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Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics: Techniques and Applications (Biomedical Engineering)
by Sujata Dash Joel J. P. C. Rodrigues Subhendu Kumar Pani Babita MajhiBiomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems
Deep Learning-Based Approaches for Sentiment Analysis (Algorithms for Intelligent Systems)
by Basant Agarwal Namita Mittal Srikanta Patnaik Richi NayakThis book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
Deep Learning-Based Detection of Catenary Support Component Defect and Fault in High-Speed Railways (Advances in High-speed Rail Technology)
by Zhigang Liu Wenqiang Liu Junping ZhongThis book focuses on the deep learning technologies and their applications in the catenary detection of high-speed railways. As the only source of power for high-speed trains, the catenary's service performance directly affects the safe operation of high-speed railways. This book systematically shows the latest research results of catenary detection in high-speed railways, especially the detection of catenary support component defect and fault. Some methods or algorithms have been adopted in practical engineering. These methods or algorithms provide important references and help the researcher, scholar, and engineer on pantograph and catenary technology in high-speed railways. Unlike traditional detection methods of catenary support component based on image processing, some advanced methods in the deep learning field, including convolutional neural network, reinforcement learning, generative adversarial network, etc., are adopted and improved in this book. The main contents include the overview of catenary detection of electrified railways, the introduction of some advance of deep learning theories, catenary support components and their characteristics in high-speed railways, the image reprocessing of catenary support components, the positioning of catenary support components, the detection of defect and fault, the detection based on 3D point cloud, etc.
Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
by Qiang Ren Yinpeng WangThis book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced. As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics.
Deep Learning-Powered Technologies: Autonomous Driving, Artificial Intelligence of Things (AIoT), Augmented Reality, 5G Communications and Beyond (Synthesis Lectures on Engineering, Science, and Technology)
by Khaled Salah MohamedThis book covers various, leading-edge deep learning technologies. The author discusses new applications of deep learning and gives insight into the integration of deep learning with various application domains, such as autonomous driving, augmented reality, AIOT, 5G and beyond.
Deep Learning: Algorithms and Applications (Studies in Computational Intelligence #865)
by Witold Pedrycz Shyi-Ming ChenThis book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
Deep Learning: Concepts and Architectures (Studies in Computational Intelligence #866)
by Witold Pedrycz Shyi-Ming ChenThis book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.
Deep Maps and Spatial Narratives (The Spatial Humanities)
by John Corrigan David J. Bodenhamer Trevor M. HarrisDeep maps are finely detailed, multimedia depictions of a place and the people, buildings, objects, flora, and fauna that exist within it and which are inseparable from the activities of everyday life. These depictions may encompass the beliefs, desires, hopes, and fears of residents and help show what ties one place to another. A deep map is a way to engage evidence within its spatio-temporal context and to provide a platform for a spatially-embedded argument. The essays in this book investigate deep mapping and the spatial narratives that stem from it. The authors come from a variety of disciplines: history, religious studies, geography and geographic information science, and computer science. Each applies the concepts of space, time, and place to problems central to an understanding of society and culture, employing deep maps to reveal the confluence of actions and evidence and to trace paths of intellectual exploration by making use of a new creative space that is visual, structurally open, multi-media, and multi-layered.
Deep Marine Mineral Resources
by Yves Fouquet Denis LacroixThe risks of shortages for some crucial metals and uncertainty about the land-based reserves of several others justify the search to diversify our sources of supply and investigate their potential. Mineral resources in the deep sea are attracting increasing interest with the progressive discovery of various forms of ores. France possesses large areas of deep seafloor in the three oceans as well as world-class human and technological resources and know-how, resulting from over 40 years of experience. This study takes stock of knowledge about mineralisations and associated metals, technologies for exploring and exploiting them, biodiversity and the potential impact of exploitation on the deep environment and the partnerships which are vital for France and Europe. This information will be useful for decision-makers in drawing up strategies, defining research and development programmes and in enhancing and developing commercial utilizations for these high-potential resources.
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
by Eric TopolOne of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard.Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.
Deep Mining Challenges: International Mining Forum 2009
by Volodymyr I. BondarenkoThe International Mining Forum is an annual meeting of scientists and professionals specializing in a broad area of mining sciences. The aim of the meeting is to exchange new ideas and experiences, evaluate previously implemented solutions and procedures and to discuss new ideas that might change the procedures, developments and the image of the mining industry as a whole.The topics covered in the proceedings of IMF 2009 are:- Mineral resources / reserves evaluation,- Classification of Fossil Energy and Mineral Resources,- Directions and Developments of Present-Day Mining.
Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
by Ruqiang Yan Zhibin ZhaoThe book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions.The authors first introduce basic applications of DL-enabled IFD, including auto-encoders, deep belief networks, and convolutional neural networks. Advanced topics of DL-enabled IFD are also explored, such as data augmentation, multi-sensor fusion, unsupervised deep transfer learning, neural architecture search, self-supervised learning, and reinforcement learning. Aiming to revolutionize the nature of IFD, Deep Neural Networks-Enabled Intelligent Fault Diangosis of Mechanical Systems contributes to improved efficiency, safety, and reliability of mechanical systems in various industrial domains.The book will appeal to academic researchers, practitioners, and students in the fields of intelligent fault diagnosis, prognostics and health management, and deep learning.
Deep Oil Spills: Facts, Fate, and Effects
by Steven A. Murawski Cameron H. Ainsworth Sherryl Gilbert David J. Hollander Claire B. Paris Michael Schlüter Dana L. WetzelThe demand for oil and gas has brought exploration and production to unprecedented depths of the world’s oceans. Currently, over 50% of the oil from the Gulf of Mexico now comes from waters in excess of 1,500 meters (one mile) deep, where no oil was produced just 20 years ago. The Deepwater Horizon oil spill blowout did much to change the perception of oil spills as coming just from tanker accidents, train derailments, and pipeline ruptures. In fact, beginning with the Ixtoc 1 spill off Campeche, Mexico in 1979-1980, there have been a series of large spill events originating at the sea bottom and creating a myriad of new environmental and well control challenges. This volume explores the physics, chemistry, sub-surface oil deposition and environmental impacts of deep oil spills. Key lessons learned from the responses to previous deep spills, as well as unresolved scientific questions for additional research are highlighted, all of which are appropriate for governmental regulators, politicians, industry decision-makers, first responders, researchers and students wanting an incisive overview of issues surrounding deep-water oil and gas production.
Deep Past: A Novel
by Eugene Linden&“A gripping thriller . . . bends (if not blows) the mind with deep and compelling ideas about consciousness, intelligence, and our place in the world.&” —Douglas Preston, #1 New York Times–bestselling authorA routine dig in Kazakhstan takes a radical turn for thirty-two-year-old anthropologist Claire Knowland when a stranger turns up at the site with a bizarre find from a remote section of the desolate Kazakh Steppe. Her initial skepticism of this mysterious discovery gives way to a realization that the find will shake the very foundations of our understanding of evolution and intelligence.Corrupt politics of Kazakhstan force Claire to take reckless chances with the discovery. Among the allies she gathers in her fight to save herself and bring the discovery to light is Sergei Anachev, a brilliant but enigmatic Russian geologist who becomes her unlikely protector even as he deals with his own unknown crisis.Ultimately, Claire finds herself fighting not just for the discovery and her academic reputation, but for her very life as great power conflict engulfs the unstable region and an unscrupulous oligarch attempts to take advantage of the chaos.Drawing on Eugene Linden&’s celebrated nonfiction investigations into what makes humans different from other species, this international thriller mixes fact and the fantastical, the realities of academic politics, and high stakes geopolitics—engaging the reader every step of the way.&“An excellent thriller with real meat on the bones . . . makes you think as well as sweat.&” —Lee Child, #1 New York Times–bestselling author &“A fascinating thriller . . . Linden does a masterly job of integrating intriguing speculative science into a page-turning plot.&” —Publishers Weekly (starred review)
Deep Reinforcement Learning Processor Design for Mobile Applications
by Hoi-Jun Yoo Juhyoung LeeThis book discusses the acceleration of deep reinforcement learning (DRL), which may be the next step in the burst success of artificial intelligence (AI). The authors address acceleration systems which enable DRL on area-limited & battery-limited mobile devices. Methods are described that enable DRL optimization at the algorithm-, architecture-, and circuit-levels of abstraction.
Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and Implementation
by Dusit Niyato Ekram Hossain Dinh Thai Hoang Nguyen Van Huynh Diep N. NguyenDeep Reinforcement Learning for Wireless Communications and Networking Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems Deep Reinforcement Learning for Wireless Communications and Networking presents an overview of the development of DRL while providing fundamental knowledge about theories, formulation, design, learning models, algorithms and implementation of DRL together with a particular case study to practice. The book also covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking. Covering new advanced models of DRL, e.g., deep dueling architecture and generative adversarial networks, as well as emerging problems considered in wireless networks, e.g., ambient backscatter communication, intelligent reflecting surfaces and edge intelligence, this is the first comprehensive book studying applications of DRL for wireless networks that presents the state-of-the-art research in architecture, protocol, and application design. Deep Reinforcement Learning for Wireless Communications and Networking covers specific topics such as: Deep reinforcement learning models, covering deep learning, deep reinforcement learning, and models of deep reinforcement learning Physical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer security Medium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell association Network layer applications, covering traffic routing, network classification, and network slicing With comprehensive coverage of an exciting and noteworthy new technology, Deep Reinforcement Learning for Wireless Communications and Networking is an essential learning resource for researchers and communications engineers, along with developers and entrepreneurs in autonomous systems, who wish to harness this technology in practical applications.
Deep Reinforcement Learning for Wireless Networks (SpringerBriefs in Electrical and Computer Engineering)
by Ying He F. Richard YuThis Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.. Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer scientists, programmers, and policy makers will also find this brief to be a useful tool.
Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach (Studies in Systems, Decision and Control #265)
by Shuai Li Yinyan Zhang Xuefeng ZhouThis book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances.It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution.Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.
Deep Reinforcement Learning: Fundamentals, Research and Applications
by Hao Dong Zihan Ding Shanghang ZhangDeep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.
Deep Sciences for Computing and Communications: First International Conference, IconDeepCom 2022, Chennai, India, March 17–18, 2022, Revised Selected Papers (Communications in Computer and Information Science #1719)
by Utku Kose Ali Kashif Bashir Kottilingam Kottursamy Annie UthraThis book constitutes selected papers presented during the First International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2022, held in Chennai, India, in March 2022.The 27 papers presented were thoroughly reviewed and selected from 97 submissions. They are organized in topical sections as follows: classification and regression problems for communication paradigms; deep learning and vision computing; deep- recurrent neural network (RNN) for industrial informatics; extended AI for heterogeneous edge.
Deep Sciences for Computing and Communications: Second International Conference, IconDeepCom 2023, Chennai, India, April 20–22, 2023, Proceedings, Part I (Communications in Computer and Information Science #2176)
by Utku Kose Ali Kashif Bashir Kottilingam Kottursamy Annie Uthra R. Gunasekaran Raja Revathi Appavoo Vimaladevi MadhivananThis two-volume set, CCIS 2176-2177, constitutes the proceedings from the Second International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023, held in Chennai, India, in April 2023. The 74 full papers and 8 short papers presented here were thoroughly reviewed and selected from 252 submissions. The papers presented in these two volumes are organized in the following topical sections: Part I: Applications of Block chain for Digital Landscape; Deep Learning approaches for Multipotent Application; Machine Learning Techniques for Intelligent Applications; Industrial use cases of IOT; NLP for Linguistic Support; Convolution Neural Network for Vision Applications. Part II: Optimized Wireless Sensor Network Protocols; Cryptography Applications for Enhanced Security; Implications of Networking on Society; Deep Learning Model for Health informatics; Web Application for Connected Communities; Intelligent Insights using Image Processing; Precision Flood Prediction Models.
Deep Sciences for Computing and Communications: Second International Conference, IconDeepCom 2023, Chennai, India, April 20–22, 2023, Proceedings, Part II (Communications in Computer and Information Science #2177)
by Utku Kose Ali Kashif Bashir Kottilingam Kottursamy Annie Uthra R. Gunasekaran Raja Revathi Appavoo Vimaladevi MadhivananThis two-volume set, CCIS 2176-2177, constitutes the proceedings from the Second International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023, held in Chennai, India, in April 2023. The 74 full papers and 8 short papers presented here were thoroughly reviewed and selected from 252 submissions. The papers presented in these two volumes are organized in the following topical sections: Part I: Applications of Block chain for Digital Landscape; Deep Learning approaches for Multipotent Application; Machine Learning Techniques for Intelligent Applications; Industrial use cases of IOT; NLP for Linguistic Support; Convolution Neural Network for Vision Applications. Part II: Optimized Wireless Sensor Network Protocols; Cryptography Applications for Enhanced Security; Implications of Networking on Society; Deep Learning Model for Health informatics; Web Application for Connected Communities; Intelligent Insights using Image Processing; Precision Flood Prediction Models.
Deep Space Communications
by Jim TaylorA collection of some of the Jet Propulsion Laboratory's space missions selected to represent the planetary communications designs for a progression of various types of missions The text uses a case study approach to show the communications link performance resulting from the planetary communications design developed by the Jet Propulsion Laboratory (JPL). This is accomplished through the description of the design and performance of six representative planetary missions. These six cases illustrate progression through time of the communications system's capabilities and performance from 1970s technology to the most recent missions. The six missions discussed in this book span the Voyager for fly-bys in the 1970s, Galileo for orbiters in the 1980s, Deep Space 1 for the 1990s, Mars Reconnaissance Orbiter (MRO) for planetary orbiters, Mars Exploration Rover (MER) for planetary rovers in the 2000s, and the MSL rover in the 2010s. Deep Space Communications: Provides an overview of the Deep Space Network and its capabilities Examines case studies to illustrate the progression of system design and performance from mission to mission and provides a broad overview of the missions systems described Discusses actual flight mission telecom performance of each system Deep Space Communications serves as a reference for scientists and engineers interested in communications systems for deep-space telecommunications link analysis and design control. Jim Taylor is a principal engineer at JPL, working on telecommunications analysis, ground-system implementation, and flight operations for deep-space and Earth-orbiting projects. He was the founding telecommunications member of JPL's Spaceflight Significant Events Group, now called Lessons Learned. He received the NASA Exceptional Achievement Medal in 2000 for innovative use of the DS1 communications systems and the NASA Exceptional Service Medal in 2006 for operational development and support on Deep Impact.
Deep Space Propulsion
by K. F. LongThe technology of the next few decades could possibly allow us to explore with robotic probes the closest stars outside our Solar System, and maybe even observe some of the recently discovered planets circling these stars. This book looks at the reasons for exploring our stellar neighbors and at the technologies we are developing to build space probes that can traverse the enormous distances between the stars. In order to reach the nearest stars, we must first develop a propulsion technology that would take our robotic probes there in a reasonable time. Such propulsion technology has radically different requirements from conventional chemical rockets, because of the enormous distances that must be crossed. Surprisingly, many propulsion schemes for interstellar travel have been suggested and await only practical engineering solutions and the political will to make them a reality. This is a result of the tremendous advances in astrophysics that have been made in recent decades and the perseverance and imagination of tenacious theoretical physicists. This book explores these different propulsion schemes - all based on current physics - and the challenges they present to physicists, engineers, and space exploration entrepreneurs. This book will be helpful to anyone who really wants to understand the principles behind and likely future course of interstellar travel and who wants to recognizes the distinctions between pure fantasy (such as Star Trek's 'warp drive') and methods that are grounded in real physics and offer practical technological solutions for exploring the stars in the decades to come.
Deep State
by Walter Jon WilliamsBy day Dagmar Shaw orchestrates vast games with millions of players spanning continents. By night, she tries to forget the sound of a city collapsing in flames around her. She tries to forget the faces of her friends as they died in front of her. She tries to forget the blood on her own hands. But then an old friend approaches Dagmar with a project. The project he pitches is so insane and so ambitious, she can't possibly say no. But this new venture will lead her from the world of alternate-reality gaming to one even more complex. A world in which the players are soldiers and spies and the name of the game is survival.