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Work Design: Occupational Ergonomics

Swarm Intelligence and Deep Evolution Evolutionary Approach to Artificial Intelligence

Swarm Intelligence and Deep Evolution Evolutionary Approach to Artificial Intelligence

The book provides theoretical and practical knowledge about swarm intelligence and evolutionary computation. It describes the emerging trends in deep learning that involve the integration of swarm intelligence and evolutionary computation with deep learning i. e. deep neuroevolution and deep swarms. The study reviews the research on network structures and hyperparameters in deep learning and attracting attention as a new trend in AI. A part of the coverage of the book is based on the results of practical examples as well as various real-world applications. The future of AI based on the ideas of swarm intelligence and evolution is also covered. The book is an introductory work for researchers. Approaches to the realization of AI and the emergence of intelligence are explained with emphasis on evolution and learning. It is designed for beginners who do not have any knowledge of algorithms or biology and explains the basics of neural networks and deep learning in an easy-to-understand manner. As a practical exercise in neuroevolution the book shows how to learn to drive a racing car and a helicopter using MindRender. MindRender is an AI educational software that allows the readers to create and play with VR programs and provides a variety of examples so that the readers will be able to create and understand AI. | Swarm Intelligence and Deep Evolution Evolutionary Approach to Artificial Intelligence

GBP 160.00
1

Deep Learning for Biomedical Applications

Deep Neural Network Applications

Deep Neural Network Applications

The world is on the verge of fully ushering in the fourth industrial revolution of which artificial intelligence (AI) is the most important new general-purpose technology. Like the steam engine that led to the widespread commercial use of driving machineries in the industries during the first industrial revolution; the internal combustion engine that gave rise to cars trucks and airplanes; electricity that caused the second industrial revolution through the discovery of direct and alternating current; and the Internet which led to the emergence of the information age AI is a transformational technology. It will cause a paradigm shift in the way’s problems are solved in every aspect of our lives and from it innovative technologies will emerge. AI is the theory and development of machines that can imitate human intelligence in tasks such as visual perception speech recognition decision-making and human language translation. This book provides a complete overview on the deep learning applications and deep neural network architectures. It also gives an overview on most advanced future-looking fundamental research in deep learning application in artificial intelligence. Research overview includes reasoning approaches problem solving knowledge representation planning learning natural language processing perception motion and manipulation social intelligence and creativity. It will allow the reader to gain a deep and broad knowledge of the latest engineering technologies of AI and Deep Learning and is an excellent resource for academic research and industry applications. | Deep Neural Network Applications

GBP 145.00
1

Game Design Deep Dive: Horror

Game Design Deep Dive: Horror

The Game Design Deep Dive series examines a specific game system or mechanic over the course of the history of the industry. This entry will examine the history and design of the horror genre and elements in video games. The author analyzes early video game examples including the differences between survival action-horror and psychological horror. Thanks to recent hits like Five Night’s at Freddy’s Bendy and the Ink Machine and recent Resident Evil titles the horror genre has seen a strong resurgence. For this book in the Game Design Deep Dive series Joshua Bycer will go over the evolution of horror in video games and game design and what it means to create a terrifying and chilling experience. FEATURES • Written for anyone interested in the horror genre anyone who wants to understand game design or anyone simply curious from a historical standpoint • Includes real game examples to highlight the discussed topics and mechanics • Explores the philosophy and aspects of horror that can be applied to any medium • Serves as a perfect companion for someone building their first game or as part of a game design classroom Joshua Bycer is a game design critic with more than eight years of experience critically analyzing game design and the industry itself. In that time through Game-Wisdom he has interviewed hundreds of game developers and members of the industry about what it means to design video games. He also strives to raise awareness about the importance of studying game design by giving lectures and presentations. His first book was 20 Essential Games to Study. He continues to work on the Game Design Deep Dive series. | Game Design Deep Dive: Horror

GBP 42.99
1

Deep Learning in Time Series Analysis

Deep Learning in Time Series Analysis

Deep learning is an important element of artificial intelligence especially in applications such as image classification in which various architectures of neural network e. g. convolutional neural networks have yielded reliable results. This book introduces deep learning for time series analysis particularly for cyclic time series. It elaborates on the methods employed for time series analysis at the deep level of their architectures. Cyclic time series usually have special traits that can be employed for better classification performance. These are addressed in the book. Processing cyclic time series is also covered herein. An important factor in classifying stochastic time series is the structural risk associated with the architecture of classification methods. The book addresses and formulates structural risk and the learning capacity defined for a classification method. These formulations and the mathematical derivations will help the researchers in understanding the methods and even express their methodologies in an objective mathematical way. The book has been designed as a self-learning textbook for the readers with different backgrounds and understanding levels of machine learning including students engineers researchers and scientists of this domain. The numerous informative illustrations presented by the book will lead the readers to a deep level of understanding about the deep learning methods for time series analysis. | Deep Learning in Time Series Analysis

