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Nutrition and Exercise Immunology

Nutrition and Exercise Immunology

Like an army of millions ready to defend its territory the human immune system acts as the body's primary line of defense-a complex network of interacting cells that protects us from pathogens and other foreign substances. But many components of the immune system exhibit change after prolonged heavy exertion indicating that it is suppressed and stressed albeit transiently following prolonged endurance exercise. For marathon runners distance swimmers and any other endurance athlete who undergoes repeated cycles of heavy exertion a weakened immune system could lead to health complications such as respiratory infection. As a result interest in various nutrient supplements with the potential to counter exercise-induced immunosuppression has grown. Nutrition and Exercise Immunology reviews the link between nutrition and immune function with special application to athletic endeavor. Written by respected researchers in sports medicine and exercise immunology this text covers topics such as carbohydrates and the immune response to prolonged exertion; protein exercise and immunity; and vitamins immunity and infection risk in athletes. It also takes a look at future directions in nutrition and exercise immunology. For sports medicine professionals dietitians nutritionists exercise immunologists as well as endurance athletes Nutrition and Exercise Immunology provides an important and in-depth look into this exciting new area of scientific research.

GBP 59.99
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Sport and Exercise Medicine An Essential Guide

Sport and Exercise Medicine An Essential Guide

The complete guide for all candidates studying and working in the field of sport and exercise medicine including higher specialist training and post graduate examinations. This revision guide covers all key elements of the UK National Curriculum in Sport and Exercise Medicine. Key features to facilitate learning include: A pictorial summary at the start of each chapter ‘Clinical consideration’ sections that show how knowledge can be applied to working clinical practice MCQ questions for each chapter including answers MFSEM examination candidates MSc students in sport and exercise medicine sport and exercise medicine specialist trainee doctors physiotherapists and general practitioners with an extended role in musculoskeletal or sport and exercise medicine will all benefit from this new text. | Sport and Exercise Medicine An Essential Guide

GBP 59.99
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The Exercise Effect on Mental Health Neurobiological Mechanisms

The Exercise Effect on Mental Health Neurobiological Mechanisms

The Exercise Effect on Mental Health contains the most recent and thorough overview of the links between exercise and mental health and the underlying mechanisms of the brain. The text will enhance interested clinicians’ and researchers’ understanding of the neurobiological effect of exercise on mental health. Editors Budde and Wegner have compiled a comprehensive review of the ways in which physical activity impacts the neurobiological mechanisms of the most common psychological and psychiatric disorders including depression anxiety bipolar disorder and schizophrenia. This text presents a rigorously evidence-based case for exercise as an inexpensive time-saving and highly effective treatment for those suffering from mental illness and distress. | The Exercise Effect on Mental Health Neurobiological Mechanisms

GBP 69.99
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Sport and Exercise Medicine OSCEs An Essential Revision Guide

Sport and Exercise Medicine OSCEs An Essential Revision Guide

As the only text on the market for Sport and Exercise Medicine objective structured clinical examinations (OSCEs) this is an invaluable guide for those studying sport and exercise medicine and sitting examinations. Whilst specifically targeted at the OSCE this book will have much wider appeal throughout the clinical setting. Sport and Exercise Medicine is a new and developing specialty and there has been a rapid increase in the number of universities offering MSc degrees in Sport and Exercise Medicine and more candidates year on the year sitting the Faculty of Sport and Exercise Medicine (FSEM) diploma exam. It is a niche specialty with very little content taught at an undergraduate level therefore knowing the material that is examined is often challenging compared with other specialties. This book provides the content to fill that need. The book is divided into sections based on the main topics that arise in sport and exercise medicine OSCEs with both core knowledge and practical tips to ensure a fluid and confident performance by the examinee in every station. A wide range of readers will benefit from this book including those about to sit a postgraduate examination in sport and exercise medicine or those enrolled in a postgraduate certificate or master’s course in the specialty. It will also be useful to doctors and allied healthcare professionals such as physiotherapists sports therapists podiatrists and nurses as well as undergraduate students. Further the young clinician who is just starting out and wants to build their confidence in musculoskeletal assessment and presenting to seniors will gain much from reading this book. | Sport and Exercise Medicine OSCEs An Essential Revision Guide

GBP 51.99
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The Ups and Downs in Drug Design Adventures in Medicinal Chemistry

