Militarized Conflict Modeling Using Computational Intelligence

The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that em

This edited volume presents advances in modeling and computational analysis techniques related to networks and online communities. It contains the best papers of notable scientists from the 4th Europe

Like the previous editions also the third edition of this book combines the detailed physical modeling of mechatronic systems and their precise numerical simulation using the Finite Element (FE) metho

The authors begin with basic electromagnetics, such as Maxwell's equations, and present modeling, theory, and computational results. The book systematically exploits interface phenomena, the electrody

Hendrik Herold explores potentials and hindrances of using retrospective geoinformation for monitoring, communicating, modeling, and eventually understanding the complex and gradually evolving process

The increasing complexity of financial problems and the enormous volume of financial data often make it difficult to apply traditional modeling and algorithmic procedures. In this context, the field o

FEM updating allows FEMs to be tuned better to reflect measured data. It can be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. This book

The Finite Difference Time Domain (FDTD) method is an essential tool in modeling inhomogeneous, anisotropic, and dispersive media with random, multilayered, and periodic fundamental (or device) nanost

Integer linear programming (ILP) is a versatile modeling and optimization technique that is increasingly used in non-traditional ways in biology, with the potential to transform biological computation. However, few biologists know about it. This how-to and why-do text introduces ILP through the lens of computational and systems biology. It uses in-depth examples from genomics, phylogenetics, RNA, protein folding, network analysis, cancer, ecology, co-evolution, DNA sequencing, sequence analysis, pedigree and sibling inference, haplotyping, and more, to establish the power of ILP. This book aims to teach the logic of modeling and solving problems with ILP, and to teach the practical 'work flow' involved in using ILP in biology. Written for a wide audience, with no biological or computational prerequisites, this book is appropriate for entry-level and advanced courses aimed at biological and computational students, and as a source for specialists. Numerous exercises and accompanying soft

This book presents a theoretical and practical overview of computational modeling in bioengineering, focusing on a range of applications including electrical stimulation of neural and cardiac tissue,

At a level accessible to introductory students, examines the nature of cognition and approaches to modeling it. Considers the computational nature of cognition and the role it plays in language and ot

This original and timely collection is one of the first books to study neuraldevelopment using computational and mathematical modeling. Modeling provides precise and exact waysof expression, which all

FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the 10th of FLINS

This book analyses intention recognition, an important ability that helps agents predict others’ behavior, in its artificial intelligence and evolutionary computational modeling aspects. It also propo

This detailed book showcases recent advances in computational design of multi-target drug candidates involving various ligand and structure-based strategies. Different chem-bioinformatic modeling stra

This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Colle

Interval Finite Element Method with MATLAB provides a thorough introduction to an effective way of investigating problems involving uncertainty using computational modeling. The well-known and versati

In this book, some of today’s leading neurolinguists and psycholinguists provide insight into the nature of phonological processing using behavioural measures, computational modeling, EEG and fMRI. Th

This text examines in detail mathematical and physical modeling, computational methods and systems for obtaining and analyzing biological structures, using pioneering research cases as examples. As su

This book offers an approach for modeling and investigating economic and social systems using of agent-based simulations and other computational techniques, in order to analyze systems-level outcomes

Handbook of Neuroevolution Through Erlang presents both the theory behind, and the methodology of, developing a neuroevolutionary-based computational intelligence system using Erlang. With a foreword

Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the practical creation and analysis of mathematical algebraic models

"The book is focused on the developments and prospective challenging problems in the area of mind game playing (i.e. playing games that require mental skills) using Computational Intelligence (CI) met

What is the mind? How does it work? How does it influence behavior? Some psychologists hope to answer such questions in terms of concepts drawn from computer science and artificial intelligence. They test their theories by modeling mental processes in computers. This book shows how computer models are used to study many psychological phenomena - including vision, language, reasoning, and learning. It also shows that computer modeling involves differing theoretical approaches. Computational psychologists disagree about some basic questions. For instance, should the mind be modeled by digital computers, or by parallel-processing systems more like brains? Do computer programs consist of meaningless patterns, or do they embody (and explain) genuine meaning?

Creativity is a topic that has traditionally interested psychologists, historians and biographers. Developments in cognitive science and artificial intelligence have provided a powerful computational framework in which creativity can be studied and the creative process can be described and explained. In this book, creativity in technology is discussed using such a computational approach. Using an important historical episode in computer technology as a case study, namely the invention of microprogramming by Maurice Wilkes in 1951, the author presents a plausible explanation of the process by which Wilkes may have arrived at his invention. Based on this case study, the author has also proposed some very general hypotheses concerning creativity that appear to corroborate the findings of some psychologists and historians and then suggests that creative thinking is not significantly different in nature from everyday thinking and reasoning.

