- Table View
- List View
Deep Eutectic Solvents for Pretreatment of Lignocellulosic Biomass (SpringerBriefs in Applied Sciences and Technology)
by Pratima BajpaiThis book focuses on the properties of deep eutectic solvents (DESs) and recent advances in their application in lignocellulosic biomass processing. Lignocellulosic biomass conversion to biofuels, biochemicals and other value-added products has attracted global attention because it is a readily available, inexpensive and renewable resource. However, in order for biomass technologies to be commercially viable, biomass recalcitrance needs to be cost-effectively reduced. Deep eutectic solvents (DESs) are new ‘green' solvents that have the high potential for biomass processing thanks to their low cost, low toxicity, biodegradability, and easy recycling and reuse. After an overview of the current lignocellulosic biomass pretreatment, the book discusses the synthesis and physiochemical properties of DESs, as well as key findings on the effects of DES on cellulose, hemicellulose and lignin solubilization, biomass pretreatment and biomass crystallinity. It then addresses the enzymatic hydrolysis performance of DES-pretreated solids, compatibility of DESs with enzymes and microorganisms, and the recycling potential of DESs. Lastly, it compares DESs with ionic liquids, and examines the challenges and opportunities relating to extending the use of DESs in lignocellulosic processing.
Deep Eutectic Solvents in Liquid-Liquid Extraction: Correlation and Molecular Dynamics Simulation
by Tamal Banerjee Nikhil Kumar Papu Kumar Naik Nabendu PaulDeep eutectic solvents (DESs) are a new class of green solvents that open a whole new world of opportunities for separation challenges. This book comprehensively provides a detailed discussion of their application as an extractive solvent in separation processes, adopting molecular dynamics (MD) simulations for atomistic insight into the solute transfer across bi-phasic systems. Furthermore, it explains ternary and quaternary mixtures, including MD simulation of relevant DES systems. Features in this volume include the following: Applications of DESs in the extraction of aromatics and polyaromatics from fuel oil by liquid–liquid extraction Eutectic behavior with respect to hydrocarbon and aqueous solutions MD insights on extraction using DESs Possible industrial applicability of potential DESs Results from Gaussian, NAMD, and PACKMOL software packages This book is aimed at researchers and graduate students working in the field of fuels and petrochemicals, separation science, chromatography, and chemical processing and design.
Deep Eutectic Solvents in the Textile Industry
by Hafeezullah Memon Amjad Farooq Aamir Farooq Zongqian WangThis book comprehensively explores the fascinating intersection of deep eutectic solvents (DES) and nanocellulose, focusing specifically on their extraction methods and textile applications. It delves into the revolutionary role of deep eutectic solvents in nanocellulose extraction. Deep eutectic solvents are a class of non-toxic, low-cost, and environmentally friendly solvents formed by combining hydrogen bond donors and acceptors. They possess unique properties that make them highly suitable for dissolving cellulose and facilitating nanocellulose extraction with enhanced efficiency and sustainability. The book begins by providing a thorough overview of nanocellulose, its types, properties, and potential applications in the textile industry. It then delves into the fundamentals of deep eutectic solvents, their composition, properties, and synthesis methods. The subsequent chapters focus on the extraction techniques and strategies employed to obtain nanocellulose using deep eutectic solvents, highlighting the advantages and challenges associated with each method. It also discusses the potential modifications and functionalizations of nanocellulose to enhance its compatibility with textile applications, such as surface grafting, blending, and composite formation. The last part of the book shifts its focus to the applications of deep eutectic solvents in the textile industries. It explores the textile materials fibers, yarns, fabrics, and modification and dyeing and highlights the resulting improvements in mechanical strength, moisture management, thermal insulation, and UV protection.
Deep Eutectic Solvents: Synthesis, Properties, and Applications
by Gabriela Guillena Diego J. RamónA useful guide to the fundamentals and applications of deep eutectic solvents Deep Eutectic Solvents contains a comprehensive review of the use of deep eutectic solvents (DESs) as an environmentally benign alternative reaction media for chemical transformations and processes. The contributors cover a range of topics including synthesis, structure, properties, toxicity and biodegradability of DESs. The book also explores myriad applications in various disciplines, such as organic synthesis and (bio)catalysis, electrochemistry, extraction, analytical chemistry, polymerizations, (nano)materials preparation, biomass processing, and gas adsorption. The book is aimed at organic chemists, catalytic chemists, pharmaceutical chemists, biochemists, electrochemists, and others involved in the design of eco-friendly reactions and processes. This important book: -Explores the promise of DESs as an environmentally benign alternative to hazardous organic solvents -Covers the synthesis, structure, properties (incl. toxicity) as well as a wide range of applications -Offers a springboard for stimulating critical discussion and encouraging further advances in the field Deep Eutectic Solvents is an interdisciplinary resource for researchers in academia and industry interested in the many uses of DESs as an environmentally benign alternative reaction media.