The Ups and Downs in Drug Design Adventures in Medicinal Chemistry

The Ups and Downs in Drug Design: Adventures in Medicinal Chemistry highlights the necessity for an integrative approach in medicinal chemistry and chemical biology. As medicinal chemistry is not a monolithic science it is important to emphasize the other various disciplines that are required for successful drug design. This book presents the author’s own personal experience in this field and describes the ups and downs that come with drug discovery. It is an excellent companion text for graduate and postgraduate students who would like further insight into the parameters of drug design including the challenges that come with the project. Key Features Illustrates real-life examples in medicinal chemistry Integrates the use of physical chemical and biological concepts that are important in drug design Highlights the ups and downs that come with drug discovery Aims to inspire students who may be struggling with the challenges and thought process in drug design Intends to be an excellent companion text for graduate and postgraduate students | The Ups and Downs in Drug Design Adventures in Medicinal Chemistry

GBP 105.00
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Innovation Commercialization and Start-Ups in Life Sciences

Innovation Commercialization and Start-Ups in Life Sciences

Innovation is a translation of a new method idea or product into reality and profit. It is a process of connected steps that accumulates into a brand reputation required for success. Unlike Fortune 500 companies whose projects are self-funded a start-up must simultaneously have a value proposition that attracts a customer (for revenue) investors (for capital) and acquirers (for a liquidity event or IPO). A high percentage of start-ups fail before attaining positive cashflow due to a variety of reasons that are detailed in this book. Avoiding the pitfalls and wrong turns are the goals of this book. Innovation Commercialization and Start-Ups in Life Sciences details the methodologies necessary to create a successful life science start-up from initiation to exit. Written by an expert who has worked with more nearly 500 life science start-ups this book discusses specific processes and investor milestones that must be navigated to align customer funder and acquirer needs. Successful commercialization requires attention to multiple constituents such as investors regulators and customers. Investors require liquidity for their return which is achieved through selling their stock in a public or private sale. The reader will gain an appreciation for the necessary data partnerships and skills needed to create a competitive and sustainable company. The author discusses such specific issues as customer problems demonstrating sales access and ensuring intellectual property is impervious to competitive advancement. This book is intended to be suitable for entrepreneurs venture capitalists and investors in both business and academic settings. These organizations have specific departments such as R&D operations business development legal regulatory and marketing that would also benefit from this book. FEATURES Focuses specifically on life science start-ups Examines how to determine a company valuation and future fundable milestones Explores how to align regulatory and clinical strategies Discusses intellectual property derived from a university or individual through formation to exit. Reviews how start‐ups must simultaneously meet the needs of multiple constituencies at once: investors regulators customers and exit candidates James F. Jordan is an author consultant and speaker. He is a Distinguished Service Professor of Healthcare & Biotechnology Management a former Fortune 100 executive and a managing director of a venture fund. Access the Support Material: https://healthcaredata. center/ Cover design by Sarah Mailhott.

GBP 56.99
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Body Composition Health and Performance in Exercise and Sport

Body Composition Health and Performance in Exercise and Sport

Interest in the relationships between body structure and function in physical activity has persisted for centuries. Body Composition: Health and Performance in Exercise and Sport advances understanding beyond simple descriptions of body physique and composition of athletes and fills gaps in our understanding of the important role of muscle fat and bone in facilitating physical performance and health in sports and physically demanding occupations. lt addresses basic practical and applied topics in body composition performance and health with comprehensive reviews organized in four logical parts: Body Composition Assessment; Physical Activity and Body Composition; Body Composition in Sports and Occupations; and Moderating Factors. This book integrates state-of-the-art knowledge by international experts in the field and produces an evidence-based practical guide for a balanced understanding of the role and use of body composition assessment in physical performance and health for youth and adults. It also provides a needed link between the practice of body composition assessment and its application by members of public health advisory committees that develop national guidelines for diet physical activity and health. This book is suitable for students and professionals in sports nutrition exercise science kinesiology and athletic training. Sport administrators and policy-makers for international and national sport federations and organizations and national intercollegiate and scholastic federations would also benefit from this book. | Body Composition Health and Performance in Exercise and Sport

GBP 44.99
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Applied Machine Learning for Smart Data Analysis

Applied Machine Learning for Smart Data Analysis

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation context awareness analyzing various data sources machine learning algorithms and intelligent services and applications. Further it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning security IoT and data mining the concepts are explained with practical implementation including results. Key Features Follows an algorithmic approach for data analysis in machine learning Introduces machine learning methods in applications Address the emerging issues in computing such as deep learning machine learning Internet of Things and data analytics Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets Case studies are covered relating to human health transportation and Internet applications | Applied Machine Learning for Smart Data Analysis

GBP 125.00
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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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Stochastic Optimization for Large-scale Machine Learning

Stochastic Optimization for Large-scale Machine Learning

Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms i. e. optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.

