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Weighing Lives - John Broome - Bog - Oxford University Press - Plusbog.dk

Weighing Lives - John Broome - Bog - Oxford University Press - Plusbog.dk

We are often faced with choices that involve the weighing of people''s lives against each other, or the weighing of lives against other good things. These are choices both for individuals and for societies. A person who is terminally ill may have to choose between palliative care and more aggressive treatment, which will give her a longer life but at some cost in suffering. We have to choose between the convenience to ourselves of road and air travel, and the lives of the future people who will be killed by the global warming we cause, through violent weather, tropical disease, and heat waves. We also make choices that affect how many lives there will be in the future: as individuals we choose how many children to have, and societies choose tax policies that influence people''s choices about having children. These are all problems of weighing lives.How should we weigh lives? Weighing Lives develops a theoretical basis for answering this practical question. It extends the work and methods of Broome''s earlier book Weighing Goods to cover the questions of life and death.Difficult problems come up in the process. In particular, Weighing Lives tackles the well-recognized, awkward problems of the ethics of population. It carefully examines the common intuition that adding people to the population is ethically neutral - neither a good nor a bad thing - but eventually concludes this intuition cannot be fitted into a coherent theory of value. In the course of its argument, Weighing Lives examines many of the issues of contemporary moral theory: the nature of consequentialism and teleology; the transitivity, continuity, and vagueness of betterness; the quantitative conception of wellbeing; the notion of a life worth living; the badness of death; and others.This is a work of philosophy, but one of its distinctive features is that it adopts some of the precise methods of economic theory (without introducing complex mathematics). Not only philosophers, but also economists and political theorists concerned with the practical question of valuing life, should find the book''s conclusions highly significant to their work.

DKK 1110.00
1

Weighing and Reasoning - - Bog - Oxford University Press - Plusbog.dk

Weighing and Reasoning - - Bog - Oxford University Press - Plusbog.dk

John Broome has made major contributions to, and radical innovations in, contemporary moral philosophy. His research combines the formal method of economics with philosophical analysis. Broome''s works stretch over formal axiology, decision theory, philosophy of economics, population axiology, the value of life, the ethics of climate change, the nature of rationality, and practical and theoretical reasoning. Weighing and Reasoning brings together fifteen original essays from leading philosophers who have been influenced by the work and thought of John Broome. It aims to offer a comprehensive evaluation of Broome''s wide-ranging and far-reaching philosophical works over the past thirty years. The volume comprises two parts. The first part is focused on Broome''s work on the theory of value, as exemplified in his books Weighing Goods, Weighing Lives, Economics out of Economics, and Climate Matters. The second part is focused on his work on practical and theoretical reasoning, which culminated in his Rationality through Reasoning. This volume also includes a piece by Broome on his intellectual history to date.

DKK 986.00
1

Artificial Intelligence and Machine Learning for Smart Community - - Bog - Taylor & Francis Ltd - Plusbog.dk

Artificial Intelligence and Machine Learning for Smart Community - - Bog - Taylor & Francis Ltd - Plusbog.dk

Intelligent systems are technologically advanced machines that perceive and respond to the world around them. Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities. - Covers the core and fundamental aspects of artificial intelligence, machine learning, and computational algorithms in smart intelligent systems - Discusses the integration of artificial intelligence with machine learning using mathematical modeling - Elaborates concepts like supervised and unsupervised learning, and machine learning algorithms, such as linear regression, logistic regression, random forest, and performance evaluation matrices - Introduces modern algorithms such as convolutional neural networks and support vector machines - Presents case studies on smart healthcare, smart traffic management, smart buildings, autonomous vehicles, smart education, modern community, and smart machines Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications is primarily written for graduate students and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology. Seasonal Blurb: This reference text presents the most recent and advanced research on the application of artificial intelligence and machine learning on intelligent systems. It will discuss important topics such as business intelligence, reinforcement learning, supervised learning, and unsupervised learning in a comprehensive manner.

