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Applied Genetic Programming and Machine Learning

Applied Genetic Programming and Machine Learning

What do financial data prediction day-trading rule development and bio-marker selection have in common? They are just a few of the tasks that could potentially be resolved with genetic programming and machine learning techniques. Written by leaders in this field Applied Genetic Programming and Machine Learning delineates the extension of Genetic Programming (GP) for practical applications. Reflecting rapidly developing concepts and emerging paradigms this book outlines how to use machine learning techniques make learning operators that efficiently sample a search space navigate the search process through the design of objective fitness functions and examine the search performance of the evolutionary system. It provides a methodology for integrating GP and machine learning techniques establishing a robust evolutionary framework for addressing tasks from areas such as chaotic time-series prediction system identification financial forecasting classification and data mining. The book provides a starting point for the research of extended GP frameworks with the integration of several machine learning schemes. Drawing on empirical studies taken from fields such as system identification finanical engineering and bio-informatics it demonstrates how the proposed methodology can be useful in practical inductive problem solving.

GBP 59.99
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Cost-Sensitive Machine Learning

Cost-Sensitive Machine Learning

In machine learning applications practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting validation and testingCost of collecting features/attributes for test dataCost of user feedback collectionCost of incorrect prediction/classificationCost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

GBP 59.99
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Machine Learning for Neuroscience A Systematic Approach

Machine Learning for Neuroscience A Systematic Approach

This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology cellular neuroscience neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN) clustering and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience. | Machine Learning for Neuroscience A Systematic Approach

GBP 82.99
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Physics of Data Science and Machine Learning

Physics of Data Science and Machine Learning

Physics of Data Science and Machine Learning links fundamental concepts of physics to data science machine learning and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists marrying quantum and statistical mechanics with modern data mining data science and machine learning. It also explains how to integrate these techniques into the design of experiments while exploring neural networks and machine learning building on fundamental concepts of statistical and quantum mechanics. This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians alongside graduate students looking to understand the basic concepts and foundations of data science machine learning and artificial intelligence. Although specifically written for physicists it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid in the development of new and innovative machine learning and artificial intelligence tools. Key Features: Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand adopt and adapt. Free from endless derivations; instead equations are presented and it is explained strategically why it is imperative to use them and how they will help in the task at hand. Illustrations and simple explanations help readers visualize and absorb the difficult-to-understand concepts. Ijaz A. Rauf is an adjunct professor at the School of Graduate Studies York University Toronto Canada. He is also an associate researcher at Ryerson University Toronto Canada and president of the Eminent-Tech Corporation Bradford ON Canada.

GBP 56.99
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Machine Learning in Healthcare Fundamentals and Recent Applications

Machine Learning in Healthcare Fundamentals and Recent Applications

Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services real estate business consumer goods social media etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology variability in medical data small datasets and lack of standard guidelines for application of AI. Therefore the development of new AI/ML tools for various domains of medicine is an ongoing field of research. Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis early detection and diagnosis of disease providing objective-based evidence to reduce human errors curtailing inter- and intra-observer errors risk identification and interventions for healthcare management real-time health monitoring assisting clinicians and patients for selecting appropriate medications and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided along with solved examples and exercises. This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems. | Machine Learning in Healthcare Fundamentals and Recent Applications

GBP 82.99
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The Handbook of Human-Machine Interaction A Human-Centered Design Approach

The Handbook of Human-Machine Interaction A Human-Centered Design Approach

The Handbook of Human-Machine Interaction features 20 original chapters and a conclusion focusing on human-machine interaction (HMI) from analysis design and evaluation perspectives. It offers a comprehensive range of principles methods techniques and tools to provide the reader with a clear knowledge of the current academic and industry practice and debate that define the field. The text considers physical cognitive social and emotional aspects and is illustrated by key application domains such as aerospace automotive medicine and defence. Above all this volume is designed as a research guide that will both inform readers on the basics of human-machine interaction from academic and industrial perspectives and also provide a view ahead at the means through which human-centered designers including engineers and human factors specialists will attempt to design and develop human-machine systems. | The Handbook of Human-Machine Interaction A Human-Centered Design Approach

GBP 52.99
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Text Mining with Machine Learning Principles and Techniques

