2019 10th International Workshop On The Analysis Of Multitemporal Remote Sensing Images Multitemp

Promote discussion of scientific, methodological and application issues related to multitemporal data analysis Push novel solutions for technical problems related to multitemporal data, promote their use in an increasing number of applications

This book is a collection of the papers accepted by the ICIVIS 2021—The International Conference on Image, Vision and Intelligent Systems held on June 15–17, 2021, in Changsha, China. The topics focus but are not limited to image, vision and intelligent systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state-of-practice in the topics covered by this conference proceedings.

This two-volume book constitutes the post-conference proceedings of the 5th International Conference on Advances in Computing and Data Sciences, ICACDS 2021, held in Nashik, India, in April 2021.* The 103 full papers were carefully reviewed and selected from 781 submissions. The papers in Part I and II are centered around topics like distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations database management system engines, data mining, information retrieval query processing, database and storage security, ubiquitous and mobile computing, parallel computing methodologies, and others. *The conference was held virtually due to the COVID-19 pandemic.

Application of Machine Learning in Smart Agriculture is the first book to present a multidisciplinary look at how technology can not only improve agricultural output, but the economic efficiency of that output as well. Through a global lens, the book approaches the subject from a technical perspective, providing important knowledge and insights for effective and efficient implementation and utilization of machine learning. As artificial intelligence techniques are being used to increase yield through optimal planting, fertilizing, irrigation, and harvesting, these are only part of the complex picture which must also take into account the economic investment and its optimized return. The performance of machine learning models improves over time as the various mathematical and statistical models are proven. Presented in three parts, Application of Machine Learning in Smart Agriculture looks at the fundamentals of smart agriculture; the economics of the technology in the agricultural marketplace; and a diverse representation of the tools and techniques currently available, and in development. This book is an important resource for advanced level students and professionals working with artificial intelligence, internet of things, technology and agricultural economics. Addresses the technology of smart agriculture from a technical perspective Reveals opportunities for technology to improve and enhance not only yield and quality, but the economic value of a food crop Discusses physical instruments, simulations, sensors, and markets for machine learning in agriculture

The book covers current developments in the field of computer system security using cryptographic algorithms and other security schemes for system as well as cloud. The proceedings compiles the selected research papers presented at ICE-TEAS 2023 Conference held at Jaipur Engineering College and Research Centre, Jaipur, India, during February 17–19, 2023. The book focuses on expert applications and artificial intelligence; information and application security; advanced computing; multimedia applications in forensics, security, and intelligence; and advances in web technologies: implementation and security issues.

The Optimum-Path Forest (OPF) classifier was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a variety of other applications such as remote sensing, electrical and petroleum engineering, and biology. In recent years, multi-label and semi-supervised versions were also developed to handle video classification problems. The book presents the principles, algorithms and applications of Optimum-Path Forest, giving the theory and state-of-the-art as well as insights into future directions. Presents the first book on Optimum-path Forest Shows how it can be used with Deep Learning Gives a wide range of applications Includes the methods, underlying theory and applications of Optimum-Path Forest (OPF)