GBP 115.00
1

Machine Learning and Deep Learning Techniques for Medical Image Recognition

Deep Learning in Computer Vision Principles and Applications

Deep Learning for Remote Sensing Images with Open Source Software

Deep Learning for Remote Sensing Images with Open Source Software

In today’s world deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing including landcover mapping forestry urban studies disaster mapping image restoration etc. Written with practitioners and students in mind this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data. Specific Features of this Book: The first book that explains how to apply deep learning techniques to public free available data (Spot-7 and Sentinel-2 images OpenStreetMap vector data) using open source software (QGIS Orfeo ToolBox TensorFlow) Presents approaches suited for real world images and data targeting large scale processing and GIS applications Introduces state of the art deep learning architecture families that can be applied to remote sensing world mainly for landcover mapping but also for generic approaches (e. g. image restoration) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills. Includes deep learning techniques through many step by step remote sensing data processing exercises.

GBP 31.99
1

Visual Object Tracking using Deep Learning

Visual Object Tracking using Deep Learning

This book covers the description of both conventional methods and advanced methods. In conventional methods visual tracking techniques such as stochastic deterministic generative and discriminative are discussed. The conventional techniques are further explored for multi-stage and collaborative frameworks. In advanced methods various categories of deep learning-based trackers and correlation filter-based trackers are analyzed. The book also: Discusses potential performance metrics used for comparing the efficiency and effectiveness of various visual tracking methods Elaborates on the salient features of deep learning trackers along with traditional trackers wherein the handcrafted features are fused to reduce computational complexity Illustrates various categories of correlation filter-based trackers suitable for superior and efficient performance under tedious tracking scenarios Explores the future research directions for visual tracking by analyzing the real-time applications The book comprehensively discusses various deep learning-based tracking architectures along with conventional tracking methods. It covers in-depth analysis of various feature extraction techniques evaluation metrics and benchmark available for performance evaluation of tracking frameworks. The text is primarily written for senior undergraduates graduate students and academic researchers in the fields of electrical engineering electronics and communication engineering computer engineering and information technology. | Visual Object Tracking using Deep Learning

GBP 89.99
1

Prevention of Accidents at Work Proceedings of the 9th International Conference on the Prevention of Accidents at Work (WOS 2017) October 3

Prevention of Accidents at Work Proceedings of the 9th International Conference on the Prevention of Accidents at Work (WOS 2017) October 3

Prevention of Accidents at Work collects papers presented at the 9th International Conference on the Prevention of Accidents at Work (WOS 2017) held in Prague Czech Republic on October 3-6 2017 organized by the VSB-Technical University of Ostrava. The conference on current issues within occupational safety is organized under the umbrella of Workingonsafety. net (WOS. net). WOS. net is an international network of decision-makers researchers and professionals responsible for the prevention of accidents and trauma at work. The network aims to bring accident prevention experts together in order to facilitate the exchange of experience new findings and best practices between different countries and sectors. WOS. net is supported by the European Agency for Safety and Health at Work (EU-OSHA). The overall theme is safety management complexity in a changing society with the motto: Do we need a holistic approach? Underlying topics include: Foundations of safety science: theories principles methods and tools; Research to practice: achievements lessons learned and challenges; Risk management and safety culture: case studies best practices and further needs; Safety regulation: reasonable practicable approach; Education and training: prerequisite for safety; Complexity and safety: multidisciplinarity and inter-stakeholder views. Prevention of Accidents at Work should be valuable to researchers policy makers safety professionals labor inspectors labor administrators and other experts in the prevention of occupational accidents. | Prevention of Accidents at Work Proceedings of the 9th International Conference on the Prevention of Accidents at Work (WOS 2017) October 3

GBP 180.00
1

Deep Learning in Biomedical and Health Informatics Current Applications and Possibilities

Deep Learning in Biomedical and Health Informatics Current Applications and Possibilities

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging electronic health records genomics and sensing and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories techniques developments and applications of deep learning and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists biomedical engineers and healthcare professionals researching and developing deep learning techniques. In short the volume : Discusses the relationship between AI and healthcare and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr. . M. A. Jabbar is a professor and Head of the Department AI&ML Vardhaman College of Engineering Hyderabad Telangana India. Prof. (Dr. ) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs) Auburn Washington USA. Dr. . Onur Dogan is an assistant professor at İzmir Bakırçay University Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP) Portugal. Dr. . Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas Mexico. | Deep Learning in Biomedical and Health Informatics Current Applications and Possibilities

GBP 44.99
1

Dark World A Book on the Deep Dark Web

Polycyclic Aromatic Hydrocarbons in Work Atmospheres Occurrence and Determination

