Search Results (168)

With rapid digital transformation, online information and reviews have become more consequential, which may lead to a public opinion crisis. How to predict the persuasion effect is an important research problem in the design of a crisis communication strategy. The method for solving this problem is to propose a predictive framework for digital persuasion, grounded in the elaboration likelihood model. Within this framework, a database is constructed, and a machine learning algorithm integrating Bayesian networks and decision trees, BNTree (Bayesian Network and Tree), is proposed. The results demonstrate that BNTree can predict persuasion effects more accurately. In addition, the prediction of BNTree also reflects the major cognitive route of netizens and the critical influence factors for persuasion effects. These findings imply that integrating psychological theory into algorithm design can enhance predictive performance and interpretability, providing practical support for crisis communication in the digital era. Full article

In this work, we propose SCRATCH-AI, a tool which relies on interpretable machine learning (ML) methods (namely, Bayesian networks and decision trees) to classify honey samples into wound healing categories. Classification explores the impact of botanical origins (i.e., honey type) and key chemical–biological characteristics such as antioxidant activity on healing, assessed through wound recovery metrics. The obtained classification performance results are very encouraging. Moreover, the models provide non-trivial insights about the causal dependencies of some specific honey features on wound healing properties and show the effect of different honey types (other than the well known Manuka) on cicatrization. The tool is inherently interpretable (due to the chosen ML techniques) and made user-friendly by a carefully designed graphical interface. We believe that the information provided by our tool will allow biologists and clinicians to better utilize honey, with the ultimate goal of leveraging honey capability to accelerate healing and reduce infection risks in clinical practice. Full article

Background: Liquidity crises pose significant risks to financial stability, and missing data in predictive models increase the uncertainty in decision-making. This study aims to develop a robust Bayesian Model Averaging (BMA) framework using decision trees (DTs) to enhance liquidity crisis prediction under missing data conditions, offering reliable probabilistic estimates and insights into uncertainty. Methods: We propose a BMA framework over DTs, employing Reversible Jump Markov Chain Monte Carlo (RJ MCMC) sampling with a sweeping strategy to mitigate overfitting. Three preprocessing techniques for missing data were evaluated: Cont (treating variables as continuous with missing values labeled by a constant), ContCat (converting variables with missing values to categorical), and Ext (extending features with binary missing-value indicators). Results: The Ext method achieved 100% accuracy on a synthetic dataset and 92.2% on a real-world dataset of 20,000 companies (11% in crisis), outperforming baselines (AUC PRC 0.817 vs. 0.803, p < 0.05). The framework provided interpretable uncertainty estimates and identified key financial indicators driving crisis predictions. Conclusions: The BMA-DT framework with the Ext technique offers a scalable, interpretable solution for handling missing data, improving prediction accuracy and uncertainty estimation in liquidity crisis forecasting, with potential applications in finance, healthcare, and environmental modeling. Full article

In uncertain battlefield environments, rapid and accurate detection, identification of hostile targets, and assessment of threat levels are crucial for supporting effective decision-making. Despite offering the advantage of structural transparency, traditional analytical methods rely on expert knowledge to construct models and often fail to comprehensively capture the non-linear causal relationships among complex threat factors. In contrast, data-driven methods excel at uncovering patterns in data but suffer from limited interpretability due to their black-box nature. Owing to probabilistic graphical modeling capabilities, Bayesian networks possess unique advantages in threat assessment. However, existing models are either constrained by the limitation of expert experience or suffer from excessively high complexity due to structure learning algorithms, making it difficult to meet the stringent real-time requirements of uncertain battlefield environments. To address these issues, this paper proposes a new method, the Tree–Hillclimb Search method—an efficient and interpretable threat assessment method specifically designed for uncertain battlefield environments. The core of the method is a structure learning algorithm constrained by expert knowledge—the initial network structure constructed from expert knowledge serves as a constraint, enabling the discovery of hidden causal dependencies among variables through structure learning. The model is then refined under these expert knowledge constraints and can effectively balance accuracy and complexity. Sensitivity analysis further validates the consistency between the model structure and the influence degree of threat factors, providing a theoretical basis for formulating hierarchical threat assessment strategies under resource-constrained conditions, which can effectively optimize sensor resource allocation. The Tree–Hillclimb Search method features (1) enhanced interpretability; (2) high predictive accuracy; (3) high efficiency and real-time performance; (4) actual impact on battlefield decision-making; and (5) good generality and broad applicability. Full article

