Analytics Modeling in Reliability and Machine Learning and Its Applications

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 le...

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書目詳細資料
企業作者: SpringerLink (Online service)
其他作者: Pham, Hoang (Editor)
格式: 電子 電子書
語言:英语
出版: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
版:1st ed. 2025.
叢編:Springer Series in Reliability Engineering,
主題:
在線閱讀:https://doi.org/10.1007/978-3-031-72636-1
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書本目錄:
  • 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.