Deep Freeze: The United States, the International Geophysical Year, and the Origins of Antarctica's Age of Science
by Dian Olson BelangerDian Olson Belanger tells the story of the pioneers who built viable communities, made vital scientific discoveries, and established Antarctica as a continent dedicated to peace and the pursuit of science, decades after the first explorers planted flags in the ice. In the tense 1950s, even as the world was locked in the Cold War, U.S. scientists, maintained by the Navy's Operation Deep Freeze, came together in Antarctica with counterparts from eleven other countries to participate in the International Geophysical Year (IGY). On July 1, 1957, they began systematic, simultaneous scientific observations of the south-polar ice and atmosphere. Their collaborative success over eighteen months inspired the Antarctic Treaty of 1959, which formalized their peaceful pursuit of scientific knowledge. Still building on the achievements of the individuals and distrustful nations thrown together by the IGY from mutually wary military, scientific, and political cultures, science prospers today and peace endures. The year 2007 marked the fiftieth anniversary of the IGY and the commencement of a new International Polar Year - a compelling moment to review what a singular enterprise accomplished in a troubled time. Belanger draws from interviews, diaries, memoirs, and official records to weave together the first thorough study of the dawn of Antarctica's scientific age. Deep Freeze offers absorbing reading for those who have ventured onto Antarctic ice and those who dream of it, as well as historians, scientists, and policy makers
Deep Future: The Next 100,000 Years of Life on Earth
by Curt StagerA Kirkus Reviews Best Nonfiction of 2011 title A bold, far-reaching look at how our actions will decide the planet's future for millennia to come.Imagine a planet where North American and Eurasian navies are squaring off over shipping lanes through an acidified, ice-free Arctic. Centuries later, their northern descendants retreat southward as the recovering sea freezes over again. And later still, future nations plan how to avert an approaching Ice Age... by burning what remains of our fossil fuels.These are just a few of the events that are likely to befall Earth and human civilization in the next 100,000 years. And it will be the choices we make in this century that will affect that future more than those of any previous generation. We are living at the dawn of the Age of Humans; the only question is how long that age will last.Few of us have yet asked, "What happens after global warming?" Drawing upon the latest, groundbreaking works of a handful of climate visionaries, Curt Stager's Deep Future helps us look beyond 2100 a.d. to the next hundred millennia of life on Earth.
Deep Green Resistance: Strategy to Save the Planet
by Derrick Jensen Aric Mcbay Lierre KeithFor years, Derrick Jensen has asked his audiences, "Do you think this culture will undergo a voluntary transformation to a sane and sustainable way of life?" No one ever says yes.Deep Green Resistance starts where the environmental movement leaves off: industrial civilization is incompatible with life. Technology can't fix it, and shopping--no matter how green--won't stop it. To save this planet, we need a serious resistance movement that can bring down the industrial economy. Deep Green Resistance evaluates strategic options for resistance, from nonviolence to guerrilla warfare, and the conditions required for those options to be successful. It provides an exploration of organizational structures, recruitment, security, and target selection for both aboveground and underground action. Deep Green Resistance also discusses a culture of resistance and the crucial support role that it can play.Deep Green Resistance is a plan of action for anyone determined to fight for this planet--and win.
Deep Homology?
by Held Lewis I. Jr.Humans and flies look nothing alike, yet their genetic circuits are remarkably similar. Here, Lewis I. Held, Jr compares the genetics and development of the two to review the evidence for deep homology, the biggest discovery from the emerging field of evolutionary developmental biology. Remnants of the operating system of our hypothetical common ancestor 600 million years ago are compared in chapters arranged by region of the body, from the nervous system, limbs and heart, to vision, hearing and smell. Concept maps provide a clear understanding of the complex subjects addressed, while encyclopaedic tables offer comprehensive inventories of genetic information. Written in an engaging style with a reference section listing thousands of relevant publications, this is a vital resource for scientific researchers, and graduate and undergraduate students.
