Applied Stochastic Models in Business and Industry最新文献

The evaluation of healthcare services is a crucial aspect of public health management, as it provides insights into service effectiveness, efficiency, and user satisfaction. This paper proposes a multidimensional approach to measuring healthcare service experience by constructing a synthetic index that incorporates three key perceptual dimensions: Cost, accessibility, and quality. These latent constructs are measured using a set of elementary indicators from the European Quality of Life Survey. To develop the dimensional synthetic indices—one for each experience dimension—as well as the overall experience index and the customer satisfaction index, we employ Partial Least Squares Path Modeling (PLS-PM). This approach not only enables the synthesis of latent variables but also allows for the analysis of structural relationships between them. The results provide a comprehensive framework for assessing healthcare service experiences and offer valuable insights for policymakers and service providers aiming to enhance healthcare quality and accessibility while improving user satisfaction.

The advent of ensemble methods, such as Random Forest (RF), has led to a paradigm shift in supervised learning. These methods have achieved remarkable levels of prediction accuracy by aggregating multiple weak learners. However, a drawback of these methods is their lack of transparency, which often prevents users from understanding their prediction processes. In light of these challenges, Explainable Ensemble Trees (E2Tree) has recently been proposed, providing a graphical representation of the relationships between response variables and predictors in RFs for classification. E2Tree constructs a single decision tree based on (dis)similarities between observations. By summarizing both distances in terms of predictors and a forest as a single decision tree, E2Tree merges the strengths of both decision trees and decision tree ensembles. In this paper, we propose to extend the E2Tree methodology to regression contexts. We investigate the performance of E2Tree for regression using real-world datasets. We use the Mantel test to test the correlation between similarities of the RF and E2Tree.

At a critical moment in the 2007–2009 financial crisis, financial authorities in the US, Japan, the United Kingdom, France, Canada, and Germany unanimously banned short sales in their respective markets. We estimate GARCH models with intervention analysis to assess the effect of such regulatory decisions on the unconditional or long-term stock market volatility, and we focus on trading days under short selling restrictions. Contrary to the conclusion reached by some important literature, our findings reveal that, for all six aforementioned markets, the volatility did not grow once the restrictions were imposed, and it began to decrease to the levels observed previously to the bankruptcy of Lehman Brothers.

Uncertain data mining has been a growing field of research in recent years. Numerous data mining techniques for performing tasks such as clustering, classification, anomaly detection, and so on have been developed for uncertain data. Principal component analysis (PCA) and its extension, kernel principal component analysis (KPCA), are two well-known techniques that are widely used for dimensionality reduction and feature extraction for traditional certain data. However, for uncertain data, these techniques have not been developed to the best of our knowledge. In this paper, uncertain principal component analysis (UPCA) and uncertain kernel principal component analysis (UKPCA) are developed. The proposed techniques consider the inherent uncertainty of the uncertain data, unlike the traditional techniques that ignore such uncertainty. In addition, in this paper, we propose the decision tree algorithm classification model combined with the developed UKPCA technique. The proposed model is capable of achieving high classification accuracy for both real-world and synthetic data, especially for cases that involve classes having nonlinear/arbitrary shapes.

Malware detection poses a critical challenge for both society and Business and Industry (B&I), particularly given the necessity for secure digital transformation. Among various cybersecurity threats, ransomware has emerged as especially disruptive, capable of halting operations, interrupting business continuity, and causing significant financial damage. Recent research has increasingly leveraged machine learning (ML) techniques to detect ransomware using Hardware Performance Counters (HPCs)—special CPU registers that track low-level hardware activities. In this study, we first propose a Sample Entropy (SampEn)-based method for compressing HPC time series data. This method effectively reduces dimensionality while preserving essential behavioral patterns, thus making it particularly suitable for practical B&I scenarios where accuracy and computational efficiency are crucial. Second, we investigate explainable algorithms for ransomware detection in B&I contexts, emphasizing transparency and interpretability. To achieve this goal, we focus on graphical models, specifically Markov Random Fields (MRFs) and Bayesian Networks. We evaluate the performance of these explainable methods against a baseline comprising Elastic Net, Support Vector Machines (SVM) with a radial kernel, XGBoost, and Autoencoder models. Our results demonstrate that these graphical models provide consistent and interpretable outcomes, closely aligned with known ransomware behaviors.

We discuss the new approach to modelling cascading failures from a probabilistic viewpoint based on exploding self-exciting point processes. It explains a possible mechanism of the cascade development. From the practical point of view, this approach might be oversimplified for modelling the flow of events (failures) in, for example, real-life power grids (as, e.g., not considering the specific network topology). However, even in this general form, it can be useful for overall modelling of the converging process of cascading failures and understanding the probabilistic nature of this interesting phenomenon. Three baseline processes are considered: the geometric process, the geometric-type process with a decreasing threshold after each event and the extended generalised Polya process.

Big data is becoming an integral part of everyday life. Data can be generated through various sources, and analyzing this data to generate revenue is the biggest challenge. The growth of grocery stores in the online and offline market requires retailers to analyze customer purchase behavior. Effective analysis can improve service quality, profitability, and customer satisfaction. This paper focuses on Market Basket Analysis (MBA), an efficient technique that identifies customer purchase behaviors. AAGI an Apriori-based algorithm is developed and used to analyze categorical data collected from grocery stores. The dataset consists of 10,233 transactions collected from the Raigad region Maharashtra, India and includes 125 unique items across dairy products, fruits, processed food, and vegetables. Association rules were generated using support values of 2%, 3%, 3.5%, and 4% and confidence values of 20%, 30%, and 40%. The best results were observed with the support of 3.5% and confidence of 30%, which produced 18 profound association rules. These findings inform targeted marketing strategies and cross-selling opportunities.