In this study, the geological disasters in Guizhou Province serve as the research object, and a systematic susceptibility evaluation is conducted in light of the province’s prominent problems with frequent geological disasters. The current research primarily focuses on the application of a single model, often with deficiencies in factor interpretation. It has not yet systematically integrated the advantages of the traditional information model and multiple machine learning algorithms, nor introduced interpretable methods to analyze the disaster mechanism deeply. In this study, the information value (IV) model is combined with machine learning algorithms—logistic regression (LR), decision tree (DT), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)—to construct a coupling model to evaluate the susceptibility to geological disasters. Combined with the Bayesian optimization algorithm, the geological disaster susceptibility evaluation model is built. The confusion matrix and receiver operating characteristic (ROC) curve were used to evaluate the model’s accuracy. The Shapley Additive exPlanations (SHAP) method is used to quantify the contribution of each influencing factor, thereby improving the transparency and credibility of the model. The results show that the coupling models, especially the IV-XGB model, achieved the best performance (AUC = 0.9448), which significantly identifies the northern Wujiang River Basin and the central karst core area as high-risk areas and clarifies the disaster-causing mechanism of “terrain–hydrology–human activities” coupling. The SHAP method further identified that NDVI, land use type, and elevation were the predominant controlling factors. This study presents a high-precision and interpretable modeling method for assessing susceptibility to geological disasters, providing a scientific basis for disaster prevention and control in Guizhou Province and similar geological conditions. Full article

In today’s fast-paced and evolving job market, salary continues to play a critical role in career decision-making. The ability to accurately classify job titles and predict corresponding salary ranges is increasingly vital for organizations seeking to attract and retain top talent. This paper proposes a novel approach, the Hybrid Bayesian Model (HBM), which combines Bayesian classification with advanced regression techniques to jointly address job title identification and salary prediction. HBM is designed to capture the inherent complexity and variability of real-world job market data. The model was evaluated against established machine learning (ML) algorithms, including Random Forests (RF), Support Vector Machines (SVM), Decision Trees (DT), and multinomial naïve Bayes classifiers. Experimental results show that HBM outperforms these benchmarks, achieving 99.80% accuracy, 99.85% precision, 100% recall, and an F1 score of 98.8%. These findings highlight the potential of hybrid ML frameworks to improve labor market analytics and support data-driven decision-making in global recruitment strategies. Consequently, the suggested HBM algorithm provides high accuracy and handles the dual tasks of job title classification and salary estimation in a symmetric way. It does this by learning from class structures and mirrored decision limits in feature space. Full article

Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability, and mechanical performance. To address this gap, this study proposes an AI-assisted framework integrating machine learning (ML) and Multi-Objective Optimization (MOO) to achieve a sustainable GPC design. A robust database of 1154 experimental records was developed, focusing on five key predictors: cement content, water-to-binder ratio, aggregate composition, glass powder content, and curing age. Seven ML models were optimized via Bayesian tuning, with the Ensemble Tree model achieving superior accuracy (R² = 0.959 on test data). SHapley Additive exPlanations (SHAP) analysis further elucidated the contribution mechanisms and underlying interactions of material components on GPC compressive strength. Subsequently, a MOO framework minimized unit cost and CO₂ emissions while meeting compressive strength targets (15–70 MPa), solved using the NSGA-II algorithm for Pareto solutions and TOPSIS for decision-making. The Pareto-optimal solutions provide actionable guidelines for engineers to align GPC design with circular economy principles and low-carbon policies. This work advances sustainable construction practices by bridging AI-driven innovation with building materials, directly supporting global goals for waste valorization and carbon neutrality. Full article

Random Forests are powerful machine learning models widely applied in classification and regression tasks due to their robust predictive performance. Nevertheless, traditional Random Forests face computational challenges during tree construction, particularly in high-dimensional data or on resource-constrained devices. In this paper, a novel node-splitting algorithm, BayesSplit, is proposed to accelerate decision tree construction via a Bayesian-based impurity estimation framework. BayesSplit treats impurity reduction as a Bernoulli event with Beta-conjugate priors for each split point and incorporates two main strategies. First, Dynamic Posterior Parameter Refinement updates the Beta parameters based on observed impurity reductions in batch iterations. Second, Posterior-Derived Confidence Bounding establishes statistical confidence intervals, efficiently filtering out suboptimal splits. Theoretical analysis demonstrates that BayesSplit converges to optimal splits with high probability, while experimental results show up to a 95% reduction in training time compared to baselines and maintains or exceeds generalization performance. Compared to the state-of-the-art MABSplit, BayesSplit achieves similar accuracy on classification tasks and reduces regression training time by 20–70% with lower MSEs. Furthermore, BayesSplit enhances feature importance stability by up to 40%, making it particularly suitable for deployment in computationally constrained environments. Full article