DKK 976.00
1

Domain-informed Machine Learning for Smart Manufacturing - Qiang Huang - Bog - Springer International Publishing AG - Plusbog.dk

Domain-informed Machine Learning for Smart Manufacturing - Qiang Huang - Bog - Springer International Publishing AG - Plusbog.dk

This book introduces the state-of-the-art understanding on domain-informed machine learning (DIML) for advanced manufacturing. Methods and case studies presented in this volume show how complicated engineering phenomena and mechanisms are integrated into machine learning problem formulation and methodology development. Ultimately, these methodologies contribute to quality control for smart personalized manufacturing. The topics include domain-informed feature representation, dimension reduction for personalized manufacturing, fabrication-aware modeling of additive manufacturing processes, small-sample machine learning for 3D printing quality, optimal compensation of 3D shape deviation in 3D printing, engineering-informed transfer learning for smart manufacturing, and domain-informed predictive modeling for nanomanufacturing quality. Demonstrating systematically how the various aspects of domain-informed machine learning methods are developed for advanced manufacturing such as additive manufacturing and nanomanufacturing, the book is ideal for researchers, professionals, and students in manufacturing and related engineering fields.

DKK 814.00
1

Introduction to Statistical Machine Learning - Masashi (professor Sugiyama - Bog - Elsevier Science & Technology - Plusbog.dk

Introduction to Statistical Machine Learning - Masashi (professor Sugiyama - Bog - Elsevier Science & Technology - Plusbog.dk

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.

DKK 884.00
1

Machine Learning in Clinical Neuroscience - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning in Clinical Neuroscience - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book bridges the gap between data scientists and clinicians by introducing all relevant aspects of machine learning in an accessible way, and will certainly foster new and serendipitous applications of machine learning in the clinical neurosciences. Building from the ground up by communicating the foundational knowledge and intuitions first before progressing to more advanced and specific topics, the book is well-suited even for clinicians without prior machine learning experience. Authored by a wide array of experienced global machine learning groups, the book is aimed at clinicians who are interested in mastering the basics of machine learning and who wish to get started with their own machine learning research. The volume is structured in two major parts: The first uniquely introduces all major concepts in clinical machine learning from the ground up, and includes step-by-step instructions on how to correctly develop and validate clinical prediction models. It also includes methodological and conceptual foundations of other applications of machine learning in clinical neuroscience, such as applications of machine learning to neuroimaging, natural language processing, and time series analysis. The second part provides an overview of some state-of-the-art applications of these methodologies. The Machine Intelligence in Clinical Neuroscience (MICN) Laboratory at the Department of Neurosurgery of the University Hospital Zurich studies clinical applications of machine intelligence to improve patient care in clinical neuroscience. The group focuses on diagnostic, prognostic and predictive analytics that aid in decision-making by increasing objectivity and transparency to patients. Other major interests of our group members are in medical imaging, and intraoperative applications of machine vision.

DKK 986.00
1

Advanced Cutting Tool Technology and Machine Processes - Yarub Al Douri - Bog - Taylor & Francis Ltd - Plusbog.dk

Advanced Cutting Tool Technology and Machine Processes - Yarub Al Douri - Bog - Taylor & Francis Ltd - Plusbog.dk

Today''s industry heavily relies on advanced cutting tool technology and manufacturing techniques to enhance quality and efficiency in production. This undergraduate-level textbook is dedicated to exploring the latest developments that enhance tool performance, lower costs, and boost productivity. The mission of this textbook is to provide readers with a thorough understanding of cutting-edge technologies and methods used in cutting tools and industrial processes. Advanced Cutting Tool Technology and Machine Processes step into the world of modern cutting tools used in the metallurgical industry. Each tool is meticulously dissected, showcasing its distinctive features and methods of operation. The text explores advanced metal cutting and processing techniques, including laser, water, and plasma cutting. By covering these cutting-edge methods, students and professionals can remain at the forefront of industry advancements. In addition to detailed tool descriptions, the textbook offers practical guidance on utilizing tools effectively and safely, as well as tips on tool maintenance to ensure longevity and peak performance. To enhance comprehension, the textbook incorporates exercise problems, case studies, and practical examples that illustrate how theoretical knowledge is applied in real-world scenarios. This hands-on approach aids in the development of problem-solving skills and the practical application of concepts. Lastly, the textbook provides comprehensive information on the properties of various metals and how to handle them effectively. This knowledge is crucial in selecting the appropriate tools for each type of metal, guaranteeing precision and efficiency in cutting processes. This textbook is ideal for undergraduate students in production, materials, industrial, mechatronics, marine, mechanical, and manufacturing engineering programs, and is also useful for graduate programs related to higher-level machining topics, as well as professional engineers and technicians. Figure slides and a solutions manual for available for qualified textbook adoptions.

