Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research

All the tools needed to perform a thorough risk assessment—whether you're working in insurance, forensics, engineering, or public safety Risk analysis is the method of analyzing the dangers to individuals, businesses, and government agencies posed by potential natural and man-made hazards. The central task of the risk assessor is predicting the success of a project. This includes isolating the entire spectrum of adverse events that can derail a project or threaten the health and safety of individuals, organizations, and the environment. Designed as a practical, in-the-field toolkit, Risk Assessment details every aspect of how a risk assessment is performed, showing the proper tool to be used at various steps in the process, as well as locating the tool that best fits the risk assessment task at hand. Examining not only the very nature of risks and consequences, with fascinating historical examples, the book progresses from simple to more complex risk assessment techniques used by the authors in their daily work, all presented in a form that can be readily adapted to any number of real-life situations: Ecological Risk Assessment Task Analysis Techniques Preliminary Hazards Analysis Failure Mode and Effects Analysis Human Reliability Analysis Critical Incident Technique With numerous industry-specific case studies, as well as additional case studies for risk assessments for a restaurant and a process plant, the book provides readers with complete examples of how each of the techniques can be used in a variety of real-world situations. Including downloadable worksheets and other useful assessment materials, as well as guidance on using PRA software, this unparalleled reference offers all the tools and techniques needed to conduct a thorough and accurate assessment of risk.

A practical guide to the methodologies used in language teaching and learning research, providing expert advice and real-life examples from leading TESOL researches Research Methods in Language Teaching and Learning provides practical guidance on the primary research methods used in second language teaching, learning, and education. Designed to support researchers and students in language education and learning, this highly accessible book covers a wide range of research methodologies in the context of actual practice to help readers fully understand the process of conducting research. Organized into three parts, the book covers qualitative studies, quantitative studies, and systematic reviews. Contributions by an international team of distinguished researchers and practitioners explain and demonstrate narrative inquiry, discourse analysis, ethnography, heuristic inquiry, mixed methods, experimental and quasi-experimental studies, and more. Each chapter presents an overview of a method of research, an in-depth description of the research framework or data analysis process, and a meta-analysis of choices made and challenges encountered. Offering invaluable insights and hands-on research knowledge to students and early-career practitioners alike, this book: Focuses on the research methods, techniques, tools, and practical aspects of performing research Provides firsthand narratives and case studies to explain the decisions researchers make Compares the relative strengths and weaknesses of different research methods Includes real-world examples for each research method and framework to highlight the context of the study Includes extensive references, further reading suggestions, and end-of-chapter review questions Part of the Guides to Research Methods in Language and Linguistics series, Research Methods in Language Teaching and Learning is essential reading for students, educators, and researchers in all related fields, including TESOL, second language acquisition, English language teaching, and applied linguistics.

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.

Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment – and this book shows you how.

A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author—a noted expert in the field—covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. In addition, the book discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization, evolutionary computation, nature-inspired algorithms and many other topics. Designed as a practical resource, all topics are explained in detail with step-by-step examples to show how each method works. The book’s exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining. This important resource: Offers an accessible and state-of-the-art introduction to the main optimization techniques Contains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques Presents a balance of theory, algorithms, and implementation Includes more than 100 worked examples with step-by-step explanations Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.

An informative and comprehensive book on the applications and techniques of dried blood spot sampling Dried blood spot (DBS) sampling involves the collection of a small volume of blood, via a simple prick or other means, from a study subject onto a cellulose or polymer paper card, which is followed by drying and transfer to the laboratory for analysis. For many years, this method of blood sample collection has been extensively utilized in some important areas of human healthcare (for example, newborn screening for inherited metabolic disorders and HIV-related epidemiological studies). Because of its advantages over conventional blood, plasma, or serum sample collection, DBS sampling has been valued by the pharmaceutical industry in drug research and development. Dried Blood Spots: Applications and Techniques features contributions from an international team of leading scientists in the field. Their contributions present a unique resource on the history, principles, procedures, methodologies, applications, and emerging technologies related to DBS. Presented in three parts, the book thoroughly examines: Applications of DBS sampling and associated procedures and methodologies in various human healthcare studies Applications and perspectives of DBS sampling in drug research and development, and therapeutic drug monitoring New technologies and emerging applications related to DBS sampling and analysis Dried Blood Spots: Applications and Techniques is a valuable working guide for researchers, professionals, and students in healthcare, medical science, diagnostics, clinical chemistry, and pharmaceuticals, etc.

Informatics and Machine Learning Discover a thorough exploration of how to use computational, algorithmic, statistical, and informatics methods to analyze digital data Informatics and Machine Learning: From Martingales to Metaheuristics delivers an interdisciplinary presentation on how analyze any data captured in digital form. The book describes how readers can conduct analyses of text, general sequential data, experimental observations over time, stock market and econometric histories, or symbolic data, like genomes. It contains large amounts of sample code to demonstrate the concepts contained within and assist with various levels of project work. The book offers a complete presentation of the mathematical underpinnings of a wide variety of forms of data analysis and provides extensive examples of programming implementations. It is based on two decades worth of the distinguished author’s teaching and industry experience. A thorough introduction to probabilistic reasoning and bioinformatics, including Python shell scripting to obtain data counts, frequencies, probabilities, and anomalous statistics, or use with Bayes’ rule An exploration of information entropy and statistical measures, including Shannon entropy, relative entropy, maximum entropy (maxent), and mutual information A practical discussion of ad hoc, ab initio , and bootstrap signal acquisition methods, with examples from genome analytics and signal analytics Perfect for undergraduate and graduate students in machine learning and data analytics programs, Informatics and Machine Learning: From Martingales to Metaheuristics will also earn a place in the libraries of mathematicians, engineers, computer scientists, and life scientists with an interest in those subjects.

Steganography is the art of communicating a secret message, hiding the very existence of a secret message. This is typically done by hiding the message within a non-sensitive document. Steganalysis is the art and science of detecting such hidden messages. The task in steganalysis is to take an object (communication) and classify it as either a steganogram or a clean document. Most recent solutions apply classification algorithms from machine learning and pattern recognition, which tackle problems too complex for analytical solution by teaching computers to learn from empirical data. Part 1of the book is an introduction to steganalysis as part of the wider trend of multimedia forensics, as well as a practical tutorial on machine learning in this context. Part 2 is a survey of a wide range of feature vectors proposed for steganalysis with performance tests and comparisons. Part 3 is an in-depth study of machine learning techniques and classifier algorithms, and presents a critical assessment of the experimental methodology and applications in steganalysis. Key features: Serves as a tutorial on the topic of steganalysis with brief introductions to much of the basic theory provided, and also presents a survey of the latest research. Develops and formalises the application of machine learning in steganalysis; with much of the understanding of machine learning to be gained from this book adaptable for future study of machine learning in other applications. Contains Python programs and algorithms to allow the reader to modify and reproduce outcomes discussed in the book. Includes companion software available from the author’s website.

The second edition of this successful machine vision textbook is completely updated, revised and expanded by 15% to reflect the developments of recent years in the fields of image acquisition, machine vision algorithms and applications. The new content includes, but is not limited to, a discussion of new cameras and image acquisition interfaces, 3D sensors and technologies, 3D object recognition and 3D image reconstruction. The authors retain their balanced approach with sufficient coverage of the theory and a strong focus on applications. All examples are based on the latest version of the machine vision software HALCON 13, a trial version of which is available from the authors' website.