In causal inference research, accurate estimation of individualized treatment effects (ITEs) is at the core of effective intervention. This paper proposes a dual-structure ITE-estimation model based on Bayesian Additive Regression Trees (BART), which constructs independent BART sub-models for the treatment and control groups, estimates ITEs using the potential outcome framework and enhances posterior stability and estimation reliability through Markov Chain Monte Carlo (MCMC) sampling. Based on psychological stress questionnaire data from graduate students, the study first integrates BART with the Shapley value method to identify employment pressure as a key driving factor and reveals substantial heterogeneity in ITEs across subgroups. Furthermore, the study constructs an ITE model using a dual-structured BART framework (BART-ITE), where employment pressure is defined as the treatment variable. Experimental results show that the model performs well in terms of credible interval width and ranking ability, demonstrating superior heterogeneity detection and individual-level sorting. External validation using both the Bootstrap method and matching-based pseudo-ITE estimation confirms the robustness of the proposed model. Compared with mainstream meta-learning methods such as S-Learner, X-Learner and Bayesian Causal Forest, the dual-structure BART-ITE model achieves a favorable balance between root mean square error and bias. In summary, it offers clear advantages in capturing ITE heterogeneity and enhancing estimation reliability and individualized decision-making. Full article

This study systematically compares the predictive performance of single machine learning (ML) models (KNN, Bayesian ridge regression, decision tree) and ensemble learning methods (bagging, boosting, stacking) for quantifying the rheological properties of mineral powder-modified asphalt, specifically the complex shear modulus (G*) and the phase angle (δ). We used two emulsifiers and three mineral powders for fabricating modified emulsified asphalt and conducting rheological property tests, respectively. Dynamic shear rheometer (DSR) test data were preprocessed using the local outlier factor (LOF) algorithm, followed by K-fold cross-validation (K = 5) and Bayesian optimization to tune model hyperparameters. This framework uniquely employs cross-validated predictions from base models as input features for the meta-learner, reducing information leakage and enhancing generalization. Traditional single ML models struggle to characterize accurately as a result, and an innovative stacking model was developed, integrating predictions from four heterogeneous base learners—KNN, decision tree (DT), random forest (RF), and XGBoost—with a Bayesian ridge regression meta-learner. Results demonstrate that ensemble models outperform single models significantly, with the stacking model achieving the highest accuracy (R² = 0.9727 for G* and R² = 0.9990 for δ). Shapley additive explanations (SHAP) analysis reveals temperature and mineral powder type as key factors, addressing the “black box” limitation of ML in materials science. This study validates the stacking model as a robust framework for optimizing asphalt mixture design, offering insights into material selection and pavement performance improvement. Full article

The massive integration of variable renewable energy sources (RESs) poses the gradual necessity for new power system architectures with wide implementation of distributed battery energy storage systems (BESSs), which support power system stability, energy management, and control. This research presents a methodology and realization of a set of 11 BESS models based on different machine learning methods. The performance of the proposed models is tested using real-life BESS data, after which a comparative evaluation is presented. Based on the results achieved, a valuable discussion and conclusions about the models’ performance are made. This study compares the results of feedforward neural networks (FNNs), a homogeneous ensemble of FNNs, multiple linear regression, multiple linear regression with polynomial features, decision-tree-based models like XGBoost, CatBoost, and LightGBM, and heterogeneous ensembles of decision tree modes in the day-ahead forecasting of an existing real-life BESS in a PV power plant. A Bayesian hyperparameter search is proposed and implemented for all of the included models. Among the main objectives of this study is to propose hyperparameter optimization for the included models, research the optimal training period for the available data, and find the best model from the ones included in the study. Additional objectives are to compare the test results of heterogeneous and homogeneous ensembles, and grid search vs. Bayesian hyperparameter optimizations. Also, as part of the deep learning FNN analysis study, a customized early stopping function is introduced. The results show that the heterogeneous ensemble model with three decision trees and linear regression as main model achieves the highest average R² of 0.792 and the second-best nRMSE of 0.669% using a 30-day training period. CatBoost provides the best results, with an nRMSE of 0.662% for a 30-day training period, and offers competitive results for R²—0.772. This study underscores the significance of model selection and training period optimization for improving battery performance forecasting in energy management systems. The trained models or pipelines in this study could potentially serve as a foundation for transfer learning in future studies. Full article

