Machine Learning Applications In Software Engineering

This book provides an introduction to fuzzy logic and surveys emergingresearch trends and the application of state-of-the-art fuzzy hybridcomputing techniques in the field of construction engineering

Covers multivariable calculus, starting from the basics and leading up to the three theorems of Green, Gauss, and Stokes, but always with an eye on practical applications. Written for a wide spectrum

This book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and eng

This updated and revised first-course textbook in applied probability provides a contemporary and lively post-calculus introduction to the subject of probability. The exposition reflects a desirable b

Through extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a

With applications ranging from medical diagnostics to environmental monitoring, molecular sensors (also known as biosensors, chemical sensors, or chemosensors), along with emerging nanotechnologies of

"This book presents traditional and contemporary applications of graph theory in the areas of industrial engineering, management science and applied operations research"--

Industrial engineering is a branch of engineering dealing with the optimization of complex processes or systems. It is concerned with the development, improvement, implementation and evaluation of pro

Describing the principles and applications of single-input, single output and multivariable predictive control in a simple and lively manner, this practical book discusses topics such as the handling

Surface coatings is a rapidly developing field of science and technology that offers new methods and techniques to control friction and wear. New coating types are continually being developed and the

A straightforward, practical guide to extreme value modeling for today's world Measuring and interpreting data for extreme values presents a unique and important challenge that has far-reaching impli

Iterative Computer Algorithms with Applications in Engineering describes in-depth the five main iterative algorithms for solving hard combinatorial optimization problems: Simulated Annealing, Genetic

This is the fourth edition of an established textbook of chemical thermodynamics used by university and technical college students of chemistry and chemical engineering. The text covers the same ground as previous editions, presenting the general theory of chemical equilibrium, including its statistical development, and illustrating its many applications in the laboratory and industry. This edition has been extensively revised in the light of recent contributions to the literature. Many new references have been added; the re-writing of certain passages, especially of those concerning the statistical interpretation of entropy and the present understanding of order-disorder transitions, also reflects changes of emphasis.

Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like th

This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including re

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering