Key Features: Provides an interdisciplinary approach, with chapter contributions from expert teams working in the governmental or private space sectors, with valuable contributions from computer scientists and legal experts; Presents insights into AI implementation and how to unlock AI technologies in the field; Up to date with the latest developments and cutting-edge applications

The Dynamical Ionosphere: A Systems Approach to Ionospheric Irregularity examines the Earth's ionosphere as a dynamical system with signatures of complexity. The system is robust in its overall configuration, with smooth space-time patterns of daily, seasonal and Solar Cycle variability, but shows a hierarchy of interactions among its sub-systems, yielding apparent unpredictability, space-time irregularity, and turbulence. This interplay leads to the need for constructing realistic models of the average ionosphere, incorporating the increasing knowledge and predictability of high variability components, and for addressing the difficulty of dealing with the worst cases of ionospheric disturbances, all of which are addressed in this interdisciplinary book. Borrowing tools and techniques from classical and stochastic dynamics, information theory, signal processing, fluid dynamics and turbulence science, The Dynamical Ionosphere presents the state-of-the-art in dealing with irregularity, forecasting ionospheric threats, and theoretical interpretation of various ionospheric configurations. - Presents studies addressing Earth's ionosphere as a complex dynamical system, including irregularities and radio scintillation, ionospheric turbulence, nonlinear time series analysis, space-ionosphere connection, and space-time structures - Utilizes interdisciplinary tools and techniques, such as those associated with stochastic dynamics, information theory, signal processing, fluid dynamics and turbulence science - Offers new data-driven models for different ionospheric variability phenomena - Provides a synoptic view of the state-of-the-art and most updated theoretical interpretation, results and data analysis tools of the "worst case" behavior in ionospheric configurations

An initial course in scientific data analysis and hypothesis testing designed for students in all science, technology, engineering, and mathematics disciplines Data Analysis for the Geosciences: Essentials of Uncertainty, Comparison, and Visualization is a textbook for upper-level undergraduate STEM students, designed to be their statistics course in a degree program. This volume provides a comprehensive introduction to data analysis, visualization, and data-model comparisons and metrics, within the framework of the uncertainty around the values. It offers a learning experience based on real data from the Earth, ocean, atmospheric, space, and planetary sciences. Volume highlights include: Serves as an initial course in scientific data analysis and hypothesis testing Focuses on the methods of data processing Introduces a wide range of analysis techniques Describes the many ways to compare data with models Centers on applications rather than derivations Explains how to select appropriate statistics for meaningful decisions Explores the importance of the concept of uncertainty Uses examples from real geoscience observations Homework problems at the end of chapters The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

This book constitutes the refereed proceedings of the Third Southwest Data Science Conference, on Recent advances in next-generation data science, SDSC 2024, held in Waco, TX, USA, in March 22, 2024. The 15 full papers presented were carefully reviewed and selected from 59 submissions. These papers focus on AI security in next-generation data science and address a range of challenges, from protecting sensitive data to mitigating adversarial threats.

Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. - Collects many representative non-traditional approaches to space weather into a single volume - Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists - Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms

This book constitutes the refereed proceedings of the First Southwest Data Science Conference, on The Recent Advances in Transdisciplinary Data Science, SDSC 2022, held in Waco, TX, USA, during March 25–26, 2022. The 14 full papers and 2 short papers included in this book were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Business and social data science; Health and biological data science; Applied data science, artificial intelligence, and data engineering.

DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.