The air transportation system is composed of multiple elements and belongs to a complex socio-technical system. It is difficult to assess the risk of an aircraft fault because it could constantly change during operation and is influenced by numerous factors. Although traditional methods such as Failure Mode, Effects, and Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) can reflect the degree of fault risk to a certain extent, they cannot accurately quantify and evaluate the fault risk under the multiple influences of human factors, random faults, and external environment. In order to solve these problems, this article proposes a fault risk assessment method for aircraft control systems based on a fault risk composite assessment framework using the Improved Risk Priority Number (IRPN) as the basis for the fault risk assessment. Firstly, a Bayesian network (BN) and Gated Recurrent Unit (GRU) are introduced into the traditional evaluation framework, and a hybrid prediction model combining static and dynamic failure probability is constructed. Subsequently, this paper uses the functional resonance analysis method (FRAM) by introducing a risk damping coefficient to analyze the propagation and evolution of fault risks and accurately evaluate the coupling effects between different functional modules in the system. Finally, taking the fault of a jammed flap/slat drive mechanism as an example, the risk of the fault is evaluated by calculating the IRPN. The calculation results show that the comprehensive failure probability of the aircraft control system in this case is 3.503 × 10⁻⁴. Taking into account the severity, the detection, and the risk damping coefficient, the calculation result of IRPN is 158.00. According to the classification standard of the risk level, the failure risk level of the aircraft belongs to a controlled risk, and emergency measures need to be taken, which is consistent with the actual disposal decision in this case. Therefore, the evaluation framework proposed in this article not only supports a quantitative assessment of system safety and provides a new method for fault risk assessments in aviation safety management but also provides a theoretical basis and practical guidance for optimizing fault response strategies. Full article

The Internet of Vehicles plays a crucial role in advancing intelligent transportation systems, with In-Vehicle Ethernet serving as the fundamental backbone network of the new generation of in-vehicle communication. However, In-Vehicle Ethernet faces various network security threats, including data theft, data tampering, and malicious attacks. This study focuses on network intrusion and security issues in In-Vehicle Ethernet, by analyzing the data characteristics of Audio Video Transport Protocol and potential network attack means. We innovatively propose a network intrusion detection method based on a weighted histogram algorithm. This method aims to enhance the security of In-Vehicle Ethernet. Experimental results show that the anomaly detection rate of the proposed weighted histogram algorithm in this study is 99.7%, which shows an improvement of 15.8% compared with the traditional Bayesian algorithm, and 6.9% higher than the decision tree algorithm. Thus, our approach enhances the stability and anti-attack ability of In-Vehicle Ethernet, providing a solid network security for In-Vehicle Networks. Full article

This manuscript proposes a methodology to leverage digitalisation to efficiently generate an overview of required condition-based railway track interventions, possession windows, and expected costs for railway networks at the beginning of the intervention planning process. The consistent and efficient generation of such an overview not only helps track managers in their decision-making but also facilitates the discussion among other decision-makers in later phases of the track intervention planning process, including line planners, capacity managers, and project managers. The methodology uses data of different levels of detail, discrete state modelling for uncertain deterioration of components, and component-level intervention strategies. It dynamically updates the condition estimates of components by capturing the interaction between deteriorating components using Bayesian filters. It also estimates the risks associated with different types of potential service losses that may occur due to sudden events using fault trees as a function of time and the condition of components. An implementation of the methodology is conducted for a 25 km regional railway network in Switzerland. The results suggest that the methodology has the potential to help track managers early in the intervention planning process. In addition, it is argued that the methodology will lead to improvements in the efficiency of the planning process, improvements in the scheduling of preventive interventions, and the reduction in corrective intervention costs upon the implementation in a digital environment. Full article

The operational efficiency and reliability of the ship’s lubrication oil system directly impact the vessel’s safety. Traditional reliability analysis methods struggle to effectively handle the system’s dynamic characteristics and multi-source data analysis. To address these issues, this study proposes an innovative method that integrates feature dimensionality reduction, a dynamic Bayesian network of gravity model to improve the accuracy of system reliability analysis. First, the proportional hazards model is used to evaluate the operational reliability of each component, providing a quantitative basis for assessing the system’s health status through failure rate estimation. Then, a dynamic Bayesian network model is employed for overall system reliability analysis, fully considering the impact of multi-state devices and different maintenance strategies. The proposed DBN-based reliability assessment method achieves significant improvements over the traditional Fault Tree Analysis (FTA). The reliability of the main lubrication oil system (GUB) increases from 0.169 to 0.261, representing a 9.2% improvement; under scheduled maintenance conditions, the system reliability stabilizes at approximately 0.9873 after $0.4\times {10}^5$ h, compared to only 0.24 without maintenance. The proposed method effectively evaluates the reliability of the lubrication oil system, and the maintenance strategy using this method can greatly improve the reliability, providing strong support for scientifically guiding maintenance decisions. Full article