Text Mining with Machine Learning Principles and Techniques

This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets conclusions which are not normally evident emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject e. g. e-mail service providers online shoppers librarians etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning presenting various algorithms with their use and possibilities and reviews the positives and negatives. Beginning with the initial data pre-processing a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results the book also provides explanations of the algorithms which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources. | Text Mining with Machine Learning Principles and Techniques

GBP 44.99
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Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

Machine learning is an exciting topic with a myriad of applications. However most textbooks are targeted towards computer science students. This however creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields. This textbook bridges this gap providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering targeting senior undergraduate and graduate students in physics and electrical engineering alongside advanced researchers. All codes are available on the author's website: C•Lab (nau. edu) They are also available on GitHub: https://github. com/StxGuy/MachineLearning Key Features: Includes detailed algorithms. Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences. All algorithms are presented with a good mathematical background. | Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

GBP 64.99
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Machine Learning Theory to Applications

Machine Learning Theory to Applications

The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars cognitive decision making communication networks security and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms. In summary this book provides a comprehensive technological path from fundamental theories to the categorization of existing algorithms covers state-of-the-art practical evaluation tools and methods to empower you to use synthetic data to improve the performance of applications. | Machine Learning Theory to Applications

GBP 52.99
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Feature Engineering for Machine Learning and Data Analytics

Feature Engineering for Machine Learning and Data Analytics

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering including feature generation feature extraction feature transformation feature selection and feature analysis and evaluation. The book presents key concepts methods examples and applications as well as chapters on feature engineering for major data types such as texts images sequences time series graphs streaming data software engineering data Twitter data and social media data. It also contains generic feature generation approaches as well as methods for generating tried-and-tested hand-crafted domain-specific features. The first chapter defines the concepts of features and feature engineering offers an overview of the book and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering namely feature selection feature transformation based feature engineering deep learning based feature engineering and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection software management and Twitter-based applications respectively. This book can be used as a reference for data analysts big data scientists data preprocessing workers project managers project developers prediction modelers professors researchers graduate students and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering or as a supplement for courses on machine learning data mining and big data analytics.

GBP 44.99
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Artificial Intelligence and Machine Learning for Business for Non-Engineers

Artificial Intelligence and Machine Learning for Business for Non-Engineers

The next big area within the information and communication technology field is Artificial Intelligence (AI). The industry is moving to automate networks cloud-based systems (e. g. Salesforce) databases (e. g. Oracle) AWS machine learning (e. g. Amazon Lex) and creating infrastructure that has the ability to adapt in real-time to changes and learn what to anticipate in the future. It is an area of technology that is coming faster and penetrating more areas of business than any other in our history. AI will be used from the C-suite to the distribution warehouse floor. Replete with case studies this book provides a working knowledge of AI’s current and future capabilities and the impact it will have on every business. It covers everything from healthcare to warehousing banking finance and education. It is essential reading for anyone involved in industry. | Artificial Intelligence and Machine Learning for Business for Non-Engineers

GBP 56.99
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Electrical Machine Drives Fundamental Basics and Practice

Electrical Machine Drives Fundamental Basics and Practice

This work was developed based on the author's experience of more than 10 years working in research and industry in the areas of electrical drives and industrial automation. Seeking the connection between theory and its applications the author presents a detailed conceptual description with lots of figures and illustrative examples that harmonize the theoretical approach with the practice. Composed of eleven chapters and three appendices the book describes in a dynamic and didactic way the fundamental concepts related to the drives of electric machines. At the end of each chapter is a set of exercises to ease the fixation of the presented content. | Electrical Machine Drives Fundamental Basics and Practice

GBP 48.99
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Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry

Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry

Today raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML) this data can be used to gain meaningful insights. In addition as data is the new raw material for today’s world AI and ML will be applied in every industrial sector. Industry 4. 0 mainly focuses on the automation of things. From that perspective the oil and gas industry is one of the largest industries in terms of economy and energy. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors namely upstream midstream and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML ranging from exploration data management extraction processing real-time data analysis monitoring cloud-based connectivity system and conditions analysis to the final delivery of the product to the end customer while taking into account the incorporation of the safety measures for a better operation and the efficient and effective execution of operations. This book explores the variety of applications that can be integrated to support the existing petroleum and adjacent sectors to solve industry problems. It will serve as a useful guide for professionals working in the petroleum industry industrial engineers AI and ML experts and researchers as well as students.

GBP 82.99
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Ghosts in the Machine Rethinking Learning Work and Culture in Air Traffic Control

Ghosts in the Machine Rethinking Learning Work and Culture in Air Traffic Control

This book provides a socio-cultural analysis of the ways in which air traffic controllers formally and informally learn about their work and the active role that organisational cultures play in shaping interpretation and meaning. In particular it describes the significant role that organizational cultures have played in shaping what is valued by controllers about their work and its role as a filter in enabling or constraining conscious inquiry. The premise of the book is that informal learning is just as important in shaping what people know and value about their work and that this area is frequently overlooked. By using an interpretative research approach the book highlights the ways in which the social structure of work organisation culture and history interweaves with learning work to guide and shape what is regarded by controllers as important and what is not. It demonstrates how this social construction is quite different from a top-down corporate culture approach. Technological and organizational reform is leading to changes in work practice and to changes in relationships between workers within the organization. These have implications for anyone wishing to understand the dynamics of organizational life. As such this study provides insights into many of the changes that are occurring in the nature of work in many different industries. Previous research into learning in air traffic control has centred largely on cognitive individual performance performance within teams or more recently on performance at a systems level. By tracing the role of context in shaping formal and informal learning this book shows why interventions at these levels sometimes fail. | Ghosts in the Machine Rethinking Learning Work and Culture in Air Traffic Control

GBP 52.99
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Digital Signal Processing in Audio and Acoustical Engineering

Digital Signal Processing in Audio and Acoustical Engineering

Starting with essential maths fundamentals of signals and systems and classical concepts of DSP this book presents from an application-oriented perspective modern concepts and methods of DSP including machine learning for audio acoustics and engineering. Content highlights include but are not limited to room acoustic parameter measurements filter design codecs machine learning for audio pattern recognition and machine audition spatial audio array technologies and hearing aids. Some research outcomes are fed into book as worked examples. As a research informed text the book attempts to present DSP and machine learning from a new and more relevant angle to acousticians and audio engineers. Some MATLAB® codes or frameworks of algorithms are given as downloads available on the CRC Press website. Suggested exploration and mini project ideas are given for proof of concept type of exercises and directions for further study and investigation. The book is intended for researchers professionals and senior year students in the field of audio acoustics. | Digital Signal Processing in Audio and Acoustical Engineering

GBP 44.99
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Diseases of Commercial Crops and Their Integrated Management

Diseases of Commercial Crops and Their Integrated Management

This volume compiles information on different aspects of diseases of commercial crops and their management. It contains 17 chapters based on different crops contributed by various authors. The book will be helpful for the students pursuing their degree in Agricultural Sciences growers teachers extension personnel and fellow researchers in their respective fields. | Diseases of Commercial Crops and Their Integrated Management

GBP 77.99
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Vibro-Acoustics Fundamentals and Applications

Vibro-Acoustics Fundamentals and Applications

The subject of vibro-acoustics is important for the design of machine elements and structures to minimize sound generated by them. For better machine designing it is necessary for machine designers (mechanical engineers) to have a thorough knowledge of vibro-acoustics. Furthermore since the design cycles of machines have become shorter designers will have to design quiet machines at the drawing-board stage rather than applying band-aid techniques after the machine has been built. Although there is common ground in the treatment of acoustics the subject of vibration is not very fortunate. Those interested in low-frequency vibration are generally concerned with the modal approach of using natural frequencies and mode shapes whereas those interested in vibro-acoustics in medium and high frequencies are generally concerned with the wave approach. Since both modal and wave approaches have their advantages it is a good idea to study both together to get the best out of them. This is useful for a better understanding the physics of vibro-acoustics. Written for students and professionals interested in gaining knowledge this book systematically integrates the relevant aspects of vibro-acoustics from various viewpoints. | Vibro-Acoustics Fundamentals and Applications

GBP 44.99
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Micro Electro Discharge Machining Principles and Applications