DKK 1093.00
1

Analysis and Design of Machine Elements - Wei Jiang - Bog - John Wiley & Sons Inc - Plusbog.dk

Analysis and Design of Machine Elements - Wei Jiang - Bog - John Wiley & Sons Inc - Plusbog.dk

Incorporating Chinese, European, and International standards and units of measurement, this book presents a classic subject in an up-to-date manner with a strong emphasis on failure analysis and prevention-based machine element design. It presents concepts, principles, data, analyses, procedures, and decision-making techniques necessary to design safe, efficient, and workable machine elements. Design-centric and focused, the book will help students develop the ability to conceptualize designs from written requirements and to translate these design concepts into models and detailed manufacturing drawings. Presents a consistent approach to the design of different machine elements from failure analysis through strength analysis and structural design, which facilitates students’ understanding, learning, and integration of analysis with designFundamental theoretical topics such as mechanics, friction, wear and lubrication, and fluid mechanics are embedded in each chapter to illustrate design in practiceIncludes examples, exercises, review questions, design and practice problems, and CAD examples in each self-contained chapter to enhance learning Analysis and Design of Machine Elements is a design-centric textbook for advanced undergraduates majoring in Mechanical Engineering. Advanced students and engineers specializing in product design, vehicle engineering, power machinery, and engineering will also find it a useful reference and practical guide.

DKK 944.00
1

Healthcare Solutions Using Machine Learning and Informatics - - Bog - Taylor & Francis Ltd - Plusbog.dk

Healthcare Solutions Using Machine Learning and Informatics - - Bog - Taylor & Francis Ltd - Plusbog.dk

Healthcare Solutions Using Machine Learning and Informatics covers novel and innovative solutions for healthcare that apply machine learning and biomedical informatics technology. The healthcare sector is one of the most critical in society. This book presents a series of artificial intelligence, machine learning, and intelligent IoT-based solutions for medical image analysis, medical big-data processing, and disease predictions. Machine learning and artificial intelligence use cases in healthcare presented in the book give researchers, practitioners, and students a wide range of practical examples of cross-domain convergence. The wide variety of topics covered include: - Artificial Intelligence in healthcare - Machine learning solutions for such disease as diabetes, arthritis, cardiovascular disease, and COVID-19 - Big data analytics solutions for healthcare data processing - Reliable biomedical applications using AI models - Intelligent IoT in healthcare The book explains fundamental concepts as well as the advanced use cases, illustrating how to apply emerging technologies such as machine learning, AI models, and data informatics into practice to tackle challenges in the field of healthcare with real-world scenarios. Chapters contributed by noted academicians and professionals examine various solutions, frameworks, applications, case studies, and best practices in the healthcare domain.

DKK 884.00
1

Quantum Machine Learning - - Bog - Taylor & Francis Ltd - Plusbog.dk

Quantum Machine Learning - - Bog - Taylor & Francis Ltd - Plusbog.dk

This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies. In summary, the book: - - Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. - - - Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. - - - Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. - - - Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. - - - Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects. - This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.