Deep Jungle: Journey To The Heart Of The Rainforest
by Fred PearceDEEP JUNGLE is an exploration of the most alien and feared habitat on Earth. Starting with man's earliest recorded adventures, Fred Pearce journeys high into the canopy - home to two-thirds of all the creatures on our planet, many of whom never come down to earth. During his travels he encounters all manner of fantastic flora and fauna, including a frog that can glide from tree to tree, a spider that can drag live chickens into its burrow and a flower that smells of decaying flesh.It is in the jungle that Pearce discovers secrets about how evolution works, the intricate links that connect us all, and maybe even clues to where humans came from - here is the key to our future foods and medicines, our climate and our understanding of how life works. At the start of a new millennium Pearce asks why we continue to waste precious time - and billions of dollars - looking for signs of life elsewhere in our universe when the greatest range of life-forms that have ever existed lies right here on our doorstep. Today environmentalists say we are on the verge of destroying the last rainforests, and with them the planet's evolutionary crucible, and maybe even its ability to maintain life on Earth. But nature has a way of getting its own back. The Mayans and the people of Angkor went too far in manipulating nature and paid the ultimate price. Their civilisations died and the jungle returned. Nature reclaimed it's own and it may do so again ...
Deep Learners and Deep Learner Descriptors for Medical Applications (Intelligent Systems Reference Library #186)
by Lakhmi C. Jain Sheryl Brahnam Loris Nanni Stefano Ghidoni Rick BrattinThis book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects.
Deep Learning Applications in Medical Image Segmentation: Overview, Approaches, and Challenges
by Sajid Yousuf Bhat Aasia Rehman Muhammad AbulaishApply revolutionary deep learning technology to the fast-growing field of medical image segmentation Precise medical image segmentation is rapidly becoming one of the most important tools in medical research, diagnosis, and treatment. The potential for deep learning, a technology which is already revolutionizing practice across hundreds of subfields, is immense. The prospect of using deep learning to address the traditional shortcomings of image segmentation demands close inspection and wide proliferation of relevant knowledge. Deep Learning Applications in Medical Image Segmentation meets this demand with a comprehensive introduction and its growing applications. Covering foundational concepts and its advanced techniques, it offers a one-stop resource for researchers and other readers looking for a detailed understanding of the topic. It is deeply engaged with the main challenges and recent advances in the field of deep-learning-based medical image segmentation. Readers will also find: Analysis of deep learning models, including FCN, UNet, SegNet, Dee Lab, and many moreDetailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systemsRecent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structuresAnalyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosisExplores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentationIdentifies and discusses the key challenges faced in medical image segmentation using deep learning techniquesProvides an overview of the latest advancements, applications, and future trends in deep learning for medical image analysis Deep Learning Applications in Medical Image Segmentation is ideal for academics and researchers working with medical image segmentation, as well as professionals in medical imaging, data science, and biomedical engineering.
Deep Learning Techniques for Biomedical and Health Informatics (Studies in Big Data #68)
by Ajith Abraham Mamta Mittal Sujata Dash Biswa Ranjan Acharya Arpad KelemenThis book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model. This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health. It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields.
Deep Learning Technologies for the Sustainable Development Goals: Issues and Solutions in the Post-COVID Era (Advanced Technologies and Societal Change)
by T. P. Singh Virender Kadyan Chidiebere UgwuThis book provides insights into deep learning techniques that impact the implementation strategies toward achieving the Sustainable Development Goals (SDGs) laid down by the United Nations for its 2030 agenda, elaborating on the promises, limits, and the new challenges. It also covers the challenges, hurdles, and opportunities in various applications of deep learning for the SDGs. A comprehensive survey on the major applications and research, based on deep learning techniques focused on SDGs through speech and image processing, IoT, security, AR-VR, formal methods, and blockchain, is a feature of this book. In particular, there is a need to extend research into deep learning and its broader application to many sectors and to assess its impact on achieving the SDGs. The chapters in this book help in finding the use of deep learning across all sections of SDGs. The rapid development of deep learning needs to be supported by the organizational insight and oversight necessary for AI-based technologies in general; hence, this book presents and discusses the implications of how deep learning enables the delivery agenda for sustainable development.
Deep Learning and Computer Vision: Volume 1 (Algorithms for Intelligent Systems)
by Uma N. Dulhare Essam Halim HousseinThis book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics (Advances in Computer Vision and Pattern Recognition)
by Gustavo Carneiro Le Lu Lin Yang Xiaosong WangThis book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
Deep Learning and Linguistic Representation (Chapman And Hall/crc Machine Learning And Pattern Recognition Ser.)
by Shalom LappinThe application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.
Deep Learning and Other Soft Computing Techniques: Biomedical and Related Applications (Studies in Computational Intelligence #1097)
by Vladik Kreinovich Nguyen Hoang PhuongThis book focuses on the use of artificial intelligence (AI) and computational intelligence (CI) in medical and related applications. Applications include all aspects of medicine: from diagnostics (including analysis of medical images and medical data) to therapeutics (including drug design and radiotherapy) to epidemic- and pandemic-related public health policies.Corresponding techniques include machine learning (especially deep learning), techniques for processing expert knowledge (e.g., fuzzy techniques), and advanced techniques of applied mathematics (such as innovative probabilistic and graph-based techniques).The book also shows that these techniques can be used in many other applications areas, such as finance, transportation, physics. This book helps practitioners and researchers to learn more about AI and CI methods and their biomedical (and related) applications—and to further develop this important research direction.
Deep Learning for Advanced X-ray Detection and Imaging Applications
by Krzysztof Kris Iniewski Liang Kevin CaiThis book provides a comprehensive overview of the latest advances in applying Artificial Intelligence (AI) to advanced X-ray imaging, with a particular focus on its medical applications. Readers will discover why AI is set to revolutionize traditional signal processing and image reconstruction with vastly improved performance. The authors illustrate how Machine Learning (ML) and Deep Learning (DL) significantly advance X-ray detection analysis, image reconstruction, and other crucial steps. This book also reveals how these technologies enable photon counting detector-based X-ray Computed Tomography (CT), which has the potential not only to improve current CT images but also enable new clinical applications, such as providing higher spatial resolution, better soft tissue contrast, K-edge imaging, and simultaneous multi-contrast agent imaging.
Deep Learning for Biomedical Applications (Artificial Intelligence (AI): Elementary to Advanced Practices)
by D. Jude Hemanth Utku Kose Omer DeperliogluThis book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.
Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications
by Mourad ElloumiThis book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (AI), Machine Learning (ML) and DL with practical applications in Biology and Medicine. Certainly, the list of topics covered in this book is not exhaustive but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data analysis either focus on specific topics or lack technical depth. The chapters presented in this book were selected for quality and relevance. The book also presents experiments that provide qualitative and quantitative overviews in the field of biomedical data analysis. The reader will require some familiarity with AI, ML and DL and will learn about techniques and approaches that deal with the most important and/or the newest topics encountered in the field of DL for biomedical data analysis. He/she will discover both the fundamentals behind DL techniques and approaches, and their applications on biomedical data. This book can also serve as a reference book for graduate courses in Bioinformatics, AI, ML and DL. The book aims not only at professional researchers and practitioners but also graduate students, senior undergraduate students and young researchers. This book will certainly show the way to new techniques and approaches to make new discoveries.
Deep Learning for Biometrics (Advances in Computer Vision and Pattern Recognition)
by Bir Bhanu Ajay KumarThis timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined. Topics and features: addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities; revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition; examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition; discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples; presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories. Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning.
Deep Learning for Crack-Like Object Detection
by Kaige Zhang Heng-Da ChengComputer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surface, underground pipeline, bridge cracks, railway tracks etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems. This book discusses crack-like object detection problem comprehensively. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. It provides a detailed review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method. It includes examples of real-world problems, which are easy to understand and could be a good tutorial for introducing computer vision and machine learning.
Deep Learning for Healthcare Decision Making (River Publishers Series in Biomedical Engineering)
by Jyotir Moy Chatterjee Fadi Al-Turjman Vishal Jain Ishaani PriyadarshiniHealth care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. The health information industry is ready for big leaps and bounds in terms of growth and advancement. This book is an attempt to unveil the hidden potential of the enormous amount of health information and technology. Throughout this book, we attempt to combine numerous compelling views, guidelines, and frameworks to enable personalized health care service options through the successful application of deep learning frameworks. The progress of the health-care sector will be incremental as it learns from associations between data over time through the application of suitable AI, deep net frameworks, and patterns. The major challenge health care is facing is the effective and accurate learning of unstructured clinical data through the application of precise algorithms. Incorrect input data leading to erroneous outputs with false positives is intolerable in healthcare as patients’ lives are at stake. This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms. The specific focus of this book will be on the application of deep learning in any area of health care, including clinical trials, telemedicine, health records management, etc.
Deep Learning for Hydrometeorology and Environmental Science (Water Science and Technology Library #99)
by Vijay P. Singh Taesam Lee Kyung Hwa ChoThis book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited.Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
Deep Learning for Physical Scientists: Accelerating Research with Machine Learning
by Edward O. Pyzer-Knapp Matthew BenatanDiscover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field Deep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome. Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems. Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader. From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy: A thorough introduction to the basic classification and regression with perceptrons An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training An examination of multi-layer perceptrons for learning from descriptors and de-noising data Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images A treatment of Bayesian optimization for tuning deep learning architectures Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: •Basic classification and regression with perceptrons •Training