Ghosts in the Machine Rethinking Learning Work and Culture in Air Traffic Control

Ghosts in the Machine Rethinking Learning Work and Culture in Air Traffic Control

This book provides a socio-cultural analysis of the ways in which air traffic controllers formally and informally learn about their work and the active role that organisational cultures play in shaping interpretation and meaning. In particular it describes the significant role that organizational cultures have played in shaping what is valued by controllers about their work and its role as a filter in enabling or constraining conscious inquiry. The premise of the book is that informal learning is just as important in shaping what people know and value about their work and that this area is frequently overlooked. By using an interpretative research approach the book highlights the ways in which the social structure of work organisation culture and history interweaves with learning work to guide and shape what is regarded by controllers as important and what is not. It demonstrates how this social construction is quite different from a top-down corporate culture approach. Technological and organizational reform is leading to changes in work practice and to changes in relationships between workers within the organization. These have implications for anyone wishing to understand the dynamics of organizational life. As such this study provides insights into many of the changes that are occurring in the nature of work in many different industries. Previous research into learning in air traffic control has centred largely on cognitive individual performance performance within teams or more recently on performance at a systems level. By tracing the role of context in shaping formal and informal learning this book shows why interventions at these levels sometimes fail. | Ghosts in the Machine Rethinking Learning Work and Culture in Air Traffic Control

GBP 52.99
1

Fundamentals of Deep Excavations

MOST Work Measurement Systems

Design of Shallow and Deep Foundations

Design of Shallow and Deep Foundations

Design of Shallow and Deep Foundations introduces the concept of limit state calculations before focusing on shallow and deep foundations. The limit state combinations of actions are examined and practical calculation models of the bearing capacity and of the settlement are presented particularly from the results of Ménard pressuremeter tests and cone penetration tests. Attention is also given to the use of numerical methods which has been developed over the past twenty years. It provides an overview of various elements of ground-structure interaction that are pertinent for a refined design of both shallow and deep foundations such as allowable displacements of structures and ground-structure couplings. This guide will be useful to practising engineers and experts in design offices contracting companies and administrations as well as students and researchers in civil engineering. Though its focus is generally on the French practice it is more widely applicable to design based on or generally in line with Eurocode 7 with references to BS ENs. Roger Frank is an Honorary Professor at Ecole Nationale des Ponts et Chaussées (ENPC). From 1998 to 2004 he chaired the committee on Eurocode 7 on Geotechnical design. Fahd Cuira is the Scientific Director of Terrasol (Setec group) France. Since 2018 he has been in charge of the course on the design of geotechnical structures at ENPC. Sébastien Burlon is a Project Director at Terrasol (Setec group) France. He is involved in the evolution of Eurocode 7 and teaches several geotechnical courses especially at ENPC.

GBP 44.99
1

Structure of the Lithosphere and Deep Processes Proceedings of the 30th International Geological Congress Volume 4

Cognitive Dependability Engineering Managing Risks in Cyber-Physical-Social Systems under Deep Uncertainty

Cognitive Dependability Engineering Managing Risks in Cyber-Physical-Social Systems under Deep Uncertainty

The work is a context-oriented analysis and synthesis of complex engineered systems to ensure continuous and safe operations under conditions of uncertainty. The book is divided in four parts the first one comprises an overview of the development of systems engineering: starting with basics of Systems Science and Single Systems Engineering through System of Systems Engineering to Cognitive Systems Engineering. The Cognitive Systems Engineering model was based on the concept of imperfect knowledge acquisition and management. The second part shows the evolutionary character of the dependability concept over the last fifty years. Beginning from simple models based on the classical probability theory through the concepts of tolerating faults as well as resilience engineering we come to the assumptions of Cognitive Dependability Engineering (CDE) based on the concept of continuous smart operation both under normal and abnormal conditions. The subject of the next part is analysis and synthesis of Cyber-Physical-Social (CPS) Systems. The methodology consists of the following steps: modeling CPS systems' structure simulating their behavior in changing conditions and in situations of disruptions and finally assessing the dependability of the entire system based on CDE. The last part of the work answers the question of how to deal with risks in CPS systems in situations of high level of uncertainty. The concept of a Cognitive Digital Twin was introduced to support the process of solving complex problems by experts and on this basis a framework for cognitive dependability based problemsolving in CPS Systems operating under deep uncertainty was developed. The possibilities and purposefulness of using this framework have been demonstrated with three practical examples of disasters that have happened in the past and have been thoroughly analyzed. | Cognitive Dependability Engineering Managing Risks in Cyber-Physical-Social Systems under Deep Uncertainty

GBP 145.00
1

Medical Generalism Now Reclaiming the Knowledge Work of Modern Practice

Health and Safety Management An Alternative Approach to Reducing Accidents Injury and Illness at Work

Game Design Deep Dive Role Playing Games