Artificial Intelligence, Big Data and Data Science in Statistics: Challenges and Solutions in Environmetrics, the Natural Sciences and Technology

Length: 384 pages
Edition: 1
Language: English
Publisher: Springer
Publication Date: 2022-11-16
ISBN-10: 3031071549
ISBN-13: 9783031071546
Sales Rank: #0 (See Top 100 Books)

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 data scientists, as well as engineers and computer scientists working in related fields or applications.

Cover
Front Matter
Part I. Methodologies and Theoretical Studies
    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
Part II. Challenges and Solutions in Applications
    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 Electroluminescence Images of Thin-Film Photovoltaic Modules
    The Impact of the Lockdown Restrictions on Air Quality During COVID-19 Pandemic in Lombardy, Italy
Back Matter