GBP 150.00
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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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Machine Learning for Decision Sciences with Case Studies in Python

Machine Learning for Decision Sciences with Case Studies in Python

This book provides a detailed description of machine learning algorithms in data analytics data science life cycle Python for machine learning linear regression logistic regression and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical oil and gas e-commerce and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised supervised and reinforcement learning. This book is aimed at researchers professionals and graduate students in data science machine learning computer science and electrical and computer engineering. | Machine Learning for Decision Sciences with Case Studies in Python

GBP 160.00
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Green Machine Learning Protocols for Future Communication Networks

Green Machine Learning Protocols for Future Communication Networks

Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However heavy resources are required to process and analyze the data which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing memory communication and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms. For future scalable and sustainable network applications efforts are required toward designing new machine learning protocols and modifying the existing ones which consume less energy i. e. green machine learning protocols. In other words novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication federated learning-based models and protocols for Beyond Fifth Generation networks approaches for cloud-based communications and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications. | Green Machine Learning Protocols for Future Communication Networks

GBP 110.00
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VLSI and Hardware Implementations using Modern Machine Learning Methods

VLSI and Hardware Implementations using Modern Machine Learning Methods

Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods algorithms architectures and frameworks designed for VLSI design. The focus is on digital analog and mixed-signal design techniques device modeling physical design hardware implementation testability reconfigurable design synthesis and verification and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall the book provides practical implementations of VLSI design IC design and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers professionals and graduate students in VLSI machine learning electrical and electronic engineering computer engineering and hardware systems. | VLSI and Hardware Implementations using Modern Machine Learning Methods

GBP 115.00
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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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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
1

Machine Learning for Sustainable Manufacturing in Industry 4.0 Concept Concerns and Applications

Machine Learning for Sustainable Manufacturing in Industry 4.0 Concept Concerns and Applications

The book focuses on the recent developments in the areas of error reduction resource optimization and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4. 0 for better quality products and efficient manufacturing methods. Focusses on machine learning applications in Industry 4. 0 ecosystem such as resource optimization data analysis and predictions. Highlights the importance of the explainable machine learning model in the manufacturing processes. Presents the integration of machine learning and big data analytics from an industry 4. 0 perspective. Discusses advanced computational techniques for sustainable manufacturing. Examines environmental impacts of operations and supply chain from an industry 4. 0 perspective. This book provides scientific and technological insight into sustainable manufacturing by covering a wide range of machine learning applications fault detection cyber-attack prediction and inventory management. It further discusses resource optimization using machine learning in industry 4. 0 and explainable machine learning models for industry 4. 0. It will serve as an ideal reference text for senior undergraduate graduate students and academic researchers in the fields including mechanical engineering manufacturing engineering production engineering aerospace engineering and computer engineering. | Machine Learning for Sustainable Manufacturing in Industry 4. 0 Concept Concerns and Applications

GBP 110.00
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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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Handbook of Machine Learning for Computational Optimization Applications and Case Studies

Handbook of Machine Learning for Computational Optimization Applications and Case Studies

Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction execution and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers professionals and application designers. | Handbook of Machine Learning for Computational Optimization Applications and Case Studies

GBP 150.00
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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 and Deep Learning Techniques for Medical Image Recognition

Machine Learning and Deep Learning Techniques for Medical Image Recognition

Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas including chest X-ray breast computer-aided detection lung and chest microscopy and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering artificial intelligence and machine learning and biomedical imaging.

GBP 110.00
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Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques UNESCO-IHE PhD Thesis

Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques UNESCO-IHE PhD Thesis

This book describes the use of machine learning techniques to build predictive models of uncertainty with application to hydrological models focusing mainly on the development and testing of two different models. The first focuses on parameter uncertainty analysis by emulating the results of Monte Carlo simulation of hydrological models using efficient machine learning techniques. The second method aims at modelling uncertainty by building an ensemble of specialized machine learning models on the basis of past hydrological model‘s performance. The book then demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty. | Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques UNESCO-IHE PhD Thesis

GBP 175.00
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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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