Guide To Computational Modelling For Decision Processes
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Guide to Computational Modelling for Decision Processes
This interdisciplinary reference and guide provides an introduction to modeling methodologies and models which form the starting point for deriving efficient and effective solution techniques, and presents a series of case studies that demonstrate how heuristic and analytical approaches may be used to solve large and complex problems. Topics and features: introduces the key modeling methods and tools, including heuristic and mathematical programming-based models, and queueing theory and simulation techniques; demonstrates the use of heuristic methods to not only solve complex decision-making problems, but also to derive a simpler solution technique; presents case studies on a broad range of applications that make use of techniques from genetic algorithms and fuzzy logic, tabu search, and queueing theory; reviews examples incorporating system dynamics modeling, cellular automata and agent-based simulations, and the use of big data; supplies expanded descriptions and examples in the appendices.
Automatic Control, Robotics, and Information Processing
This book presents a wide and comprehensive range of issues and problems in various fields of science and engineering, from both theoretical and applied perspectives. The desire to develop more effective and efficient tools and techniques for dealing with complex processes and systems has been a natural inspiration for the emergence of numerous fields of science and technology, in particular control and automation and, more recently, robotics. The contributions gathered here concern the development of methods and algorithms to determine best practices regarding broadly perceived decisions or controls. From an engineering standpoint, many of them focus on how to automate a specific process or complex system. From a tools-based perspective, several contributions address the development of analytic and algorithmic methods and techniques, devices and systems that make it possible to develop and subsequently implement the automation and robotization of crucial areas of human activity. All topics discussed are illustrated with sample applications.
Computational Intelligence for Genomics Data
Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers. - Provides comparative analysis of machine learning and deep learning methods in the analysis of genomic data, discussing major design challenges, best practices, pitfalls, and research potential - Explores machine and deep learning techniques applied to dimensionality reduction, feature extraction, data selection, and their application in genomics - Presents case studies of various diseases based on gene microarray expression data, including cancer, liver disorders, neuromuscular disorders, and neurodegenerative disorders