DKK 901.00
1

Statistical Machine Learning - Richard Golden - Bog - Taylor & Francis Ltd - Plusbog.dk

Statistical Machine Learning - Richard Golden - Bog - Taylor & Francis Ltd - Plusbog.dk

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: - - - Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms - - - Matrix calculus methods for supporting machine learning analysis and design applications - - - Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions - - - Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification - This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

DKK 993.00
1

Advanced Data Mining and Applications - - Bog - Springer International Publishing AG - Plusbog.dk

Advanced Data Mining and Applications - - Bog - Springer International Publishing AG - Plusbog.dk

This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos.

DKK 800.00
1

Data Analytics and Machine Learning - - Bog - Springer Verlag, Singapore - Plusbog.dk

Data Analytics and Machine Learning - - Bog - Springer Verlag, Singapore - Plusbog.dk

This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data analytics, big data, and machine learning solutions in their own organizations. The book discusses the transformative power of data analytics and big data in various industries and sectors and how machine learning applications have revolutionized exploration by enabling advanced data analysis techniques for mapping, geospatial analysis, and environmental monitoring, enhancing our understanding of the world and its dynamic processes. This book explores how big data explosion, the power of analytics and machine learning revolution can bring new prospects and opportunities in the dynamic and data-rich landscape. It highlights the future research directions in data analytics, big data, and machine learning that explores the emerging trends, challenges, and opportunities in these fields by covering interdisciplinary approaches such as handling and analyzing real-time and streaming data.

DKK 1197.00
1

Machine Translation - Pushpak Bhattacharyya - Bog - Taylor & Francis Inc - Plusbog.dk

Machine Translation - Pushpak Bhattacharyya - Bog - Taylor & Francis Inc - Plusbog.dk

Three paradigms have dominated machine translation (MT)—rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT). These paradigms differ in the way they handle the three fundamental processes in MT—analysis, transfer, and generation (ATG). In its pure form, RBMT uses rules, while SMT uses data. EBMT tries a combination—data supplies translation parts that rules recombine to produce translation. Machine Translation compares and contrasts the salient principles and practices of RBMT, SMT, and EBMT. Offering an exposition of language phenomena followed by modeling and experimentation, the text: - Introduces MT against the backdrop of language divergence and the Vauquois triangle - Presents expectation maximization (EM)-based word alignment as a turning point in the history of MT - Discusses the most important element of SMT—bilingual word alignment from pairs of parallel translations - Explores the IBM models of MT, explaining how to find the best alignment given a translation pair and how to find the best translation given a new input sentence - Covers the mathematics of phrase-based SMT, phrase-based decoding, and the Moses SMT environment - Provides complete walk-throughs of the working of interlingua-based and transfer-based RBMT - Analyzes EBMT, showing how translation parts can be extracted and recombined to translate a new input, all automatically - Includes numerous examples that illustrate universal translation phenomena through the usage of specific languages Machine Translation is designed for advanced undergraduate-level and graduate-level courses in machine translation and natural language processing. The book also makes a handy professional reference for computer engineers. Print Versions of this book also include access to the ebook version.

DKK 854.00
1

Multiple 3-phase Fault Tolerant Permanent Magnet Machine Drives - Bo Wang - Bog - John Wiley & Sons Inc - Plusbog.dk

Multiple 3-phase Fault Tolerant Permanent Magnet Machine Drives - Bo Wang - Bog - John Wiley & Sons Inc - Plusbog.dk

Groundbreaking analysis of a fully functional fault-tolerant machine drive Electrical machine drives have become an increasingly important component of transportation electrification, including electric vehicles, railway and subway traction, aerospace actuation, and more. This expansion of electrical machine drives into safety-critical areas has driven an increasingly urgent demand for high reliability and strong fault tolerance. Machine drives incorporating a permanent magnet (PM)-assisted synchronous reluctance machine drive with a segregated winding have shown to exhibit notably reduced PM flux and correspondingly enhanced fault tolerance. Multiple 3-Phase Fault Tolerant Permanent Magnet Machine Drives: Design and Control offers one of the first fully integrated accounts of a functional fault-tolerant machine drive. It proposes a segregated winding which can be incorporated into multiple machine topologies without affecting performance and brings together cutting-edge technologies to manage these crucial drives in both healthy and fault conditions. The result is a must-own for engineers and researchers alike. Readers will also find: Advanced modeling techniques for different operation conditions Detailed discussion on topics including fault detection techniques, postfault tolerant control strategies, and many more An authorial team with immense experience in the study of fault-tolerant machine drives Multiple 3-Phase Fault Tolerant Permanent Magnet Machine Drives: Design and Control is ideal for researchers and graduate students in engineering and related industries.

