Analytics Modeling in Reliability and Machine Learning and Its Applications

2025
ISBN: 978-3-031-72635-4
Verlag: Springer

Buch, Englisch, 350 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Springer Series in Reliability Engineering

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.



Bringing together many experts and active researchers from academic and industry in the field, this book offers a broad picture of current research on the analytics modeling and techniques and its applications in industry. Topics to be covered in this book 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.



Each chapter is written by active researchers and experts in the field including from industry that hopes to connect the gap between the theoretical and practical applications in the areas of reliability and statistical computing and data science.



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.

Autoren/Hrsg.

Pham, Hoang

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Pflege Krankenpflege
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Astronautik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik EDV | Informatik Technische Informatik
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Luft- und Raumfahrttechnik, Luftverkehr

Inhaltsverzeichnis

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.

Über Autor(innen)

Hoang Pham is Distinguished Professor and former Chairman (2007-2013) of the Department of Industrial and Systems Engineering at Rutgers University. Before joining Rutgers in 1993, he was Senior Engineering Specialist with the Idaho National Engineering Laboratory, Idaho Falls, Idaho and Boeing Company in Seattle, Washington. His research areas include reliability modeling and prediction, software reliability, and statistical inference. He is Editor-in-Chief of the International Journal of Reliability, Quality and Safety Engineering and Editor of Springer Series in Reliability Engineering and has been Conference Chair and Program Chair of over 50 international conferences and workshops. Dr. Pham is Author or Co-author of 7 books and has published over 220 journal articles, 100 conference papers, and edited 17 books including Springer Handbook in Engineering Statistics and Handbook in Reliability Engineering. He has delivered over 50 invited keynote and plenary speeches at many international conferences and institutions. His numerous awards include the 2009 IEEE Reliability Society Engineer of the Year Award. He is Fellow of the IEEE, AAIA, and IISE.