Machine Learning
Mohammad Zahaby; Iman Makhdoom
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 ...
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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.
Bayesian Computation Statistics
Iman Makhdoom; Shahram Yaghoobzadeh Shahrastani; FGhazalnaz Sharifonnasabi
Abstract
This study focuses on estimating the parameters of the Lindley distribution under a Type-II censoringscheme using Bayesian inference. Three estimation approaches—E-Bayesian, hierarchical Bayesian, andBayesian methods—are employed, with a focus on vague prior data. The accuracy of the estimates ...
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This study focuses on estimating the parameters of the Lindley distribution under a Type-II censoringscheme using Bayesian inference. Three estimation approaches—E-Bayesian, hierarchical Bayesian, andBayesian methods—are employed, with a focus on vague prior data. The accuracy of the estimates isevaluated using the entropy loss function and the squared error loss function (SELF). We assess theefficiency of the proposed methods through Monte Carlo simulations, utilizing the Lindley approximationand the Markov Chain Monte Carlo (MCMC) technique. To demonstrate its practical applicability, weapply 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 theproposed approaches. This comprehensive evaluation underscores the advantages of Bayesian methods inparameter estimation under censoring schemes, providing valuable insights for applications in reliabilityanalysis and related fields. The study concludes with a summary of key findings, offering a foundation forfurther exploration of Bayesian techniques in censored data analysis.
Bayesian Computation Statistics
Iman Makhdoom
Abstract
This article focuses on the M/M/ 1 /K queuing model. In this model, the inter-arrival times ofcustomers to the system are random variables with an exponential distribution parameterized by λ , andthe service times of customers are random variables with an exponential distribution parameterized ...
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This article focuses on the M/M/ 1 /K queuing model. In this model, the inter-arrival times ofcustomers to the system are random variables with an exponential distribution parameterized by λ , andthe service times of customers are random variables with an exponential distribution parameterized byµ . We aim to estimate the traffic intensity parameter of this model using Bayesian, E-Bayesian, andhierarchical Bayesian methods. These methods utilize the entropy loss function and an appropriate priordistribution for the independent parameters λ and µ . Additionally, we employ the shrinkage-basedmaximum likelihood estimation method to obtain the parameter estimates. To determine the desiredtraffic intensity parameter estimate, we introduce a decision criterion based on a cost function, anda fuzzy criterion called the Average Customer Satisfaction Index (ACSI). The goal is to select theestimation with a higher ACSI index. To facilitate understanding, we compare this estimation using theMonte Carlo simulation method and two numerical examples based on the ACSI index.
