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Books on the topic 'Classification of biomedical time series'

Author: Grafiati

Published: 21 December 2024

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1

Melin, Patricia, Martha Ramirez, and Oscar Castillo. Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71101-5.

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2

Abel, Jaison R. A time series and cross-sectional classification of state regulatory policy adopted for local exchange carriers: Divestiture to present, 1984-1998. Columbus, Ohio: National Regulatory Research Institute, 1998.

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3

Time Series Clustering and Classification. Chapman and Hall/CRC, 2019.

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4

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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5

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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6

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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7

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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8

Maharaj, Elizabeth Ann, Jorge Caiado, and Pierpaolo DUrso. Time Series Clustering and Classification. Taylor & Francis Group, 2021.

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9

Buza, Krisztian. Fusion Methods for Time-Series Classification. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2011.

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10

Volna, Eva, Martin Kotyrba, and Michal Janosek. Pattern Recognition and Classification in Time Series Data. IGI Global, 2016.

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11

McDowall, David, Richard McCleary, and Bradley J. Bartos. Interrupted Time Series Analysis. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190943943.001.0001.

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Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioural, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing and model selection. New developments, including Bayesian hypothesis testing and synthetic control group designs are described and their prospects for widespread adoption are discussed. Example analyses make optimal use of graphical illustrations. Mathematical methods used in the example analyses are explicated assuming only exposure to an introductory statistics course. Design and Analysis of Time Series Experiments (DATSE) and other appropriate authorities are cited for formal proofs. Forty completed example analyses are used to demonstrate the implications of model properties. The example analyses are suitable for use as problem sets for classrooms, workshops, and short-courses.

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12

Li, édéric. Deep Learning for Time-Series Classification Enhanced by Transfer Learning Based on Sensor Modality Discrimination. Logos Verlag Berlin, 2021.

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13

McCleary, Richard, David McDowall, and Bradley Bartos. Design and Analysis of Time Series Experiments. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.001.0001.

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Design and Analysis of Time Series Experiments develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioral, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing, and model selection. The validity of causal inferences is approached from two complementary directions. The four-validity system of Cook and Campbell relies on ruling out discrete threats to statistical conclusion, internal, construct, and external validity. The Rubin system causal model relies on the identification of counterfactual time series. The two approaches to causal validity are shown to be complementary and are illustrated with a construction of a synthetic control time series. Example analyses make optimal use of graphical illustrations. Mathematical methods used in the example analyses are explicated in technical appendices, including expectation algebra, sequences and series, maximum likelihood, Box-Cox transformation analyses and probability.

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14

Nguyen, Huy Hoang, and Paul N. Adams. Building Statistical Models in Python: Develop Useful Models for Regression, Classification, Time Series, and Survival Analysis. de Gruyter GmbH, Walter, 2023.

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15

Whitenack, Daniel. Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language. Packt Publishing - ebooks Account, 2017.

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16

Hall, Richard, Bonnie Antoun, H. Jerry Qi, Hongbing Lu, Jevan Furmanski, Alireza Amirkhizi, G. P. Tandon, and Charles Lu. Challenges In Mechanics of Time-Dependent Materials and Processes in Conventional and Multifunctional Materials, Volume 2: Proceedings of the 2013 ... Society for Experimental Mechanics Series). Springer, 2013.

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17

Ferraty, Frédéric, and Yves Romain, eds. The Oxford Handbook of Functional Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.001.0001.

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This handbook presents the state-of-the-art of the statistics dealing with functional data analysis. With contributions from international experts in the field, it discusses a wide range of the most important statistical topics (classification, inference, factor-based analysis, regression modeling, resampling methods, time series, random processes) while also taking into account practical, methodological, and theoretical aspects of the problems. The book is organised into three sections. Part I deals with regression modeling and covers various statistical methods for functional data such as linear/nonparametric functional regression, varying coefficient models, and linear/nonparametric functional processes (i.e. functional time series). Part II considers related benchmark methods/tools for functional data analysis, including curve registration methods for preprocessing functional data, functional principal component analysis, and resampling/bootstrap methods. Finally, Part III examines some of the fundamental mathematical aspects of the infinite-dimensional setting, with a focus on the stochastic background and operatorial statistics: vector-valued function integration, spectral and random measures linked to stationary processes, operator geometry, vector integration and stochastic integration in Banach spaces, and operatorial statistics linked to quantum statistics.

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18

Van Dyk, Jacob. The Modern Technology of Radiation Oncology, Vol 4. Medical Physics Publishing, 2020. http://dx.doi.org/10.54947/9781951134020.

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High praise continues to come in for the 4th volume of Jake Van Dyk's The Modern Technology of Radiation Oncology. From Peter Metcalfe in Physical and Engineering Sciences in Medicine… "Thank goodness medical physics has Jacob Van Dyk. Like Tiger Woods and Phil Mickelson in golf, his textbooks continue to make major comebacks. He has managed to assemble the most talented among us to sustain the up-to-date knowledge that is essential to our profession. Reference knowledge from this textbook will help ensure the medical physics profession is at the cutting edge of cancer research and clinical treatment. This textbook has taken pride of place on my bookshelf, right next to my most treasured Porsche magazines. I could not give it a higher accolade than that." From Rajesh A. Kinhikar in Journal of Medical Physics…"This resourceful book has aimed to serve as a comprehensive textbook for the practicing radiotherapy professionals. I would like to congratulate the authors and the Editor for such a high?quality scientific feast and strongly recommend the fourth volume of The Modern Technology of Radiation Oncologyto the clinical medical physicists and radiation oncology professionals involved with the rapidly evolving radiotherapy." New topics addressed in volume 4 include surface-guided radiation therapy (RT), PET/MRI, real-time MRI guidance, robust optimization, automated treatment planning, artificial intelligence, adaptive RT, machine learning, big data, radiomics, particle therapy RBE, nanoparticle applications, economic considerations, global medical physics activities, global access to RT, and FLASH RT. The volumes in this series have not only been valued by medical physicists and radiation oncologists in clinical practice around the world, but have also provided an important learning resource for residency programs, radiation technologists, dosimetrists, research students, biomedical engineers, and ancillary professionals related with radiotherapy. Administrators and scientists affiliated with the practice of radiation therapy will also find this book a useful resource.

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19

Jockers, Matthew L. Style. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037528.003.0006.

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This chapter shows how stylistic signals can be derived from high-frequency features and how the usage, or nonusage, of those features was susceptible to influences that are external to the so-called “authorial style,” external influences such as genre, time, and gender. These aspects of style were explored using a controlled corpus of 106 British novels where genre was a key point of analysis. The chapter first provides an overview of statistical or quantitative authorship attribution before discussing the author's project, in which he analyzed the degree to which novelistic genres express a distinguishable stylistic signal by focusing on the distribution of novels in a corpus based on their genres and decades of publication. Through a series of experiments, he demonstrates the use of the classification methodology as a way of measuring the extent to which factors beyond an individual author's personal style may play a role in determining the linguistic usage and style of the resulting text.

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