LEADER 06315nam 22009255 450 001 9910983346303321 005 20250725140639.0 010 $a9783031726361$b(electronic bk.) 010 $z9783031726354 024 7 $a10.1007/978-3-031-72636-1 035 $a(MiAaPQ)EBC31883550 035 $a(Au-PeEL)EBL31883550 035 $a(CKB)37288857700041 035 $a(DE-He213)978-3-031-72636-1 035 $a(OCoLC)1498496278 035 $a(EXLCZ)9937288857700041 100 $a20250120d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalytics Modeling in Reliability and Machine Learning and Its Applications /$fedited by Hoang Pham 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (480 pages) 225 1 $aSpringer Series in Reliability Engineering,$x2196-999X 311 08$aPrint version: Pham, Hoang Analytics Modeling in Reliability and Machine Learning and Its Applications Cham : Springer,c2025 9783031726354 327 $aPreface -- 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. 330 $aThis book presents novel research and application chapters on topics in reliability, statistics, and machine learning. It has an emphasis on analytical models and techniques and practical applications in reliability engineering, data science, manufacturing, health care, and industry using machine learning, AI, optimization, and other computational methods. Today, billions of people are connected to each other through their mobile devices. Data is being collected and analysed more than ever before. The era of big data through machine learning algorithms, statistical inference, and reliability computing in almost all applications has resulted in a dramatic shift in the past two decades. Data analytics in business, finance, and industry is vital. It helps organizations and business to achieve better results and fact-based decision-making in all aspects of life. The book offers a broad picture of current research on the analytics modeling and techniques and its applications in industry. Topics include: l Reliability modeling and methods. l Software reliability engineering. l Maintenance modeling and policies. l Statistical feature selection. l Big data modeling. l Machine learning: models and algorithms. l Data-driven models and decision-making methods. l Applications and case studies in business, health care, and industrial systems. Postgraduates, researchers, professors, scientists, engineers, and practitioners in reliability engineering and management, machine learning engineering, data science, operations research, industrial and systems engineering, statistics, computer science and engineering, mechanical engineering, and business analytics will find in this book state-of-the-art analytics, modeling and methods in reliability and machine learning. 410 0$aSpringer Series in Reliability Engineering,$x2196-999X 606 $aMachine learning 606 $aComputers 606 $aMedical care 606 $aIndustrial engineering 606 $aProduction engineering 606 $aMathematical optimization 606 $aAerospace engineering 606 $aAstronautics 606 $aMachine Learning 606 $aHardware Performance and Reliability 606 $aHealth Care 606 $aIndustrial and Production Engineering 606 $aOptimization 606 $aAerospace Technology and Astronautics 606 $aAprenentatge automàtic$2thub 606 $aOrdinadors$2thub 606 $aAssistència sanitària$2thub 606 $aEnginyeria industrial$2thub 606 $aOptimització matemàtica$2thub 606 $aAstronàutica$2thub 608 $aLlibres electrònics$2thub 615 0$aMachine learning. 615 0$aComputers. 615 0$aMedical care. 615 0$aIndustrial engineering. 615 0$aProduction engineering. 615 0$aMathematical optimization. 615 0$aAerospace engineering. 615 0$aAstronautics. 615 14$aMachine Learning. 615 24$aHardware Performance and Reliability. 615 24$aHealth Care. 615 24$aIndustrial and Production Engineering. 615 24$aOptimization. 615 24$aAerospace Technology and Astronautics. 615 7$aAprenentatge automàtic 615 7$aOrdinadors 615 7$aAssistència sanitària 615 7$aEnginyeria industrial 615 7$aOptimització matemàtica 615 7$aAstronàutica 676 $a006.31 700 $aPham$b Hoang$028208 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910983346303321 996 $aAnalytics Modeling in Reliability and Machine Learning and Its Applications$94316819 997 $aUNINA