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Shape grammar and space syntax have been separately developed but rarely combined in any significant way. The first of these is typically used to investigate or generate the formal or geometric properties of architecture, while the second is used to analyze the spatial, topological, or social properties of architecture. Despite the reciprocal relationship between form and space in architecture--it is difficult to conceptualize a completed building without a sense of both of these properties--the two major computational theories have been largely developed and applied in isolation from each another. Grammatical and Syntactical Approaches in Architecture: Emerging Research and Opportunities is a critical scholarly resource that explores the relationship between shape grammar and space syntax for urban planning and architecture and enables the creative discovery of both the formal and spatial features of an architectural style or type. This book, furthermore, presents a new method to selectively capture aspects of both the grammar and syntax of architecture. Featuring a range of topics such as mathematical analysis, spatial configuration, and domestic architecture, this book is essential for architects, policymakers, urban planners, researchers, academicians, and students.
Information in today's advancing world is rapidly expanding and becoming widely available. This eruption of data has made handling it a daunting and time-consuming task. Natural language processing (NLP) is a method that applies linguistics and algorithms to large amounts of this data to make it more valuable. NLP improves the interaction between humans and computers, yet there remains a lack of research that focuses on the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative research on the methods and applications of linguistic information processing and its computational properties. This publication will support readers with performing sentence classification and language generation using neural networks, apply deep learning models to solve machine translation and conversation problems, and apply deep structured semantic models on information retrieval and natural language applications. While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data analysts, data scientists, academics, researchers, and students seeking current research on the fundamental concepts and techniques of natural language processing.
With the continual development of professional industries in today's modernized world, certain technologies have become increasingly applicable. Cyber-physical systems, specifically, are a mechanism that has seen rapid implementation across numerous fields. This is a technology that is constantly evolving, so specialists need a handbook of research that keeps pace with the advancements and methodologies of these devices. Tools and Technologies for the Development of Cyber-Physical Systems is an essential reference source that discusses recent advancements of cyber-physical systems and its application within the health, information, and computer science industries. Featuring research on topics such as autonomous agents, power supply methods, and software assessment, this book is ideally designed for data scientists, technology developers, medical practitioners, computer engineers, researchers, academicians, and students seeking coverage on the development and various applications of cyber-physical systems.