Predictive analytics: modeling and optimization

In: Advanced research in reliability and system assurance engineering

In: Taylor & Francis eBooks

Abstract

Chapter 1 Role of MCDM in Software Reliability Engineering Chapter 2 Fault Tree Analysis of a Computerized Numerical Control Turning Center Chapter 3 How to Schedule Elective Patients in Hospitals to Gain Full Utilization of Resources and Eliminate Patient Overcrowding Chapter 4 Reducing the Deterioration Rate of Inventory through Preservation Technology Investment under Fuzzy and Cloud Fuzzy Environment Chapter 5 Image Formation Using Deep Convolutional Generative Adversarial Networks Chapter 6 Optimal Preservation Technology Investment and Price for the Deteriorating Inventory Model with Price-Sensitivity Stock- Dependent Demand Chapter 7 EOQ with Shortages and Learning Effect Chapter 8 Optimal Production-Inventory Policies for Processed Fruit Juices Manufacturer and Multi-retailers with Trended Demand and Quality Degradation Chapter 9 Information Visualization: Perception and Limitations for Data-Driven Designs Chapter 10 IoT, Big Data, and Analytics -- Challenges and Opportunities Chapter 11 Multiple-Criteria Decision Analysis Using VLSI Global Routing Chapter 12 Application of IoT in Water Supply Management Chapter 13 A Hybrid Approach for Video Indexing Using Computer Vision and Speech Recognition Chapter 14 Statistical Methodology for Software Reliability with Environmental Factors Chapter 15 Maintenance Data-Trends Based Reliability Availability and Maintainability (RAM) Assessment of a Steam Boiler

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Predictive analytics: modeling and optimization

In: Advanced research in reliability and system assurance engineering

Kumar, Vijay; Ram, Mangey

Kumar, Vijay; Ram, Mangey

Abstract

"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, introduces current achievements and applications of AI, 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"--

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