Over the last few years, the area of Natural Language Processing has drastically grown in recognition, not only within the research and development community, but also with industry professionals. As NLP continues to be discussed and researched, certain areas continue to grow and mature. As a result, the need for advanced research and information is in high demand. Emerging Applications of Natural Language Processing: Concepts and New Research provides pertinent and vital information that researchers, postgraduate, doctoral students, and practitioners are seeking. This book compiles the latest discoveries and advances in NLP methodologies and applications while expanding upon various topics regarding the future of NLP.

In this era of healthcare applications predominantly occupy both individuals as well as the healthcare industries, so the need for analytical reports becomes an essential component for success. Especially, the IoT applications employed for healthcare which generate a huge amount of data that needs to be analyzed to produce the expected reports. To accomplish this task, a cloud-based analytical solution will be the right choice by which the reports can be generated faster compared to the traditional ways. In this book, the different analytical methods coupled with AI to analyze the IoT data on the cloud are discussed. This book applies AI in edge analytics for healthcare applications, analyzes the impact of tools and techniques in edge analytics for healthcare, and provides security solutions for edge analytics in healthcare IoT. Each chapter provides in-depth details on how to apply different analytical methods and tools for analytics of healthcare applications devised using IoT. As the IoT devices are generating huge amounts of data, it is highly essential to do the analytics on the cloud and this book showcases the mechanisms that are going to be applied for it. Hence, this book provides a holistic idea on how to do edge analytics for healthcare IoT using AI.

The advancement of security technologies has allowed information systems to store more crucial and sensitive data. With these advancements, organisations turn to physiological and behavioral methods of identification in order to guard against unwanted intrusion. Research Developments in Biometrics and Video Processing Techniques investigates advanced techniques in user identification and security, including retinal, facial, and finger print scans as well as signature and voice authentication models. Through its in-depth examination of computer vision applications and other biometric security technologies, this reference volume will provide researchers, engineers, developers, and students with insight into the latest research on enhanced security systems design and development.

This book systematically presents the operating principles and technical characteristics of the main radio navigating systems (RNSs) that make it possible to adequately evaluate the corresponding scratch indexes and levels of air safety for air vehicles, the chief concern of the International Civil Aviation Organization (ICAO). The book discusses how RNS systems substantially determine navigation accuracy and reliability, and therefore air safety; in addition, it presents practical solutions to problems arising in the operation and development of RNS systems.

The emergence of artificial intelligence has created a vast amount of advancements within various professional sectors and has transformed the way organizations conduct themselves. The implementation of intelligent systems has assisted with developing traditional processes including decision making, risk management, and security. An area that requires significant attention and research is how these companies are becoming accustomed to computer intelligence and applying this technology to their everyday practices. Transdisciplinary Perspectives on Risk Management and Cyber Intelligence is a pivotal reference source that provides vital research on the application of intelligent systems within various professional sectors as well as the exploration of theories and empirical findings. While highlighting topics such as decision making, cognitive science, and knowledge management, this publication explores the management of risk and uncertainty using training exercises, as well as the development of managerial intelligence competency. This book is ideally designed for practitioners, educators, researchers, policymakers, managers, developers, analysts, politicians, and students seeking current research on modern approaches to the analysis and performance of cyber intelligence.

This book discusses the computational geometry, topology and physics of digital images and video frame sequences. This trio of computational approaches encompasses the study of shape complexes, optical vortex nerves and proximities embedded in triangulated video frames and single images, while computational geometry focuses on the geometric structures that infuse triangulated visual scenes. The book first addresses the topology of cellular complexes to provide a basis for an introductory study of the computational topology of visual scenes, exploring the fabric, shapes and structures typically found in visual scenes. The book then examines the inherent geometry and topology of visual scenes, and the fine structure of light and light caustics of visual scenes, which bring into play catastrophe theory and the appearance of light caustic folds and cusps. Following on from this, the book introduces optical vortex nerves in triangulated digital images. In this context, computational physics is synonymous with the study of the fine structure of light choreographed in video frames. This choreography appears as a sequence of snapshots of light reflected and refracted from surface shapes, providing a solid foundation for detecting, analyzing and classifying visual scene shapes.

This book presents a variety of perspectives on vision-based applications. These contributions are focused on optoelectronic sensors, 3D & 2D machine vision technologies, robot navigation, control schemes, motion controllers, intelligent algorithms and vision systems. The authors focus on applications of unmanned aerial vehicles, autonomous and mobile robots, industrial inspection applications and structural health monitoring. Recent advanced research in measurement and others areas where 3D & 2D machine vision and machine control play an important role, as well as surveys and reviews about vision-based applications. These topics are of interest to readers from diverse areas, including electrical, electronics and computer engineering, technologists, students and non-specialist readers. * Presents current research in image and signal sensors, methods, and 3D & 2D technologies in vision-based theories and applications; * Discusses applications such as daily use devices including robotics, detection, tracking and stereoscopic vision systems, pose estimation, avoidance of objects, control and data exchange for navigation, and aerial imagery processing; * Includes research contributions in scientific, industrial, and civil applications.

This book demonstrates an original concept for implementing the rough set theory in the construction of decision-making systems. It addresses three types of decisions, including those in which the information or input data is insufficient. Though decision-making and classification in cases with missing or inaccurate data is a common task, classical decision-making systems are not naturally adapted to it. One solution is to apply the rough set theory proposed by Prof. Pawlak. The proposed classifiers are applied and tested in two configurations: The first is an iterative mode in which a single classification system requests completion of the input data until an unequivocal decision (classification) is obtained. It allows us to start classification processes using very limited input data and supplementing it only as needed, which limits the cost of obtaining data. The second configuration is an ensemble mode in which several rough set-based classification systems achieve the unequivocal decision collectively, even though the systems cannot separately deliver such results.

The healthcare industry is starting to adopt digital twins to improve personalized medicine, healthcare organization performance, and new medicine and devices. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and healthcare organizations as well as drug and device manufacturers. Digital twins are digital representations of human physiology built on computer models. The use of digital twins in healthcare is revolutionizing clinical processes and hospital management by enhancing medical care with digital tracking and advancing modelling of the human body. These tools are of great help to researchers in studying diseases, new drugs, and medical devices. Digital Twins and Healthcare: Trends, Techniques, and Challenges facilitates the advancement and knowledge dissemination in methodologies and applications of digital twins in the healthcare and medicine fields. This book raises interest and awareness of the uses of digital twins in healthcare in the research community. Covering topics such as deep neural network, edge computing, and transfer learning method, this premier reference source is an essential resource for hospital administrators, pharmacists, medical professionals, IT consultants, students and educators of higher education, librarians, and researchers.

Medical internet of things (IoT)-based applications are being utilized in several industries and have been shown to provide significant advantages to users in critical health applications. Artificial intelligence (AI) plays a key role in the growth and success of medical IoT applications and IoT devices in the medical sector. To enhance revenue, improve competitive advantage, and increase consumer engagement, the use of AI with medical IoT should be encouraged in the healthcare and medical arena. Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications provides greater knowledge of how AI affects healthcare and medical efficacy in order to improve outputs. It focuses on a thorough and comprehensive introduction to machine learning. Covering topics such as patient treatment, cyber-physical systems, and telemedicine, this premier reference source is a dynamic resource for hospital administrators, medical professionals, government officials, students and faculty of higher education, librarians, researchers, and academicians.

Robotic systems consist of object or scene recognition, vision-based motion control, vision-based mapping, and dense range sensing, and are used for identification and navigation. As these computer vision and robotic connections continue to develop, the benefits of vision technology including savings, improved quality, reliability, safety, and productivity are revealed. Robotic Vision: Technologies for Machine Learning and Vision Applications is a comprehensive collection which highlights a solid framework for understanding existing work and planning future research. This book includes current research on the fields of robotics, machine vision, image processing and pattern recognition that is important to applying machine vision methods in the real world.

The implementation of data and information analysis has become a trending solution within multiple professions. New tools and approaches are continually being developed within data analysis to further solve the challenges that come with professional strategy. Pattern recognition is an innovative method that provides comparison techniques and defines new characteristics within the information acquisition process. Despite its recent trend, a considerable amount of research regarding pattern recognition and its various strategies is lacking. Pattern Recognition Applications in Engineering is an essential reference source that discusses various strategies of pattern recognition algorithms within industrial and research applications and provides examples of results in different professional areas including electronics, computation, and health monitoring. Featuring research on topics such as condition monitoring, data normalization, and bio-inspired developments, this book is ideally designed for analysts; researchers; civil, mechanical, and electronic engineers; computing scientists; chemists; academicians; and students.

This book is a collection of essays exploring adaptive systems from many perspectives, ranging from computational applications to models of adaptation in living and social systems. The essays on computation discuss history, theory, applications, and possible threats of adaptive and evolving computations systems. The modeling chapters cover topics such as evolution in microbial populations, the evolution of cooperation, and how ideas about evolution relate to economics.
The title Perspectives on Adaptation in Natural and Artificial Systems honors John Holland, whose 1975 Book, Adaptation in Natural and Artificial Systems has become a classic text for many disciplines in which adaptation play a central role. The essays brought together here were originally written to honor John Holland, and span most of the different areas touched by his wide-ranging and influential research career. The authors include some of the most prominent scientists in the fields of artificial intelligence evolutionary computation, and complex adaptive systems. Taken together, these essays present a broad modern picture of current research on adaptation as it relates to computers, living systems, society, and their complex interactions.

Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. There are considerable interests and applications in forecasting using neural networks. Neural Networks in Business Forecasting provides for researchers and practitioners some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.

This collection represents the primary reference work for researchers and students in the area of Temporal Reasoning in Artificial Intelligence. Temporal reasoning has a vital role to play in many areas, particularly Artificial Intelligence. Yet, until now, there has been no single volume collecting together the breadth of work in this area. This collection brings together the leading researchers in a range of relevant areas and provides an coherent description of the breadth of activity concerning temporal reasoning in the filed of Artificial Intelligence.
Key Features:
- Broad range: foundations; techniques and applications
- Leading researchers around the world have written the chapters
- Covers many vital applications
- Source book for Artificial Intelligence, temporal reasoning
- Approaches provide foundation for many future software systems
- Broad range: foundations; techniques and applications
- Leading researchers around the world have written the chapters
- Covers many vital applications
- Source book for Artificial Intelligence, temporal reasoning
- Approaches provide foundation for many future software systems

This volume presents several machine intelligence technologies, developed over recent decades, and illustrates how they can be combined in application. One application, the detection of dementia from patterns in speech, is used throughout to illustrate these combinations. This application is a classic stationary pattern detection task, so readers may easily see how these combinations can be applied to other similar tasks. The expositions of the methods are supported by the basic theory they rest upon, and their application is clearly illustrated. The book's goal is to allow readers to select one or more of these methods to quickly apply to their own tasks. Includes a variety of machine intelligent technologies and illustrates how they can work together Shows evolutionary feature subset selection combined with support vector machines and multiple classifiers combined Includes a running case study on intelligent processing relating to Alzheimer's / dementia detection, in addition to several applications of the machine hybrid algorithms

The general focus of this book is on multimodal communication, which captures the temporal patterns of behavior in various dialogue settings. After an overview of current theoretical models of verbal and nonverbal communication cues, it presents studies on a range of related topics: paraverbal behavior patterns in the classroom setting; a proposed optimal methodology for conversational analysis; a study of time and mood at work; an experiment on the dynamics of multimodal interaction from the observer's perspective; formal cues of uncertainty in conversation; how machines can know we understand them; and detecting topic changes using neural network techniques. A joint work bringing together psychologists, communication scientists, information scientists and linguists, the book will be of interest to those working on a wide range of applications from industry to home, and from health to security, with the main goals of revealing, embedding and implementing a rich spectrum of information on human behavior.

The discovery and development of new computational methods have expanded the capabilities and uses of simulations. With agent-based models, the applications of computer simulations are significantly enhanced. Multi-Agent-Based Simulations Applied to Biological and Environmental Systems is a pivotal reference source for the latest research on the implementation of autonomous agents in computer simulation paradigms. Featuring extensive coverage on relevant applications, such as biodiversity conservation, pollution reduction, and environmental risk assessment, this publication is an ideal source for researchers, academics, engineers, practitioners, and professionals seeking material on various issues surrounding the use of agent-based simulations.

a) Provides basic concepts of Natural Language Processing for getting started from scratch. b) Introduces advanced concepts for scaling, deep learning and real-world issues seen in the industry. c) Provides applications of Natural Language Processing over a diverse set of 15 industry verticals. d) Shares practical implementation including Python code, tools and techniques for a variety of Natural Language Processing applications and industrial products for a hands-on experience. e) Gives readers a sense of all there is to build successful Natural Language Processing projects: the concepts, applications, opportunities and hands-on material.

The main purpose of this book is not only to present recent studies and advances in the field of social science research, but also to stimulate discussion on related practical issues concerning statistics, mathematics, and economics. Accordingly, a broad range of tools and techniques that can be used to solve problems on these topics are presented in detail in this book, which offers an ideal reference work for all researchers interested in effective quantitative and qualitative tools. The content is divided into three major sections. The first, which is titled "Social work", collects papers on problems related to the social sciences, e.g. social cohesion, health, and digital technologies. Papers in the second part, "Education and teaching issues," address qualitative aspects, education, learning, violence, diversity, disability, and ageing, while the book's final part, "Recent trends in qualitative and quantitative models for socio-economic systems and social work", features contributions on both qualitative and quantitative issues. The book is based on a scientific collaboration, in the social sciences, mathematics, statistics, and economics, among experts from the "Pablo de Olavide" University of Seville (Spain), the "University of Defence" of Brno (Czech Republic), the "G. D'Annunzio" University of Chieti-Pescara (Italy) and "Alexandru Ioan Cuza University" of Iasi (Romania). The contributions, which have been selected using a peer-review process, examine a wide variety of topics related to the social sciences in general, while also highlighting new and intriguing empirical research conducted in various countries. Given its scope, the book will appeal, in equal measure, to sociologists, mathematicians, statisticians and philosophers, and more generally to scholars and specialists in related fields.

As today's world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.

This book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a tutorial of basic concepts and techniques of deep learning. In addition, the book systematically investigates the main human centric analysis tasks of different levels, ranging from detection and segmentation to parsing and higher-level understanding. At last, it presents the state-of-the-art solutions based on deep learning for every task, as well as providing sufficient references and extensive discussions. Specifically, this book addresses four important research topics, including 1) localizing persons in images, such as face and pedestrian detection; 2) parsing persons in details, such as human pose and clothing parsing, 3) identifying and verifying persons, such as face and human identification, and 4) high-level human centric tasks, such as person attributes and human activity understanding. This book can serve as reading material and reference text for academic professors / students or industrial engineers working in the field of vision surveillance, biometrics, and human-computer interaction, where human centric visual analysis are indispensable in analysing human identity, pose, attributes, and behaviours for further understanding.

The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe-Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity.

This book contains selected papers from SEB-18, the Tenth International Conference on Sustainability in Energy and Buildings, which was organised by KES International and Griffith University and held in Gold Coast, Australia in June 2018. SEB-18 invited contributions on a range of topics related to sustainable buildings and renewable energy, and explored innovative topics regarding intelligent buildings and cities. Applicable areas included the sustainable design and of buildings, neighbourhoods and cities (built and natural environment); optimisation and modelling techniques; smart energy systems for smart cities; green information communications technology; and a broad range of solar, wind, wave and other renewable energy topics. The aim of the conference was to bring together researchers and government and industry professionals to discuss the future of energy in buildings, neighbourhoods and cities from a theoretical, practical, implementation and simulation perspective. In addition, SEB-18 offered an exciting opportunity to present, interact, and learn about the latest research in Sustainability in Energy and Buildings.