Analytics Modeling in Reliability and Machine Learning and Its Applications / edited by Hoang Pham.

Format:

Book

Author/Creator:

Pham, Hoang.

Series:

Springer Series in Reliability Engineering, 2196-999X

Language:

English

Subjects (All):

Machine learning.

Computers.

Medical care.

Industrial engineering.

Production engineering.

Mathematical optimization.

Aerospace engineering.

Astronautics.

Machine Learning.

Hardware Performance and Reliability.

Health Care.

Industrial and Production Engineering.

Optimization.

Aerospace Technology and Astronautics.

Local Subjects:

Machine Learning.

Hardware Performance and Reliability.

Health Care.

Industrial and Production Engineering.

Optimization.

Aerospace Technology and Astronautics.

Physical Description:

1 online resource (480 pages)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

This book presents novel research and application chapters on topics in reliability, statistics, and machine learning. It has an emphasis on analytical models and techniques and practical applications in reliability engineering, data science, manufacturing, health care, and industry using machine learning, AI, optimization, and other computational methods. Today, billions of people are connected to each other through their mobile devices. Data is being collected and analysed more than ever before. The era of big data through machine learning algorithms, statistical inference, and reliability computing in almost all applications has resulted in a dramatic shift in the past two decades. Data analytics in business, finance, and industry is vital. It helps organizations and business to achieve better results and fact-based decision-making in all aspects of life. The book offers a broad picture of current research on the analytics modeling and techniques and its applications in industry. Topics include: l Reliability modeling and methods. l Software reliability engineering. l Maintenance modeling and policies. l Statistical feature selection. l Big data modeling. l Machine learning: models and algorithms. l Data-driven models and decision-making methods. l Applications and case studies in business, health care, and industrial systems. Postgraduates, researchers, professors, scientists, engineers, and practitioners in reliability engineering and management, machine learning engineering, data science, operations research, industrial and systems engineering, statistics, computer science and engineering, mechanical engineering, and business analytics will find in this book state-of-the-art analytics, modeling and methods in reliability and machine learning.

Contents:

Preface

1. Reliability Analysis For Inventory Management For Repair Parts Based on Imperfect Data.-2. Improved Industrial Risk Analysis via a Human Factor-driven Bayesian Network Approach

3. Unsupervised Representation Learning Approach for Intrusion Detection in the Industrial Internet of Things Network Environment

4. Aero-engine Life Prediction Based on ARIMA and LSTM with Multi-Head Attention Mechanism

5. Human-Machine Integration to Strengthen Risk Management in the Winemaking Industry

6. One-Class Classification for Credit Card Fraud Detection: A Detailed Study with Comparative Insights from Binary Classification

7. Performance Analysis of Big Transfer Models on Biomedical Image Classification

8. Machine Learning Approach for Testing the Efficiency of Software Reliability Estimators of Weibull Class Models

9. Holistic Perishable Pharmaceutical Inventory Management System

10. Optimum Switch Self-Check Interval for Safety-Critical Device Mission Reliability

11. Accurate Estimation of Cargo Power Using Machine Learning Algorithms

12. Digital Transformation in Software Quality Assurance

13. Stress Studies: A Review

14. Higher Order Dynamic Mode Decomposition-based Timeseries Forecasting for Covid-19

15. System Trustability: New Concept and Applications

16. Digital Twin Implementation in Small and Medium Size Enterprises: A Case Study

17. Software Reliability Modeling: A Review.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Pham, Hoang Analytics Modeling in Reliability and Machine Learning and Its Applications

ISBN:

9783031726361

OCLC:

1498496278