Predictive Analytics of Psychological Disorders in Healthcare, Mittal
Автор: Dinsmore Thomas W. Название: Disruptive Business Analytics ISBN: 1484213122 ISBN-13(EAN): 9781484213124 Издательство: Springer Рейтинг: Цена: 3492.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Learn all you need to know about seven key innovations disrupting business analytics today. These innovations—the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics—are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities.Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization.Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today.What You'll LearnDiscover how the open source business model works and how to make it work for youSee how cloud computing completely changes the economics of analyticsHarness the power of Hadoop and its ecosystemFind out why Apache Spark is everywhereDiscover the potential of streaming and real-time analyticsLearn what Deep Learning can do and why it mattersSee how self-service analytics can change the way organizations do business
Who This Book Is For
Corporate actors at all levels of responsibility for analytics: analysts, CIOs, CTOs, strategic decision makers, managers, systems architects, technical marketers, product developers, IT personnel, and consultants.
Описание: This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis.
Автор: Roy Sudipta, Goyal Lalit Mohan, Mittal Mamta Название: Advanced Prognostic Predictive Modelling in Healthcare Data Analytics ISBN: 9811605408 ISBN-13(EAN): 9789811605406 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis.
Автор: Burk, Scott , Miner, Gary Название: It`s All Analytics! ISBN: 0367359685 ISBN-13(EAN): 9780367359683 Издательство: Taylor&Francis Рейтинг: Цена: 9033.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject.
Автор: Prasant Kumar Pattnaik Название: Smart Healthcare Analytics in IoT Enabled Environment ISBN: 3030375501 ISBN-13(EAN): 9783030375508 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book addresses various aspects of how smart healthcare can be used to detect and analyze diseases, the underlying methodologies, and related security concerns.
Автор: Short Название: Simulation Theory ISBN: 1138816051 ISBN-13(EAN): 9781138816053 Издательство: Taylor&Francis Рейтинг: Цена: 22968.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Theory of Mind (ToM) is the term used for our ability to predict and explain the behaviour of ourselves and others. Accounts of this theory have so far fallen into two competing types: Simulation Theory and 'Theory Theory'. In contrast with Theory Theory, Simulation Theory argues that we predict behaviour not by employing a model of people, but by replicating others' thoughts and feelings. This book presents a novel defence of Simulation Theory, reviewing the major challenges against it and positing the theory as the most effective method for exploring how we know each other and ourselves.
Drawing on key research in the field, chapters reopen the debates surrounding Theory of Mind and cover a variety of topics including schizophrenia with implications for experimental social psychology. In the past, one of the greatest criticisms against Simulation Theory is that it cannot explain systematic error in Theory of Mind. This book explores the rapidly developing heuristics and biases programme, pioneered by Kahneman and Tversky, to suggest that a novel bias mismatch defence available to Simulation Theory explains these systematic errors.
Simulation Theory: A psychological and philosophical consideration will appeal to a range of researchers and academics, including psychologists from the fields of cognitive, social and developmental psychology, as well as philosophers, psychotherapists and practitioners looking for further research on Theory of Mind. The book will also be of relevance to those interested in autism, since it offers a new approach to Theory of Mind which explains central symptoms in autistic subjects.
Автор: Short Название: Simulation Theory ISBN: 1138294349 ISBN-13(EAN): 9781138294349 Издательство: Taylor&Francis Рейтинг: Цена: 7961.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Theory of Mind (ToM) is the term used for our ability to predict and explain the behaviour of ourselves and others. Accounts of this theory have so far fallen into two competing types: Simulation Theory and 'Theory Theory'. In contrast with Theory Theory, Simulation Theory argues that we predict behaviour not by employing a model of people, but by replicating others' thoughts and feelings. This book presents a novel defence of Simulation Theory, reviewing the major challenges against it and positing the theory as the most effective method for exploring how we know each other and ourselves.
Drawing on key research in the field, chapters reopen the debates surrounding Theory of Mind and cover a variety of topics including schizophrenia with implications for experimental social psychology. In the past, one of the greatest criticisms against Simulation Theory is that it cannot explain systematic error in Theory of Mind. This book explores the rapidly developing heuristics and biases programme, pioneered by Kahneman and Tversky, to suggest that a novel bias mismatch defence available to Simulation Theory explains these systematic errors.
Simulation Theory: A psychological and philosophical consideration will appeal to a range of researchers and academics, including psychologists from the fields of cognitive, social and developmental psychology, as well as philosophers, psychotherapists and practitioners looking for further research on Theory of Mind. The book will also be of relevance to those interested in autism, since it offers a new approach to Theory of Mind which explains central symptoms in autistic subjects.
Описание: Recent years have witnessed important advancements in our understanding of the psychological underpinnings of subjective properties of visual information, such as aesthetics, memorability, or induced emotions. Concurrently, computational models of objective visual properties such as semantic labelling and geometric relationships have made significant breakthroughs using the latest achievements in machine learning and large-scale data collection. There has also been limited but important work exploiting these breakthroughs to improve computational modelling of subjective visual properties. The time is ripe to explore how advances in both of these fields of study can be mutually enriching and lead to further progress. This book combines perspectives from psychology and machine learning to showcase a new, unified understanding of how images and videos influence high-level visual perception - particularly interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, and creativity. These human-based metrics are interesting for a very broad range of current applications, ranging from content retrieval and search, storytelling, to targeted advertising, education and learning, and content filtering. Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, using state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the tasks are by nature highly challenging and multifaceted, requiring thought on both the psychological implications of the human concepts, as well as their translation to machines.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Автор: Fusheng Wang; Lixia Yao; Gang Luo Название: Data Management and Analytics for Medicine and Healthcare ISBN: 3319577409 ISBN-13(EAN): 9783319577401 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2016, in New Delhi, India, in September 2016, held in conjunction with the 42nd International Conference on Very Large Data Bases, VLDB 2016.
Автор: Valentine Fontama; Roger Barga; Wee Hyong Tok Название: Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition ISBN: 1484212010 ISBN-13(EAN): 9781484212011 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models.
Автор: Fuentes Alvaro Название: Hands-On Predictive Analytics with Python ISBN: 178913871X ISBN-13(EAN): 9781789138719 Издательство: Неизвестно Цена: 9010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will teach you all the processes you need to build a predictive analytics solution: understanding the problem, preparing datasets, exploring relationships, model building, tuning, evaluation, and deployment. You`ll earn to use Python and its data analytics ecosystem to implement the main techniques used in real-world projects.
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