Browsing by Author "Prashant Kumar Sonker"

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Prediction of hepatitis-C virus using statistical learning models
(BioMed Central Ltd, 2025) Shalini Kumari; Subhajit Das; Prashant Kumar Sonker; Agni Saroj; Mukesh Sunil Kumar
The hepatitis-c virus (HCV) is a viral infection that targets the liver and has emerged as a significant global health concern. This study investigates the classification of HCV patients by identifying the potential factors crucial for the progression and early detection of HCV. The study includes dataset of 615 HCV patients from the UCI Machine Learning Repository for illustrative purposes and analyzed it through machine learning models such as naive Bayes (NB), random forest (RF), support vector machine (SVM), logistic regression (LR), decision trees (DT), and artificial neural network (ANN). The models were evaluated using various performance metrics, and a comparative analysis using non-parametric tests was conducted to evaluate the statistical significance of the model. The empirical findings show that the RF model achieved the highest performance, with an accuracy of 96.71% with Brier score (BS) of 0.035 and Matthews correlation coefficient (MCC) of 0.849, an accuracy of 96.45% with BS of 0.031 and MCC of 0.837 and an accuracy 97.41% with BS of 0.026 and MCC of 0.947 when evaluated using all features, using selected features, and selected features with the application of the synthetic minority oversampling technique (SMOTE). The analytical methods have improved the overall predictive accuracy for HCV infection and will aid in the early identification of the disease. As a result, patients can be treated at the earliest possible stage, thereby increasing the number of lives saved. © The Author(s) 2025.
Stress-strength reliability estimation for non-identical strength: a study on power Muth distribution
(Springer, 2025) Prashant Kumar Sonker; Agni Saroj; Vikas Baranwal; Mukesh Sunil Kumar
This article explores the extension of the stress-strength reliability model of a system and the multi-component systems when the components of the system are considered to be non-identical. These components are separated into two categories. Each component of the system has some strength and the common random stress applied to it. The component strength of both the categories follows Power-Muth (PM) distribution and the stress applied to the components also follows PM distribution. It may follow any other lifetime distributions. Both the strength and the stress are independent of each other. The estimation of stress-strength reliability and multi-component stress-strength reliability is carried out using well-known ML and MPS estimation methods. Based on varying parameters, the reliability of the models is discussed. All the statistical calculations are done by using Monte Carlo simulation. Real data applicability of the extended model is also performed in the article. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2025.
Stress–strength reliability models on power-Muth distribution
(Springer, 2023) Prashant Kumar Sonker; Mukesh Kumar; Agni Saroj
The power Muth distribution (PM), was first introduced by Jodra et al. (Math Model Anal 22(2):186–201, 2017) with great applicability in reliability theory. In this paper, we studied parameter estimation of PM distribution to know the changes in the behaviour of distribution by varying their parameters. Also, the reliability estimation, the stress–strength reliability (SSR) and the multi-component stress–strength reliability (MSSR) estimation are carried out. For the stress–strength model, the component strength and the stress applied to it, both are independent random variables and follow similar PM distribution. SSR describes the probability that the component strength is greater than the stress applied to it. While the MSSR works based on s-out-of-k (1 ≤ s≤ k) model which is described as the probability that at least s-out-of-k components’ strength are greater than the stress applied on it. Reliability behaviour is the major objective of this paper. For the estimation of parameters, we are inclined towards the maximum likelihood and maximum product spacing method of estimation. Based on their mean square error we compared these two. In the multi-component reliability model, the suitable trend is observed based on the number of components’ strengths exceeding the stresses applied to them. The effect of shape and scale parameters of PM distribution on various reliability models is observed. All the above statistical performances are carried out via the Monte Carlo simulation process. Real data applicability of the distribution is applied to the stress rupture life of Kevlar pressure vessel data on different used reliability models. © 2022, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.