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Deep Eutectic Solvents in Liquid-Liquid Extraction: Correlation and Molecular Dynamics Simulation
by Papu Kumar Naik Nikhil Kumar Nabendu Paul Tamal BanerjeeDeep 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 Liquid-Liquid Extraction: Correlation and Molecular Dynamics Simulation
by Papu Kumar Naik Nikhil Kumar Nabendu Paul Tamal BanerjeeDeep 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.
The Deep Hot Biosphere: The Myth of Fossil Fuels
by Thomas GoldThis book sets forth a set of truly controversial and astonishing theories: First, it proposes that below the surface of the earth is a biosphere of greater mass and volume than the biosphere the total sum of living things on our planet's continents and in its oceans. Second, it proposes that the inhabitants of this subterranean biosphere are not plants or animals as we know them, but heat-loving bacteria that survive on a diet consisting solely of hydrocarbons that is, natural gas and petroleum. And third and perhaps most heretically, the book advances the stunning idea that most hydrocarbons on Earth are not the byproduct of biological debris ("fossil fuels"), but were a common constituent of the materials from which the earth itself was formed some 4.5 billion years ago.The implications are astounding. The theory proposes answers to often-asked questions: Is the deep hot biosphere where life originated, and do Mars and other seemingly barren planets contain deep biospheres? Even more provocatively, is it possible that there is an enormous store of hydrocarbons upwelling from deep within the earth that can provide us with abundant supplies of gas and petroleum?However far-fetched these ideas seem, they are supported by a growing body of evidence, and by the indisputable stature and seriousness Gold brings to any scientific debate. In this book we see a brilliant and boldly original thinker, increasingly a rarity in modern science, as he develops potentially revolutionary ideas about how our world works.
Deep Imaging in Tissue and Biomedical Materials: Using Linear and Nonlinear Optical Methods
by Lingyan Shi Robert R. AlfanoThe use of light for probing and imaging biomedical media is promising for the development of safe, noninvasive, and inexpensive clinical imaging modalities with diagnostic ability. The advent of ultrafast lasers has enabled applications of nonlinear optical processes, which allow deeper imaging in biological tissues with higher spatial resolution. This book provides an overview of emerging novel optical imaging techniques, Gaussian beam optics, light scattering, nonlinear optics, and nonlinear optical tomography of tissues and cells. It consists of pioneering works that employ different linear and nonlinear optical imaging techniques for deep tissue imaging, including the new applications of single- and multiphoton excitation fluorescence, Raman scattering, resonance Raman spectroscopy, second harmonic generation, stimulated Raman scattering gain and loss, coherent anti-Stokes Raman spectroscopy, and near-infrared and mid-infrared supercontinuum spectroscopy. The book is a comprehensive reference of emerging deep tissue imaging techniques for researchers and students working in various disciplines.
Deep Impact as a World Observatory Event: Proceedings of the ESO/VUB Conference held in Brussels, Belgium, 7-10 August 2006 (ESO Astrophysics Symposia)
by Hans Ulrich Käufl Christiaan SterkenIn the context of the NASA Deep Impact space mission, comet 9P/Tempel1 has been at the focus of an unprecedented worldwide long-term multi-wavelength observation campaign. The comet was also studied throughout its perihelion passage by various sources including the Deep Impact mission itself, the Hubble Space Telescope, Spitzer, Rosetta, XMM and all major ground-based observatories in a wavelength band from cm-wave radio astronomy to x-rays. This book includes the proceedings of a meeting that brought together an audience of theoreticians and observers - across the electromagnetic spectrum and from different sites and projects - to make full use of the massive ground-based observing data set. The coherent presentation of all data sets illustrates and examines the various observational constraints on modelling the cometary nucleus, cometary gas, cometary plasma, cometary dust, and the comet's surface and its activity.
Deep Impact Mission: Looking Beneath the Surface of a Cometary Nucleus
by C. T. RussellDeep Impact, or at least part of the flight system, is designed to crash into comet 9P/Tempel 1. This bold mission design enables cometary researchers to peer into the cometary nucleus, analyzing the excavated material with its imagers and spectrometers. The book describes the mission, its objectives, expected results, payload, and data products in articles written by those most closely involved. This mission has the potential of revolutionizing our understanding of the cometary nucleus.
Deep Inelastic Positron-Proton Scattering in the High-Momentum-Transfer Regime of HERA (Springer Tracts in Modern Physics #168)
by Ulrich F. KatzAbout three decades after the first experiments on deep inelastic lepton hadron scattering began to investigate the structure of hadrons, the history of this fruitful field of particle physics continues in the broad spectrum of research performed at the electron and positron proton collider HERA at DESY, where the multipurpose detectors ZEUS and H1 access ep scattering at a center of mass energy of 300 GeV and explore as yet uncharted kinematic realms of deep inelastic scattering. After the first years of data taking at HERA, each of the experiments has collected a total of roughly 40 pb 1 of e+p data, yielding sensitivity to deep inelastic e+p interactions at high four momentum transfers, Q2, where typi cal cross sections drop into the subpicobarn regime. This kinematic domain is characterized by electroweak unification, manifesting itself most markedly in the neutral and charged current cross sections, which approach an equal order of magnitude as Q2 rises above the square of the W and Z masses. Consequently, HERA allows, for the first time, studies of both types of pro cesses simultaneously with the same initial state conditions and in the same detector, and thus we can investigate the interplay of electroweak and strong forces governing the respective cross sections.
Deep Inelastic Scattering
by Robin Devenish Amanda Cooper-SarkarThis book provides an up-to-date, self-contained account of deep inelastic scattering in high-energy physics, intended for graduate students and physicists new to the subject. It covers the classic results which led to the quark-parton model of hadrons and the establishment of quantum chromodynamics as the theory of the strong nuclear force, in addition to new vistas in the subject opened up by the electron-proton collider HERA. The extraction of parton momentum distribution functions, a key input for physics at hadron colliders such as the Tevatron at Fermi Lab and the Large Hadron Collider at CERN, is described in detail. The challenges of the HERA data at 'low x' are described and possible explanations in terms of gluon dynamics and other models outlined. Other chapters cover: jet production at large momentum transfer and the determination of the strong coupling constant, electroweak interactions at very high momentum transfers, the extension of deep inelastic techniques to include hadronic probes, a summary of fully polarised inelastic scattering and the spin structure of the nucleon, and finally a brief account of methods in searching for signals 'beyond the standard model'.
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 and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets (Advances in Computer Vision and Pattern Recognition)
by Le Lu Yefeng Zheng Gustavo Carneiro Lin YangThis book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics (Advances in Computer Vision and Pattern Recognition)
by Le Lu Xiaosong Wang Gustavo Carneiro Lin YangThis 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
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 Linguistic Representation
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 Nguyen Hoang Phuong Vladik KreinovichThis 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 and XAI Techniques for Anomaly Detection: Integrating Theory And Practice Of Explainable Deep Learning Anomaly Detection
by Cher SimonIntegrate the theory and practice of deep anomaly explainability
Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
by Yinpeng Wang Qiang RenThis 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-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
by Yinpeng Wang Qiang RenThis 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 for Biomedical Applications (Artificial Intelligence (AI): Elementary to Advanced Practices)
by Utku Kose Omer Deperlioglu D. Jude HemanthThis 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 Applications (Artificial Intelligence (AI): Elementary to Advanced Practices)
by Utku Kose Omer Deperlioglu D. Jude HemanthThis 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 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 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 Data Analytics: Foundations, Biomedical Applications, and Challenges
by Nilanjan Dey Himansu Das Chittaranjan PradhanDeep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications.Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networksProvides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning