Download Free Machine Learning Approaches For Urban Computing Book in PDF and EPUB Free Download. You can read online Machine Learning Approaches For Urban Computing and write the review.

This book discusses various machine learning applications and models, developed using heterogeneous data, which helps in a comprehensive prediction, optimization, association analysis, cluster analysis and classification-related applications for various activities in urban area. It details multiple types of data generating from urban activities and suitability of various machine learning algorithms for handling urban data. The book is helpful for researchers, academicians, faculties, scientists and geospatial industry professionals for their research work and sets new ideas in the field of urban computing.
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
An authoritative treatment of urban computing, offering an overview of the field, fundamental techniques, advanced models, and novel applications. Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. Using today's large-scale computing infrastructure and data gathered from sensing technologies, urban computing combines computer science with urban planning, transportation, environmental science, sociology, and other areas of urban studies, tackling specific problems with concrete methodologies in a data-centric computing framework. This authoritative treatment of urban computing offers an overview of the field, fundamental techniques, advanced models, and novel applications. Each chapter acts as a tutorial that introduces readers to an important aspect of urban computing, with references to relevant research. The book outlines key concepts, sources of data, and typical applications; describes four paradigms of urban sensing in sensor-centric and human-centric categories; introduces data management for spatial and spatio-temporal data, from basic indexing and retrieval algorithms to cloud computing platforms; and covers beginning and advanced topics in mining knowledge from urban big data, beginning with fundamental data mining algorithms and progressing to advanced machine learning techniques. Urban Computing provides students, researchers, and application developers with an essential handbook to an evolving interdisciplinary field.
The perception of smart cities encompasses a strategy that uses different types of technologies, artificial intelligence (AI), and machine learning and in which, through the internet of things (IoT) and sensor-based data collection, the strategy extrapolates information using insights gained from that data to manage or monitor or track assets, resources, and services efficiently in an urban area. Both these models deeply affect the localities where they are applied and can create together immense possibilities for urban recovery, better quality of life, physical and mental health protection, and economic and social redevelopment. Smart Cities and Machine Learning in Urban Health promotes interdisciplinary work that develops and illustrates the concept of resilience in relation to smart city and machine learning. The book examines the ability of an area and its communities to recover quickly from difficulties; the rigidness and resistance of an area and its communities to possible crisis; the ability of an area, its communities, infrastructure, and business to spring back into shape; and the responsiveness and mitigation towards the crisis with a special look at the impact of the COVID-19 pandemic. The research's theoretical foundation rests on a wide range of non-architectural sources, primarily AI, sociology, urban studies, and technological development, but it explores everything on cases taken from real cities, thus transforming them into pieces of architectural interest. Covering topics such as carbon emissions, digital healthcare systems, and urban transformation, this book is an essential resource for graduate and post-graduate students, policymakers, researchers, university faculty, engineers, public management, hospital administration, professors, and academicians.
METAVERSE AND IMMERSIVE TECHNOLOGIES The book covers the multidimensional perspectives of the metaverse through the prism of virtual reality, augmented reality, blockchain, artificial intelligence, and IoT, ranging from rudimentary to advanced applications. This book provides a thorough explanation of how the technology behind metaverse and other virtual reality technologies are changing the world. The primary objective is to present the revolutionary innovation of the 21st century—the metaverse—and exhibit its wide range of applications in different domains. Although blockchain and VR/AR were the first popularly known applications of the metaverse, several other applications also exist. While some still believe the metaverse is overhyped, in reality, it is transforming almost every industry—healthcare, 3D, 4D, industry, game industry, business management, artificial intelligence, and IoT, just to name a few. This technological breakthrough not only paved the way for virtual reality but also provided useful solutions for other areas of technology. The unique nature of the technology, which is a single, shared, immersive, persistent, 3D virtual space where humans experience life in ways not possible in the physical world, makes it suitable for all real-world applications; it has great potential to transform business, and companies are already in the race for different product offerings. Audience AI and computer science researchers, engineers and graduate students, IT personnel in business as well as entrepreneurs and policymakers.
This book gives comprehensive insights into the application of AI, machine learning, and deep learning in developing efficient and optimal surveillance systems for both indoor and outdoor environments, addressing the evolving security challenges in public and private spaces. Mathematical Models Using Artificial Intelligence for Surveillance Systems aims to collect and publish basic principles, algorithms, protocols, developing trends, and security challenges and their solutions for various indoor and outdoor surveillance applications using artificial intelligence (AI). The book addresses how AI technologies such as machine learning (ML), deep learning (DL), sensors, and other wireless devices could play a vital role in assisting various security agencies. Security and safety are the major concerns for public and private places in every country. Some places need indoor surveillance, some need outdoor surveillance, and, in some places, both are needed. The goal of this book is to provide an efficient and optimal surveillance system using AI, ML, and DL-based image processing. The blend of machine vision technology and AI provides a more efficient surveillance system compared to traditional systems. Leading scholars and industry practitioners are expected to make significant contributions to the chapters. Their deep conversations and knowledge, which are based on references and research, will result in a wonderful book and a valuable source of information.
MACHINE LEARNING APPROACHES FOR CONVERGENCE OF IOT AND BLOCKCHAIN The unique aspect of this book is that its focus is the convergence of machine learning, IoT, and blockchain in a single publication. Blockchain technology and the Internet of Things (IoT) are two of the most impactful trends to have emerged in the field of machine learning. Although there are a number of books available solely on the subjects of machine learning, IoT and blockchain technology, no such book has been available which focuses on machine learning techniques for IoT and blockchain convergence until now. Thus, this book is unique in terms of the topics it covers. Designed as an essential guide for all academicians, researchers, and those in industry who are working in related fields, this book will provide insights into the convergence of blockchain technology and the IoT with machine learning. Highlights of the book include: Examines many industries such as agriculture, manufacturing, food production, healthcare, the military, and IT Security of the Internet of Things using blockchain and AI Developing smart cities and transportation systems using machine learning and IoT Audience The target audience of this book is professionals and researchers (artificial intelligence specialists, systems engineers, information technologists) in the fields of machine learning, IoT, and blockchain technology.
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and gaming have been successfully explored in recent years. Providing an accessible introduction to the field, this book covers model-based and model-free approaches, policy iteration, and policy search methods. It presents illustrative examples and state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. The book provides a bridge between RL and data mining and machine learning research.
FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.