Creativity is a topic that has traditionally interested psychologists, historians and biographers. Developments in cognitive science and artificial intelligence have provided a powerful computational framework in which creativity can be studied and the creative process can be described and explained. In this book, creativity in technology is discussed using such a computational approach. Using an important historical episode in computer technology as a case study, namely the invention of microprogramming by Maurice Wilkes in 1951, the author presents a plausible explanation of the process by which Wilkes may have arrived at his invention. Based on this case study, the author has also proposed some very general hypotheses concerning creativity that appear to corroborate the findings of some psychologists and historians and then suggests that creative thinking is not significantly different in nature from everyday thinking and reasoning.

Partial differential equations arise in almost all areas of science, engineering, modeling, and forecasting. During the last two decades pseudospectral methods have emerged as successful alternatives to better known computational procedures, (e.g. finite difference and finite element methods of numerical solution), in several key application areas. These areas include computational fluid dynamics, wave motion, and weather forecasting. This book explains how, when and why this pseudospectral approach works. In order to make the subject accessible to students as well as researchers and engineers, the subject is presented using illustrations, examples, heuristic explanations, and algorithms rather than rigorous theoretical arguments. This book will be of interest to graduate students, scientists and engineers interested in applying pseudospectral methods to real problems.

Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques to the detection, mitigation, treatment, and elimination of the COVID-19 virus around the world. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes. Data Science for COVID-19, Volume 2 extends the coverage of Volume 1 into research on statistical data and applications of Data Science to a variety of perspectives for analysis and understanding of the wide variety of impacts COVID-19 is having on societies and medical practice around the world.

Intractability is a growing concern across the cognitive sciences: while many models of cognition can describe and predict human behavior in the lab, it remains unclear how these models can scale to situations of real-world complexity. Cognition and Intractability is the first book to provide an accessible introduction to computational complexity analysis and its application to questions of intractability in cognitive science. Covering both classical and parameterized complexity analysis, it introduces the mathematical concepts and proof techniques that can be used to test one's intuition of (in)tractability. It also describes how these tools can be applied to cognitive modeling to deal with intractability, and its ramifications, in a systematic way. Aimed at students and researchers in philosophy, cognitive neuroscience, psychology, artificial intelligence, and linguistics who want to build a firm understanding of intractability and its implications in their modeling work, it is an id

Intractability is a growing concern across the cognitive sciences: while many models of cognition can describe and predict human behavior in the lab, it remains unclear how these models can scale to situations of real-world complexity. Cognition and Intractability is the first book to provide an accessible introduction to computational complexity analysis and its application to questions of intractability in cognitive science. Covering both classical and parameterized complexity analysis, it introduces the mathematical concepts and proof techniques that can be used to test one's intuition of (in)tractability. It also describes how these tools can be applied to cognitive modeling to deal with intractability, and its ramifications, in a systematic way. Aimed at students and researchers in philosophy, cognitive neuroscience, psychology, artificial intelligence, and linguistics who want to build a firm understanding of intractability and its implications in their modeling work, it is an id

Modeling atmospheric processes in order to forecast the weather or future climate change is an extremely complex and computationally intensive undertaking. One of the main difficulties is that there are a huge number of factors that need to be taken into account, some of which are still poorly understood. The Factor Separation (FS) method is a computational procedure that helps deal with these nonlinear factors. In recent years many scientists have applied FS methodology to a range of modeling problems, including paleoclimatology, limnology, regional climate change, rainfall analysis, cloud modeling, pollution, crop growth, and other forecasting applications. This book is the first to describe the fundamentals of the method, and to bring together its many applications in the atmospheric sciences. The main audience is researchers and graduate students using the FS method, but it is also of interest to advanced students, researchers, and professionals across the atmospheric sciences.

The second edition of a rigorous and example-driven introduction to topics in economic dynamics that emphasizes techniques for modeling dynamic systems.This text provides an introduction to the modern theory of economic dynamics, with emphasis on mathematical and computational techniques for modeling dynamic systems. Written to be both rigorous and engaging, the book shows how sound understanding of the underlying theory leads to effective algorithms for solving real-world problems. The material makes extensive use of programming examples to illustrate ideas, bringing to life the abstract concepts in the text. Key topics include algorithms and scientific computing, simulation, Markov models, and dynamic programming. Part I introduces fundamentals and part II covers more advanced material. This second edition has been thoroughly updated, drawing on recent research in the field.New for the second edition:“Programming-language agnostic” presentation using pseudocode.New chapter 1 covering