Journal of Data Science and Modeling

Abstract The Weighted Marshall-Olkin Bivariate Exponential (WMOBE) distribution was first proposed by
Jamalizadeh and Kundu (2013), who examined its different characteristics and properties. Bayesian
estimation of the model parameters is carried out using both the squared error loss (SEL) function,
which is symmetric, and the linear-exponential (LINEX) loss function, which is asymmetric. These
estimators are derived under both informative and non-informative gamma priors. Given the complexity
of the four-parameters model, explicit analytical solutions for the Bayesian estimators are not attainable,
making it necessary to employ the Gibbs sampling procedure. Markov Chain Monte Carlo (MCMC)
methods are widely utilized to compute and implement these estimates. Furthermore, the convergence
behavior of the Markov chain toward a stationary distribution is carefully analyzed. Credible intervals,
particularly the highest posterior density (HPD) intervals for the unknown parameters, are also
constructed. To assess and compare the effectiveness of these estimation approaches, Monte Carlo simulations are performed. Finally, the methodology is applied to a real-world dataset for illustrative purposes.

Abstract Breast cancer (BC) is one of the leading causes of death in women worldwide. Early diagnosis of this disease can save many women’s lives. The Breast Imaging Reporting and Data System (BIRADS) is a standard method developed by the American College of Radiology (ACR). However, physicians have had a lot of contradictions in determining the value of BIRADS, and all aspects of patients have not been considered in diagnosing this disease using the methods that have been used so far. In this article, a novel decision support system (DSS) has been presented. In the proposed DSS, firstly, c-mean clustering was used to determine the molecular subtype for patients who did not have this value by combining the mammography reports processing along with hospital information systems (HIS) obtained from their electronic files. Then several classifiers such as convolutional neural networks (CNN), decision tree (DT), multi-level fuzzy min-max neural network (MLF), multi-class support vector machine (SVM), and XGboost were trained to determine the BIRADS. Finally, the values obtained by these classifiers were combined using weighted ensemble learning with the majority voting algorithm to obtain the appropriate value of BIRADS. This helps physicians in the early diagnosis of BC. Finally, the results were evaluated in terms of accuracy, specificity, sensitivity, positive predicted value (PPV), negative predicted value (NPV), and f1-measure by the confusion matrix. The obtained values were, 97.94%, 98.79%, 92.08%, 92.34%, 98.80%, and 92.19% respectively.

Abstract This study focuses on estimating the parameters of the Lindley distribution under a Type-II censoring
scheme using Bayesian inference. Three estimation approaches—E-Bayesian, hierarchical Bayesian, and
Bayesian methods—are employed, with a focus on vague prior data. The accuracy of the estimates is
evaluated using the entropy loss function and the squared error loss function (SELF). We assess the
efficiency of the proposed methods through Monte Carlo simulations, utilizing the Lindley approximation
and the Markov Chain Monte Carlo (MCMC) technique. To demonstrate its practical applicability, we
apply the methodology to a real-world dataset to analyze the performance of the methods in detail.
Comparative results from the simulations and data analysis reveal the robustness and accuracy of the
proposed approaches. This comprehensive evaluation underscores the advantages of Bayesian methods in
parameter estimation under censoring schemes, providing valuable insights for applications in reliability
analysis and related fields. The study concludes with a summary of key findings, offering a foundation for
further exploration of Bayesian techniques in censored data analysis.

Abstract This article focuses on the M/M/ 1 /K queuing model. In this model, the inter-arrival times of
customers to the system are random variables with an exponential distribution parameterized by λ , and
the service times of customers are random variables with an exponential distribution parameterized by
µ . We aim to estimate the traffic intensity parameter of this model using Bayesian, E-Bayesian, and
hierarchical Bayesian methods. These methods utilize the entropy loss function and an appropriate prior
distribution for the independent parameters λ and µ . Additionally, we employ the shrinkage-based
maximum likelihood estimation method to obtain the parameter estimates. To determine the desired
traffic intensity parameter estimate, we introduce a decision criterion based on a cost function, and
a fuzzy criterion called the Average Customer Satisfaction Index (ACSI). The goal is to select the
estimation with a higher ACSI index. To facilitate understanding, we compare this estimation using the
Monte Carlo simulation method and two numerical examples based on the ACSI index.