DKK 1007.00
1

Machine Learning for Speaker Recognition - Jen Tzung (national Chiao Tung University Chien - Bog - Cambridge University Press - Plusbog.dk

Machine Learning for Speaker Recognition - Jen Tzung (national Chiao Tung University Chien - Bog - Cambridge University Press - Plusbog.dk

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

DKK 911.00
1

Practical Machine Learning with R - Carsten Lange - Bog - Taylor & Francis Ltd - Plusbog.dk

Practical Machine Learning with R - Carsten Lange - Bog - Taylor & Francis Ltd - Plusbog.dk

This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts. Students are not required to have advanced mathematics knowledge such as matrix algebra or calculus. The author introduces machine learning algorithms, utilizing the widely used R language for statistical analysis. Each chapter includes examples, case studies, and interactive tutorials to enhance understanding. No prior programming knowledge is needed. The book leverages the tidymodels package, an extension of R, to streamline data processing and model workflows. This package simplifies commands, making the logic of algorithms more accessible by minimizing programming syntax hurdles. The use of tidymodels ensures a unified experience across various machine learning models. With interactive tutorials that students can download and follow along at their own pace, the book provides a practical approach to apply machine learning algorithms to real-world scenarios. In addition to the interactive tutorials, each chapter includes a Digital Resources section, offering links to articles, videos, data, and sample R code scripts. A companion website further enriches the learning and teaching experience: https://ai.lange-analytics.com . This book is not just a textbook; it is a dynamic learning experience that empowers students and instructors alike with a practical and accessible approach to machine learning in business and economics. Key Features: - Unlocks machine learning basics without advanced mathematics — no calculus or matrix algebra required. - Demonstrates each concept with R code and real-world data for a deep understanding — no prior programming knowledge is needed. - Bridges the gap between theory and real-world applications with hands-on interactive projects and tutorials in every chapter, guided with hints and solutions. - Encourages continuous learning with chapter-specific online resources—video tutorials, R-scripts, blog posts, and an online community. - Supports instructors through a companion website that includes customizable materials such as slides and syllabi to fit their specific course needs.

DKK 810.00
1

Machine Learning in Finance - Matthew F. Dixon - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning in Finance - Matthew F. Dixon - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers'' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher''s perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

DKK 884.00
1

Advanced Electroencephalography Analytical Methods - - Bog - Taylor & Francis Ltd - Plusbog.dk

Advanced Electroencephalography Analytical Methods - - Bog - Taylor & Francis Ltd - Plusbog.dk

Advanced Electroencephalography Analytical Methods: Fundamentals, Acquisition, and Applications presents the theoretical basis and applications of electroencephalography (EEG) signals in neuroscience, involving signal analysis, processing, signal acquisition, representation, and applications of EEG signal analysis using non-linear approaches and machine learning. It explains principles of neurophysiology, linear signal processing, computational intelligence, and the nature of signals including machine learning. Applications involve computer-aided diagnosis, brain-computer interfaces, rehabilitation engineering, and applied neuroscience. This book: - Includes a comprehensive review on biomedical signals nature and acquisition aspects - Focuses on selected applications of neuroscience/cardiovascular/muscle-related biomedical areas - Provides a machine learning update to a classical biomedical signal processing approach - Explains deep learning and application to biomedical signal processing and analysis - Explores relevant biomedical engineering and neuroscience state-of-the-art applications This book is intended for researchers and graduate students in biomedical signal processing, electrical engineering, neuroscience, and computer science.

DKK 995.00
1