Condition Monitoring Using Computational Intelligence Methods

This book offers an introduction to current methods in computational modeling in neuroscience. The book describes realistic modeling methods at levels of complexity ranging from molecular interaction

Big Data is made up of lots of little data: numbers entered into cell phones, addresses entered into GPS devices, visits to websites, online purchases, ATM transactions,

A comprehensive overview of OpenMP, the standard application programming interface for shared memory parallel computing—a reference for students and professionals.

This book presents a variety of computational methods used to solve dynamic problems in economics and finance. It emphasizes practical numerical methods rather than mathematical proofs and focuses on

The use of innovative computational models in political economic research as a complement to traditional analytical methodologies. Researchers are increasingly turning to computational methods to st

The new edition of an introduction to the art of computational problem solving using Python.This book introduces students with little or no prior programming experience to the art of computational pro

This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using

Policy makers need quantitative as well as qualitative answers to pressing policy questions. Because of advances in computational methods, quantitative estimates are now derived from coherent nonline

Researchers are increasingly turning to computational methods to study the dynamic properties of political and economic systems. Politicians, citizens, interest groups, and organizations interact in d

A guide to computational thinking education, with a focus on artificial intelligence literacy and the integration of computing and physical objects. Computing has become an essential part of today’s primary and secondary school curricula. In recent years, K–12 computer education has shifted from computer science itself to the broader perspective of computational thinking (CT), which is less about technology than a way of thinking and solving problems―“a fundamental skill for everyone, not just computer scientists,” in the words of Jeanette Wing, author of a foundational article on CT. This volume introduces a variety of approaches to CT in K–12 education, offering a wide range of international perspectives that focus on artificial intelligence (AI) literacy and the integration of computing and physical objects. The book first offers an overview of CT and its importance in K–12 education, covering such topics as the rationale for teaching CT; programming as a general problem-solving

Why a new approach is needed in the quest for general artificial intelligence.Since the inception of artificial intelligence, we have been warned about the imminent arrival of computational systems th

Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage.In The AI Advantage, Thomas Davenport offers a guide to using artificial in

What can artificial intelligence teach us about the mind? If AI's underlying concept is that thinking is a computational process, then how can computation illuminate thin

This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides student

There are many excellent computational biology resources now available for learning about methods that have been developed to address specific biological systems, but comparatively little attention h

Using qualitative methods to deal with imperfect information. In this book Simon Parsons describes qualitative methods for reasoning under uncertainty, "uncertainty" being a catch-all term for vario

The first introductory textbook in the emerging, fast-developing field of computational psychiatry.Computational psychiatry applies computational modeling and theoretical approaches to psychiatric que

A book at the intersection of data science and media studies, presenting concepts and methods for computational analysis of cultural data. How can we see a billion images? What analytical methods can

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learnin

Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage.

This book offers a practical guide to the computational methods at the heart of most modern quantitative research. It will be essential reading for research assistants needing hands-on experience; stu

The interplay between optimization and machine learning is one of the most importantdevelopments in modern computational science. Optimization formulations and methods are proving tobe vital in design

Using the tools of information technology to understand the molecular machinery ofthe cell offers both challenges and opportunities to computational scientists. Over the past decade,novel algorithms h

Computational systems biology unifies the mechanistic approach of systems biologywith the data-driven approach of computational biology. Computational systems biology aims todevelop algorithms that un

New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally,

An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments. Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills. After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and

Examining the potential benefits and risks of using artificial intelligence to advance global sustainability.Drones with night vision are tracking elephant and rhino poachers in African wildlife parks

An introduction to computational thinking that traces a genealogy beginning centuries before the digital computer.A few decades into the digital era, scientists discovered that thinking in terms of co

Detailed descriptions of detection, direction-finding, and signal-estimation methods, using consistent formalisms and notation, emphasizing HF antenna array sensing applications.Adaptive antenna array

This book presents evidence for a universal word order constraint, the Final-over-Final Condition (FOFC), and discusses the theoretical implications of this phenomenon. FOFC is a syntactic condition t

Intelligence does not arise only in individual brains; it also arises in groups of individuals. This is collective intelligence: groups of individuals acting collectively in ways that seem intelligent

In Monitoring Movements in Development Aid, Casper Jensen and BritWinthereik consider the processes, social practices, and infrastructures that are emerging tomonitor development aid, discussing both

Computational finance, an exciting new cross-disciplinary research area, draws extensively on the tools and techniques of computer science, statistics, information systems, and financial economics. Th

This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. Computational finance, an excit

Qualitative reasoning is one of the most vigorous areas in artificial intelligence. This book presents, within a conceptually unified theoretical framework, a body of methods that have been developed

Emphasizing issues of computational efficiency, Michael Kearns andUmesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intel

Reflecting Alan Robinson's fundamental contribution to computational logic, this book brings together seminal papers in inference, equality theories, and logic programming. It is an exceptional collec

An expert offers a guide to where we should use artificial intelligence―and where we should not.Before we know it, artificial intelligence (AI) will work its way into every corner of our lives, making decisions about, with, and for us. Is this a good thing? There’s a tendency to think that machines can be more “objective” than humans―can make better decisions about job applicants, for example, or risk assessments. In Awkward Intelligence, AI expert Katharina Zweig offers readers the inside story, explaining how many levers computer and data scientists must pull for AI’s supposedly objective decision making. She presents the good and the bad: AI is good at processing vast quantities of data that humans cannot―but it’s bad at making judgments about people. AI is accurate at sifting through billions of websites to offer up the best results for our search queries and it has beaten reigning champions in games of chess and Go. But, drawing on her own research, Zweig shows how inac

A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication.Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but in terms of artificial communication.To do this, we need a concept of communication that can take into account the possibility that a communication partner may be not a human being but an al

An argument that-despite dramatic advances in the field-artificial intelligence is nowhere near developing systems that are genuinely intelligent. In this provocative book, Brian Cantwell Smith argu