Desheng Wu – författare

Olson & Desheng Wu 2 The Human Reaction to Risk and Opportunity . Koenig Part II ERM Perspectives 3 Enterprise Risk Management: Financial and Accounting Perspectives . Olson 4 An Empirical Study on Enterprise Risk Management in Insurance . 39 Madhusudan Acharyya 5 Supply Chain Risk Management .

Olson & Desheng Wu 2 The Human Reaction to Risk and Opportunity . Koenig Part II ERM Perspectives 3 Enterprise Risk Management: Financial and Accounting Perspectives . Olson 4 An Empirical Study on Enterprise Risk Management in Insurance . 39 Madhusudan Acharyya 5 Supply Chain Risk Management .

This textbook, now in its fourth edition, serves as a comprehensive guide to learning various aspects of risk, encompassing supply chain management, artificial intelligence, and sustainability.

This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold.

This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems.  Data mining includes descriptive and predictive modeling.  Operations research includes all three. This book focuses on prescriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second editionprovides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling.  Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting.  Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.

This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling.

This book aims to stay one step beyond the innovations of information and communication technologies and smart healthcare management and provides an overview of the risks smart healthcare management could help to alleviate, and those risks it would create or amplify.

This book aims to stay one step beyond the innovations of information and communication technologies and smart healthcare management and provides an overview of the risks smart healthcare management could help to alleviate, and those risks it would create or amplify.