Methodologies and Applications of Computational Statistics for Machine Intelligence

With the field of computational statistics growing rapidly, there is a need for capturing the advances and assessing their impact. Advances in simulation and graphical analysis also add to the pace of the statistical analytics field. Computational statistics play a key role in financial applications, particularly risk management and derivative pricing, biological applications including bioinformatics and computational biology, and computer network security applications that touch the lives of people. With high impacting areas such as these, it becomes important to dig deeper into the subject and explore the key areas and their progress in the recent past. Methodologies and Applications of Computational Statistics for Machine Intelligence serves as a guide to the applications of new advances in computational statistics. This text holds an accumulation of the thoughts of multiple experts together, keeping the focus on core computational statistics that apply to all domains. Covering topics including artificial intelligence, deep learning, and trend analysis, this book is an ideal resource for statisticians, computer scientists, mathematicians, lecturers, tutors, researchers, academic and corporate libraries, practitioners, professionals, students, and academicians. Methodologies and Applications of Computational Statistics for Machine Intelligence Table of Contents Detailed Table of Contents Preface Acknowledgment 1 Hybrid Feature Vector-Assisted Action Representation for Human Action Recognition Using Support Vector Machines 2 Unsupervised Summarization Approach With Computational Statistics of Microblog Data 3 Machine Intelligence of Pi From Geometrical Figures With Variable Parameters Using SCILab 4 Computational Statistics-Based Prediction Algorithms Using Machine Learning 5 Computational Statistics of Data Science for Secured Software Engineering 6 Quantitative and Visual Exploratory Data Analysis for Machine Intelligence 7 Continuous Autoregressive Moving Average Models: From Discrete AR to Lévy-Driven CARMA Models 8 A Computational Statistics Review for Low Complexity Clutter Cancellation for Passive Bi-Static Radar 9 Machine Intelligence-Based Trend Analysis of COVID-19 for Total Daily Confirmed Cases in Asia and Africa 10 Application of Machine Intelligence-Based Knowledge Graphs for Software Engineering 11 A Comprehensive Study on Artificial Intelligence and Robotics for Machine Intelligence 12 Innovation and Creativity for Data Mining Using Computational Statistics Compilation of References About the Contributors Index "This book delves into computational statistics that focus on devising an efficient methodology to obtain quantitative solutions for problems that are devised quantitatively and brings together computational capability and statistical advanced thought processes to solve some of the problems encountered in the field"-- Provided by publisher Provides a guide to the applications of new advances in computational statistics. The book focuses on core computational statistics that apply to all domains, and cover topics including artificial intelligence, deep learning, and trend analysis.