Titlebook: Reliability Engineering for Industrial Processes; An Analytics Perspec P. K. Kapur,Hoang Pham,Vivek Kumar Book 2024 The Editor(s) (if appli

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Analysis of Progressively Censored Repair Time of Airborne Communication Transceiver with Burr-Hatkmethods of estimation for complete data are generalized to the case under progressive censored samples. These approaches comprise maximum likelihood, least squares, maximum product spacings, and Bayesian estimation. Interval estimate and coverage probability for the parameter are derived by the use

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,A Review on?Kidney Failure Prediction Using Machine Learning Models,y detection of kidney failure is crucial in preventing and managing this condition. In recent years, machine learning (ML) models have emerged as promising tools for predicting kidney failure, offering the potential to improve patient outcomes through timely intervention. This comprehensive review p

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,Machine Learning Based Remaining Useful Life Estimation—Concept and Case Study,ications indispensable. Traditional methods, like Reactive Maintenance, fail to detect problems beforehand and can jeopardize resources and/or lives. Proactive Maintenance measures, especially Predictive Maintenance has gained popularity with the advent of tons of data-handling resources. Remaining

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Modelling Software Reliability Growth Incorporating Testing Coverage Function and Fault Reduction Frequires quality software. A number of studies have been undertaken in recent years in order to develop an extremely trustworthy software system. To be more precise, there have been several analytical software reliability models put out for the evaluation of software reliability. Here, we examine re

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,Software Defect Prediction Using Abstract Syntax Trees Features and Object—Oriented Metrics,uirements. Previous studies used methods such as classifying modules as faulty or not, or performing multi-class classification to predict the number of bugs. Some studies used Object-Oriented (OO) metrics, while others used Abstract Syntax Trees (ASTs) to extract code features for bug prediction. T

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,A Review of Alzheimer’s Disease Identification by Machine Learning,ep learning techniques. Support Vector Machines (SVMs) and Decision Trees serve as robust tools, providing transparency and interpretability in the analysis of diverse datasets, including genetic, clinical, and imaging information. These methods contribute to the elucidation of key factors influenci

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