Details
Artificial Intelligence, Big Data and Data Science in Statistics
Challenges and Solutions in Environmetrics, the Natural Sciences and Technology|
181,89 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 15.11.2022 |
| ISBN/EAN: | 9783031071553 |
| Sprache: | englisch |
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Beschreibungen
This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book’s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and datascientists, as well as engineers and computer scientists working in related fields or applications.
- <b>Part I Methodologies and Theoretical Studies.</b> - One-Round Cross-Validation and Uncertainty Determination for Randomized Neural Networks with Applications to Mobile Sensors. - Scale Invariant and Robust Pattern Identification in Univariate Time Series, with Application to Growth Trend Detection in Music Streaming Data. - Fine-Tuned Parallel Piecewise Sequential Confidence Interval and Point Estimation Strategies for the Mean of a Normal Population: Big Data Context. - Statistical Learning for Change Point and Anomaly Detection in Graphs. - On the Robustness of Kernel-Based Pairwise Learning. - Global Sensitivity Analysis for the Interpretation of Machine Learning Algorithms. - Improving Gaussian Process Emulators with Boundary Information. - <b>Part II Challenges and Solutions in Applications.</b> - An Overview and General Framework for Spatiotemporal Modeling and Applications in Transportation and Public Health. - Introduction to Wafer Tomography: Likelihood-Based Prediction of Integrated-Circuit Yield. - Uncertainty Quantification Based on Bayesian Neural Networks for Predictive Quality. - Two Statistical Degradation Models of Batteries Under Different Operating Conditions. - Detecting Diamond Breakouts of Diamond Impregnated Tools for Core Drilling of Concrete by Force Measurements. - Visualising Complex Data Within a Data Science Loop: A Spatio-Temporal Example from Football. - Application of the Singular Spectrum Analysis on Electro luminescence Images of Thin-Film Photovoltaic Modules. - The Impact of the Lockdown Restrictions on Air Quality During COVID-19 Pandemic in Lombardy, Italy.
<b>A</b><b>nsgar Steland</b> is a Full Professor at the Institute of Statistics at RWTH Aachen University, Germany. Previously he held positions at the Technische Universität Berlin, the European University Viadrina and the Ruhr-University Bochum. He is an Elected Member of the International Statistical Institute (ISI), Chair of the Society for Reliability, Quality and Safety and Chair of the German Statistical Society’s Statistics in Natural Sciences and Technology Section. His main research interests include change detection and quality control, high-dimensional statistics, time series analysis, nonparametric statistics, and image analysis and its applications to econometrics, the natural sciences and engineering, especially photovoltaics.<b> </b><p><b>Kwok-Leung Tsui </b>is a Professor at Virginia Tech’s Grado Department of Industrial and Systems Engineering in Blacksburg, VA, USA. Previously he held positions at AT&T Bell Laboratories, Georgia Institute of Technologyand the City University of Hong Kong. He is a fellow of the American Statistical Association, American Society for Quality, the International Society of Engineering Asset Management, and the Hong Kong Institution of Engineers. He is an elected Council Member of the International Statistical Institute (ISI), and a U.S. Representative to the International Organization for Standardization (ISO) Technical Committee on Statistical Methods. His current research interests include data science and data analytics, surveillance in healthcare and public health, personalized health monitoring, prognostics and systems health management, calibration and validation of computer models, process control and monitoring, and robust design and Taguchi methods.</p>
This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book’s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and datascientists, as well as engineers and computer scientists working in related fields or applications.
Demonstrates the interplay between statistics, data science, machine learning and artificial intelligence Focuses on applications in environmental science, the natural sciences, and technology Features invited contributions by experts in the field
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