Search and Classification Using Multiple Autonomous Vehicles

Search and Classification Using Multiple Autonomous Vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, ca

Path planning for vehicles operating in uncertain 2D-environments presents a survey including several path planning methods developed using fuzzy logic, grapho-analytical search, neural networks and n

Modern videography provides an ever-widening window into subsea echinoderm life with vast potential for new knowledge. Supported by video evidence throughout, this Element begins with time-lapse video made in 1983 on film, using an off-the-shelf camera, flash, and underwater housings. Although quality has now been significantly improved by digital imagery, films from over thirty years ago captured crinoid feeding behavior previously unknown and demonstrated a great potential to learn about many other aspects of their biology. This sequence is followed by several examples of recent digital videography from submersibles of deep-sea crinoids and remotely operated vehicles (ROVs) (stalked and unstalked), as well as close-up video of crinoids in aquaria. These recent studies enabled a new classification of crinoid arm postures, provided detailed views of food particle capture, and revealed a wide range of behaviors in taxa never before seen in life.

Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are