Analytics Modeling In Reliability And Machine Learning And Its Applications
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| Author | : Hoang Pham |
| Publisher | : Springer Nature |
| Total Pages | : 497 |
| Release | : 2021-08-13 |
| Genre | : Technology & Engineering |
| ISBN | : 3030769046 |
This book presents the state-of-the-art methodology and detailed analytical models and methods used to assess the reliability of complex systems and related applications in statistical reliability engineering. It is a textbook based mainly on the author’s recent research and publications as well as experience of over 30 years in this field. The book covers a wide range of methods and models in reliability, and their applications, including: statistical methods and model selection for machine learning; models for maintenance and software reliability; statistical reliability estimation of complex systems; and statistical reliability analysis of k out of n systems, standby systems and repairable systems. Offering numerous examples and solved problems within each chapter, this comprehensive text provides an introduction to reliability engineering graduate students, a reference for data scientists and reliability engineers, and a thorough guide for researchers and instructors in the field.
| Author | : Mangey Ram |
| Publisher | : Elsevier |
| Total Pages | : 431 |
| Release | : 2021-08-12 |
| Genre | : Business & Economics |
| ISBN | : 0128233230 |
Safety and Reliability Modeling and Its Applications combines work by leading researchers in engineering, statistics and mathematics who provide innovative methods and solutions for this fast-moving field. Safety and reliability analysis is one of the most multidimensional topics in engineering today. Its rapid development has created many opportunities and challenges for both industrialists and academics, while also completely changing the global design and systems engineering environment. As more modeling tasks can now be undertaken within a computer environment using simulation and virtual reality technologies, this book helps readers understand the number and variety of research studies focusing on this important topic. The book addresses these important recent developments, presenting new theoretical issues that were not previously presented in the literature, along with solutions to important practical problems and case studies that illustrate how to apply the methodology. Uses case studies from industry practice to explain innovative solutions to real world safety and reliability problems Addresses the full interdisciplinary range of topics that influence this complex field Provides brief introductions to important concepts, including stochastic reliability and Bayesian methods
| Author | : Hoang Pham |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2025-02-11 |
| Genre | : Mathematics |
| ISBN | : 9783031726354 |
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.
| Author | : Vijay Kumar |
| Publisher | : CRC Press |
| Total Pages | : 289 |
| Release | : 2021-01-13 |
| Genre | : Mathematics |
| ISBN | : 1000332861 |
Predictive analytics refers to making predictions about the future based on different parameters which are historical data, machine learning, and artificial intelligence. This book provides the most recent advances in the field along with case studies and real-world examples. It discusses predictive modeling and analytics in reliability engineering and introduces current achievements and applications of artificial intelligence, data mining, and other techniques in supply chain management. It covers applications to reliability engineering practice, presents numerous examples to illustrate the theoretical results, and considers and analyses case studies and real-word examples. The book is written for researchers and practitioners in the field of system reliability, quality, supply chain management, and logistics management. Students taking courses in these areas will also find this book of interest.
| Author | : Kishor S. Trivedi |
| Publisher | : Cambridge University Press |
| Total Pages | : 729 |
| Release | : 2017-08-03 |
| Genre | : Computers |
| ISBN | : 1107099501 |
Learn about the techniques used for evaluating the reliability and availability of engineered systems with this comprehensive guide.
| Author | : Kumar, Pradeep |
| Publisher | : IGI Global |
| Total Pages | : 281 |
| Release | : 2017-03-03 |
| Genre | : Computers |
| ISBN | : 1522525467 |
Constant improvements in technological applications have allowed for more opportunities to develop automated systems. This not only leads to higher success in smart data analysis, but also ensures that technological progression will continue. Ubiquitous Machine Learning and its Applications is a pivotal reference source for the latest research on the issues and challenges machines face in the new millennium. Featuring extensive coverage on relevant areas such as computational advertising, software engineering, and bioinformatics, this publication is an ideal resource for academicians, graduate students, engineering professionals, and researchers interested in discovering how they can apply these advancements to various disciplines.
| Author | : Arnab Kumar Laha |
| Publisher | : Springer Nature |
| Total Pages | : 236 |
| Release | : 2021-06-08 |
| Genre | : Business & Economics |
| ISBN | : 9813366567 |
This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts, and consists of selected presentations at the 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. The book is conceptually divided in seven parts. The first part gives expository briefs on some topics of current academic and practitioner interests, such as data streams, binary prediction and reliability shock models. In the second part, the contributions look at artificial intelligence applications with chapters related to explainable AI, personalized search and recommendation, and customer retention management. The third part deals with credit risk analytics, with chapters on optimization of credit limits and mitigation of agricultural lending risks. In its fourth part, the book explores analytics and data mining in the retail context. In the fifth part, the book presents some applications of analytics to operations management. This part has chapters related to improvement of furnace operations, forecasting food indices and analytics for improving student learning outcomes. The sixth part has contributions related to adaptive designs in clinical trials, stochastic comparisons of systems with heterogeneous components and stacking of models. The seventh and final part contains chapters related to finance and economics topics, such as role of infrastructure and taxation on economic growth of countries and connectedness of markets with heterogenous agents, The different themes ensure that the book would be of great value to practitioners, post-graduate students, research scholars and faculty teaching advanced business analytics courses.
| Author | : Management Association, Information Resources |
| Publisher | : IGI Global |
| Total Pages | : 2174 |
| Release | : 2011-07-31 |
| Genre | : Computers |
| ISBN | : 1609608194 |
"This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets"--Provided by publishe
| Author | : Management Association, Information Resources |
| Publisher | : IGI Global |
| Total Pages | : 1961 |
| Release | : 2020-03-06 |
| Genre | : Science |
| ISBN | : 1799824616 |
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries, including business and healthcare. It is necessary to develop specific software programs that can analyze and interpret large amounts of data quickly in order to ensure adequate usage and predictive results. Cognitive Analytics: Concepts, Methodologies, Tools, and Applications provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques. It also examines the incorporation of pattern management as well as decision-making and prediction processes through the use of data management and analysis. Highlighting a range of topics such as natural language processing, big data, and pattern recognition, this multi-volume book is ideally designed for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, software engineers, IT specialists, and academicians.
| Author | : Vineeth Balasubramanian |
| Publisher | : Newnes |
| Total Pages | : 323 |
| Release | : 2014-04-23 |
| Genre | : Computers |
| ISBN | : 0124017150 |
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
