Applications in reliability and statistical computing / / edited by Hoang Pham

• Record Nr.: UNINA9910672439403321
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2023]; ©2023
• ISBN: 9783031212321; 9783031212314
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (310 pages)
• Collana: Springer Series in Reliability Engineering, , 2196-999X
• Disciplina: 004
• Soggetti: Mathematical statistics - Data processing; Computer systems - Reliability; Reliability (Engineering)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 1.Forecasting The Long-Term Growth of S&P 500 Index -- 2.Smart Maintenance and Human Factor Modeling for Aircraft Safety -- 3.Feedback-based algorithm for negotiating human preferences and making risk assessment decisions -- 4.Joining Aspect Detection and Opinion Target Expression based on Multi-Deep Learning Models -- 5.Voting Systems with Supervising Mechanisms -- 6.Assessing the Severity of COVID-19 in the United States -- 7.Promoting expert knowledge for comprehensive human risk management in industrial environments -- 8.Data Quality Assessment for ML Decision-Making -- 9.From Holistic Health to Holistic Reliability – Toward an Integration of Classical Reliability with Modern Big-data Based Health Monitoring -- 10.On the Aspects of Vitamin D and COVID-19 Infections and Modeling Time-delay Body's Immune System With Time-dependent Effects of Vitamin D and Probiotic -- 11.A Staff Scheduling Problem of Customers with Reservations in Consideration With Expected Wait Time of a Customer Without Reservation -- 12.Decision Support System for Ranking of Software Reliability Growth Models -- 13.Human Pose Estimation using Artificial Intelligence -- 14.Neural Network Modeling and What-if Scenarios: Applications for Market Development ; Forecasting -- 15.Mental Health Studies: A Review.
• Sommario/riassunto: This book discusses practical applications of reliability and statistical methods and techniques in various disciplines, using machine learning, artificial intelligence, optimization, and other computation methods. Bringing together research from international experts, each chapter aims to cover both methods and practical aspects on reliability or statistical computations with emphasis on applications. 5G and IoT are set to generate an estimated 1 billion terabytes of data by 2025 and companies continue to search for new techniques and tools that can help them practice data collection effectively in promoting their business. This book explores the era of big data through reliability and statistical computing, showcasing how almost all applications in our daily life have experienced a dramatic shift in the past two decades to a truly global industry. Including numerous illustrations and worked examples, the book is of interest to researchers, practicing engineers, and postgraduate students in the fields of reliability engineering, statistical computing, and machine learning.