Machine Learning Applications In Software Engineering

Heat exchangers are essential in a wide range of engineering applications, including power plants, automobiles, airplanes, process and chemical industries, and heating, air conditioning and refrigerat

The development of nanomaterials opens the possibility for new materials with outstanding properties compared to classical engineering materials. These materials can find applications in different fie

This textbook is aimed at engineering students who are likely to come across magnetics applications in their professional practice. Whether designing lithography equipment containing ferromagnetic bru

This collection on ?Mechanics of Generalized Continua - from Micromechanical Basics to Engineering Applications“ brings together leading scientists in this field from France, Russian Federation, and G

Turbomachinery is a challenging and diverse field, with applications for professionals and students in many subsets of the mechanical engineering discipline, including fluid mechanics, combustion and

Imagine a world where machines can see and understand the world the way humans do. Rapid progress in artificial intelligence has led to smartphones that recognize faces, cars that detect pedestrians, and algorithms that suggest diagnoses from clinical images, among many other applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing. This book introduces the neuroscientific study of neuronal computations in visual cortex alongside of the psychological understanding of visual cognition and the burgeoning field of biologically-inspired artificial intelligence. Topics include the neurophysiological investigation of visual cortex, visual illusions, visual disorders, deep convolutional neural networks, machine learning, and generative adversarial networks among others. It is an ideal resource for students and researchers looking to build bridges across different approaches to studying and developing

This book assists design engineers in making the design trade-offs and the necessary decisions in the various stages of the design in a wide range of engineering applications. The book shall cover the

Automatic control systems have become essential features in virtually every area of technology, from machine tools to aerospace vehicles. This book is a comprehensive, clearly written introduction to automatic control engineering. The author begins with the fundamentals of modelling mechanical, electrical, and electromechanical systems in the state variable format. The emphasis is on classical feedback control theory and design, and their application to practical electromechanical and aerospace problems. Following a careful grounding in classical control theory, the author introduces modern control theory, including digital control and nonlinear system analysis. Over 230 problems help the reader apply principles discussed in the text to practical engineering situations. Engineering students and practising engineers will find all they need to know about control system analysis and design in this valuable text.

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; found

This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in par

The parallel developments of the Finite Element Methods in the 1950’s and the engineering applications of stochastic processes in the 1940’s provided a combined numerical analysis tool for the studies

The ability to mix minute quantities of fluids is critical in a range of recent and emerging techniques in engineering, chemistry and life sciences, with applications as diverse as inkjet printing, ph

Solar radiation data is important for a wide range of applications, e.g. in engineering, agriculture, health sector, and in many fields of the natural sciences. A few examples showing the diversity of

This is the first of two volumes devoted to probability theory in physics, physical chemistry, and engineering, providing an introduction to the problem of the random walk and its applications. In its

This book is a self-contained introduction into stochastic processes with special emphasis on their applications in science, engineering, finance, computer science and operations research. It provides

This book analyzes scientific problems within the history of physics, engineering, chemistry, astronomy and medicine, correlated with technological applications in the social context. When and how is

Written for engineering students, this text provides comprehensive exposition of main methods used in differential equations for both introductory and advanced courses. It includes many applications a

Applied Photochemistry encompasses the major applications of the chemical effects resulting from light absorption by atoms and molecules in chemistry, physics, medicine and engineering, and contains c

A crucial element of structural and continuum mechanics, stability theory has limitless applications in civil, mechanical, aerospace, naval and nuclear engineering. This text of unparalleled scope pre

Ontologies and formal representations of knowledge are extremely powerful tools for modeling and managing large applications in several domains ranging from knowledge engineering, to data mining, to

The most authoritative reference covering nanotechnology usage in medicine. Internal and external applications of nanoscience and vital topics from gene expression to tissue engineering are explored i

This reference reviews applications of optical coherence tomography (OCT) in biology, medicine, engineering, and applied physics. The book summarizes technological advances that have led to the availa

Magnetohydrodynamics (MHD) plays a crucial role in astrophysics, planetary magnetism, engineering and controlled nuclear fusion. This comprehensive textbook emphasizes physical ideas, rather than mathematical detail, making it accessible to a broad audience. Starting from elementary chapters on fluid mechanics and electromagnetism, it takes the reader all the way through to the latest ideas in more advanced topics, including planetary dynamos, stellar magnetism, fusion plasmas and engineering applications. With the new edition, readers will benefit from additional material on MHD instabilities, planetary dynamos and applications in astrophysics, as well as a whole new chapter on fusion plasma MHD. The development of the material from first principles and its pedagogical style makes this an ideal companion for both undergraduate students and postgraduate students in physics, applied mathematics and engineering. Elementary knowledge of vector calculus is the only prerequisite.

Fuzzy classi?ers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scienti?c and business applications. Fuzzy classi?ers use fuzzy rules an

This book teaches the fundamentals of fluid flow by including both theory and the applications of fluid flow in chemical engineering. It puts fluid flow in the context of other transport phenomena suc

Han (polymer engineering, U. of Akron) has written a two-volume opus (the volumes are sold individually) laying out the rheological behavior of polymer materials in v.1 and their applications in polym

This book covers major classes of crystals in the design of novel materials, with an emphasis on advances and applications.* The editor is one of the pioneers in the "design" and "engineering" approac

Carbon nanotubes, with their extraordinary engineering properties, have garnered much attention in the past 10 years. Because of the broad range of potential applications, the scientific community is

Carbon Materials such as nanoparticles, fibres, adamantly- and graphene like structures are widely used in sciences and engineering. Applications range from energy and gasstorage, to electronics and o

This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined

Stem-Cell Nanoengineering reviews the applications of nanotechnology in the fields of stem cells, tissue engineering, and regenerative medicine. Topics addressed include various types of stem cells, u

Active researchers report on the current status of analyzing piezoelectric structures in engineering applications, focusing on particular aspects they are most familiar with. The topics include non-un

"Solar Energy" is an authoritative reference on the design of solar energy systems in building projects, with applications, operating principles, and simple tools for the construction, engineering, an

Due to their unique properties, rubber materials are found in multiple engineering applications such as tires, engine mounts, shock absorbers, flexible joints, seals, etc. Nevertheless, the complex na

This book describes the design of the first functioning single-sided tomograph, the related measurement methods, and a number of applications in medicine, materials science, and chemical engineering.

The coupling of models from different physical domains and the efficient and reliable simulation of multidisciplinary problems in engineering applications are important topics for various fields of en

Highly flexible structures as discussed in this work by Pai (mechanical and aerospace engineering, U. of Missouri-Columbia) are nonlinearly elastic structures designed that have applications from spac

Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics to finance, statistics, management science, and medicine. Whil

A comprehensive introduction and teaching resource for state-of-the-art Qualitative Comparative Analysis (QCA) using R software. This guide facilitates the efficient teaching, independent learning, and use of QCA with the best available software, reducing the time and effort required when encountering not just the logic of a new method, but also new software. With its applied and practical focus, the book offers a genuinely simple and intuitive resource for implementing the most complete protocol of QCA. To make the lives of students, teachers, researchers, and practitioners as easy as possible, the book includes learning goals, core points, empirical examples, and tips for good practices. The freely available online material provides a rich body of additional resources to aid users in their learning process. Beyond performing core analyses with the R package QCA, the book also facilitates a close integration with the R package SetMethods allowing for a host of additional protocols for

There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.