Organizational Productivity And Performance Measurements Using Predictive Modeling And Analytics
Download Organizational Productivity And Performance Measurements Using Predictive Modeling And Analytics PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Organizational Productivity And Performance Measurements Using Predictive Modeling And Analytics book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics
Businesses are collecting massive amounts of data every day as a way to better understand their processes, competition, and the markets they serve. This data can be used to increase organizational productivity and performance; however, is essential that organizations collecting large data sets have the tools available to them to fully understand the data they are collecting. Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics takes a critical look at methods for enhancing an organization’s operations and day-to-day activities through the effective use of data. Focusing on a variety of applications of predictive analytics within organizations of all types, this critical publication is an essential resource for business managers, data scientists, graduate-level students, and researchers.
Handbook of Research on Transdisciplinary Knowledge Generation
Traditional methods of viewing the world through the scientific method or instrumental knowledge do not adequately serve the needs of theory, research, and practice within an increasingly complex world. Through transdisciplinary theory, the focus is on a new form of learning and problem solving involving cooperation among different parts of society to meet the complex challenges of society. The Handbook of Research on Transdisciplinary Knowledge Generation is a critical scholarly resource that examines mutual learning across disciplinary lines as a strategy by which to understand the world and apply practical knowledge. Featuring a wide array of topics such as linguistic diversity, medical education, and social constructivism, this book is essential for educational professionals, researchers, students, administrators, and academicians.
Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder
Autism spectrum disorder (ASD) is known as a neuro-disorder in which a person may face problems in interaction and communication with people, amongst other challenges. As per medical experts, ASD can be diagnosed at any stage or age but is often noticeable within the first two years of life. If caught early enough, therapies and services can be provided at this early stage instead of waiting until it is too late. ASD occurrences appear to have increased over the last couple of years leading to the need for more research in the field. It is crucial to provide researchers and clinicians with the most up-to-date information on the clinical features, etiopathogenesis, and therapeutic strategies for patients as well as to shed light on the other psychiatric conditions often associated with ASD. In addition, it is equally important to understand how to detect ASD in individuals for accurate diagnosing and early detection. Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder discusses the early detection and diagnosis of autism spectrum disorder enabled by artificial intelligence technologies, applications, and therapies. This book will focus on the early diagnosis of ASD through artificial intelligence, such as deep learning and machine learning algorithms, for confirming diagnosis or suggesting the need for further evaluation of individuals. The chapters will also discuss the use of artificial intelligence technologies, such as medical robots, for enhancing the communication skills and the social and emotional skills of children who have been diagnosed with ASD. This book is ideally intended for IT specialists, data scientists, academicians, scholars, researchers, policymakers, medical practitioners, and students interested in how artificial intelligence is impacting the diagnosis and treatment of autism spectrum disorder.