Micro Electro Discharge Machining Principles and Applications

Micro Electro Discharge Machining (EDM) is a prominent technology for the fabrication of micro components in many fields. Nowadays it is used like a conventional machine tool due to favorable characteristics. This book provides the fundamental knowledge of the principles of the process and its variants the different process parameters the role of machine components and systems the challenges and how to eliminate processing errors. It also includes real life applications of micro EDM in different areas with the most relevant examples. | Micro Electro Discharge Machining Principles and Applications

GBP 44.99
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Design of Internet of Things

Design of Internet of Things

The text provides a comprehensive overview of the design aspects of the internet of things devices and covers the fundamentals of big data and data science. It explores various scenarios such as what are the middleware and frameworks available and how to build a stable standards-based and Secure internet of things device. It discusses important concepts including embedded programming techniques machine-to-machine architecture and the internet of things for smart city applications. It will serve as an ideal design book for professionals senior undergraduate and graduate students in the fields including electrical engineering electronics and communication engineering and computer engineering. The book- Covers applications and architecture needed to deliver solutions to end customers and readers. Discusses practical aspects of implementing the internet of things in diverse areas including manufacturing and software development. Highlights big data concepts and embedded programming techniques. Presents technologies including machine to machine integrated sensors and radio-frequency identification. Introduces global system for mobile communication and precise details of standards based on internet of things architecture models. The book focuses on practical design aspects such as how to finalize a processor integrated circuit which operating system to use etc. in a single volume. It will serve as an ideal text for professionals senior undergraduate and graduate students in diverse engineering domains including electrical electronics and communication computer. | Design of Internet of Things

GBP 44.99
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Basic Cost Engineering

Basic Cost Engineering

This work focuses on the application of fundamental cost engineering principles to the capital and operating costs estimation of major projects. It provides detailed coverage of profitability risk and sensitivity analysis. This third edition: discusses novel strategies for calculating preliminary estimates using MasterFormat; presents new information on estimating the retrofitting and extension of existing plants; contains current international cost data; and more. ;A solutions manual is available to instructors only. | Basic Cost Engineering

GBP 59.99
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Intelligent Systems for Engineers and Scientists A Practical Guide to Artificial Intelligence

Intelligent Systems for Engineers and Scientists A Practical Guide to Artificial Intelligence

The fourth edition of this bestselling textbook explains the principles of artificial intelligence (AI) and its practical applications. Using clear and concise language it provides a solid grounding across the full spectrum of AI techniques so that its readers can implement systems in their own domain of interest. The coverage includes knowledge-based intelligence computational intelligence (including machine learning) and practical systems that use a combination of techniques. All the key techniques of AI are explained—including rule-based systems Bayesian updating certainty theory fuzzy logic (types 1 and 2) agents objects frames symbolic learning case-based reasoning genetic algorithms and other optimization techniques shallow and deep neural networks hybrids and the Lisp Prolog and Python programming languages. The book also describes a wide range of practical applications in interpretation and diagnosis design and selection planning and control. Fully updated and revised Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence Fourth Edition features: A new chapter on deep neural networks reflecting the growth of machine learning as a key technique for AI A new section on the use of Python which has become the de facto standard programming language for many aspects of AI The rule-based and uncertainty-based examples in the book are compatible with the Flex toolkit by Logic Programming Associates (LPA) and its Flint extension for handling uncertainty and fuzzy logic. Readers of the book can download this commercial software for use free of charge. This resource and many others are available at the author’s website: adrianhopgood. com. Whether you are building your own intelligent systems or you simply want to know more about them this practical AI textbook provides you with detailed and up-to-date guidance. | Intelligent Systems for Engineers and Scientists A Practical Guide to Artificial Intelligence

GBP 59.99
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Multivariate Calculus

Multivariate Calculus

This book is a compilation of all basic topics on functions of Several Variables and is primarily meant for undergraduate and post graduate students. Topics covered are: Limits continuities and differentiabilities of functions of several variables. Properties of Implicit functions and Jacobians. Extreme values of multivariate functions. Various types of integrals in planes and surfaces and their related theorems including Dirichlet and Liouville’s extension to Dirichlet. Print edition not for sale in South Asia (India Sri Lanka Nepal Bangladesh Pakistan or Bhutan) | Multivariate Calculus

GBP 82.99
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