sánchez-Bocanegra, Carlos Fernandez Llatas, Fernando Alvarez López and Francesc Saigí-Rubió
Appl. Syst. Innov. 2026, 9(5), 86; https://doi.org/10.3390/asi9050086 - 27 Apr 2026
Viewed by 1632
Abstract
Objectives: Despite the widespread availability of digital clinical information, timely access to relevant biomedical evidence during routine consultations remains limited in practice. Primary care clinicians, in particular, face significant time constraints that make it difficult to integrate comprehensive literature searches into everyday workflows. [...] Read more.
Objectives: Despite the widespread availability of digital clinical information, timely access to relevant biomedical evidence during routine consultations remains limited in practice. Primary care clinicians, in particular, face significant time constraints that make it difficult to integrate comprehensive literature searches into everyday workflows. This study evaluates whether an ontology-based recommender system can support routine clinical workflows by reducing information retrieval time while preserving the clinically acceptable usefulness of retrieved evidence. We assessed the performance of the HOPE (Health Operation for Personalised Evidence) system compared with realistic manual PubMed searches conducted by physicians. Materials and Methods: We conducted an observational evaluation involving 50 primary care physicians, who independently assessed 30 anonymised, rewritten clinical cases representative of common primary care scenarios. HOPE automatically extracted biomedical concepts from case descriptions using natural language processing and mapped them to Unified Medical Language System (UMLS) ontologies to generate ranked PubMed recommendations. A subset of 10 physicians also conducted manual PubMed searches in line with their usual clinical practice. Article relevance was assessed using a predefined binary criterion, and a reference relevance set was established by consensus among three senior physicians using a pooled document set. Retrieval performance was evaluated using Precision@k, relative Recall@k, and Normalised Discounted Cumulative Gain (NDCG@k). Manual search time was measured using a standardised stopwatch protocol, whereas HOPE response time was logged automatically by the system. Results: Inter-physician agreement in relevance assessment was substantial (Fleiss’ κ = 0.66; 95% CI: 0.61–0.70). HOPE achieved moderate-to-high precision within the top-ranked results (Precision@3 = 0.72), with relative recall increasing as additional documents were considered. Ranking metrics indicated that relevant articles were generally positioned early in the result lists. The mean total retrieval time for manual PubMed searches was 13.3 ± 1.7 min per case, compared with 17.4 ± 2.1 s for HOPE-assisted retrieval (p < 0.001). Conclusions: In a controlled, workflow-oriented evaluation using synthetic clinical cases, HOPE substantially reduced information retrieval time while maintaining clinically acceptable relevance in the retrieved literature. These findings support the use of ontology-based, AI-assisted systems as workflow-support tools to facilitate timely access to biomedical evidence, without replacing clinical judgment. Full article
(This article belongs to the Special Issue AI-Enhanced Decision Support Systems)
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