Download Deep Learning and Parallel Computing Environment for Bioengineering Systems Ebook PDF

Deep Learning and Parallel Computing Environment for Bioengineering Systems

Deep Learning and Parallel Computing Environment for Bioengineering Systems
A Book

by Dr. Arun Kumar Sangaiah

  • Publisher : Academic Press
  • Release : 2019-07-26
  • Pages : 280
  • ISBN : 0128172932
  • Language : En, Es, Fr & De
GET BOOK

Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data

Intelligent IoT Systems in Personalized Health Care

Intelligent IoT Systems in Personalized Health Care
A Book

by Arun Kumar Sangaiah,Subhas Chandra Mukhopadhyay

  • Publisher : Academic Press
  • Release : 2020-12-01
  • Pages : 360
  • ISBN : 0128232048
  • Language : En, Es, Fr & De
GET BOOK

Intelligent IoT Systems in Personalized Health Care delivers a significant forum for the technical advancement of IoMT learning in parallel computing environments across biomedical engineering diversified domains and its applications. Pursuing an interdisciplinary approach, the book focuses on methods used to identify and acquire valid, potentially useful knowledge sources. The book presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT and its capabilities in solving a diverse range of problems for biomedical engineering and its real-world personalized health care applications. The book is well suited for researchers exploring the significance of IoT based architecture to perform predictive analytics of user activities in sustainable health. Presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT Illustrates state-of-the-art developments in new theories and applications of IoMT techniques as applied to parallel computing environments in biomedical engineering systems Presents concepts and technologies successfully used in the implementation of today's intelligent data-centric IoT systems and Edge-Cloud-Big data

Deep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics
A Book

by Dr. Basant Agarwal,Valentina E. Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma

  • Publisher : Academic Press
  • Release : 2020-01-14
  • Pages : 367
  • ISBN : 0128190620
  • Language : En, Es, Fr & De
GET BOOK

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis

Signal Processing and Machine Learning for Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data
A Book

by Ervin Sejdic,Tiago H. Falk

  • Publisher : CRC Press
  • Release : 2018-07-04
  • Pages : 606
  • ISBN : 149877346X
  • Language : En, Es, Fr & De
GET BOOK

Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.

Software Engineering Perspectives in Intelligent Systems

Software Engineering Perspectives in Intelligent Systems
Proceedings of 4th Computational Methods in Systems and Software 2020, Vol.2

by Radek Silhavy

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030633195
  • Language : En, Es, Fr & De
GET BOOK

Artificial Intelligence in the Age of Neural Networks and Brain Computing

Artificial Intelligence in the Age of Neural Networks and Brain Computing
A Book

by Robert Kozma,Cesare Alippi,Yoonsuck Choe,Francesco Carlo Morabito

  • Publisher : Academic Press
  • Release : 2018-10-30
  • Pages : 352
  • ISBN : 0128162503
  • Language : En, Es, Fr & De
GET BOOK

Artificial Intelligence in the Age of Neural Networks and Brain Computing demonstrates that existing disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity and smart autonomous search engines. The book covers the major basic ideas of brain-like computing behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as future alternatives. The success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel and Amazon can be interpreted using this book. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN) Authored by top experts, global field pioneers and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks

Machine Learning for Healthcare

Machine Learning for Healthcare
Handling and Managing Data

by Rashmi Agrawal,Jyotir Moy Chatterjee,Abhishek Kumar,Pramod Singh Rathore,Dac-Nhuong Le

  • Publisher : CRC Press
  • Release : 2020-12-09
  • Pages : 204
  • ISBN : 1000221881
  • Language : En, Es, Fr & De
GET BOOK

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.

Distributed and Parallel Computing

Distributed and Parallel Computing
6th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP, Melbourne, Australia, October 2-3, 2005, Proceedings

by Michael Hobbs,International Conference on Algorithms and Architectures for Parallel Processing,Andrzej Goscinski

  • Publisher : Springer Science & Business Media
  • Release : 2005-09-19
  • Pages : 448
  • ISBN : 9783540292357
  • Language : En, Es, Fr & De
GET BOOK

This book constitutes the refereed proceedings of the 6th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2005, held in Melbourne, Australia in October 2005. The 27 revised full papers and 25 revised short papers presented were carefully reviewed and selected from 95 submissions. The book covers new architectures of parallel and distributed systems, new system management facilities, and new application algorithms with special focus on two broad areas of parallel and distributed computing, i.e., architectures, algorithms and networks, and systems and applications.

Parallel Computing for Bioinformatics and Computational Biology

Parallel Computing for Bioinformatics and Computational Biology
Models, Enabling Technologies, and Case Studies

by Albert Y. Zomaya

  • Publisher : John Wiley & Sons
  • Release : 2006-04-14
  • Pages : 1000
  • ISBN : 0471756490
  • Language : En, Es, Fr & De
GET BOOK

Discover how to streamline complex bioinformatics applications withparallel computing This publication enables readers to handle more complexbioinformatics applications and larger and richer data sets. As theeditor clearly shows, using powerful parallel computing tools canlead to significant breakthroughs in deciphering genomes,understanding genetic disease, designing customized drug therapies,and understanding evolution. A broad range of bioinformatics applications is covered withdemonstrations on how each one can be parallelized to improveperformance and gain faster rates of computation. Current parallelcomputing techniques and technologies are examined, includingdistributed computing and grid computing. Readers are provided witha mixture of algorithms, experiments, and simulations that providenot only qualitative but also quantitative insights into thedynamic field of bioinformatics. Parallel Computing for Bioinformatics and Computational Biology isa contributed work that serves as a repository of case studies,collectively demonstrating how parallel computing streamlinesdifficult problems in bioinformatics and produces better results.Each of the chapters is authored by an established expert in thefield and carefully edited to ensure a consistent approach and highstandard throughout the publication. The work is organized into five parts: * Algorithms and models * Sequence analysis and microarrays * Phylogenetics * Protein folding * Platforms and enabling technologies Researchers, educators, and students in the field of bioinformaticswill discover how high-performance computing can enable them tohandle more complex data sets, gain deeper insights, and make newdiscoveries.

Scheduling in Parallel Computing Systems

Scheduling in Parallel Computing Systems
Fuzzy and Annealing Techniques

by Shaharuddin Salleh,Albert Y. Zomaya

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 170
  • ISBN : 1461550653
  • Language : En, Es, Fr & De
GET BOOK

Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques advocates the viability of using fuzzy and annealing methods in solving scheduling problems for parallel computing systems. The book proposes new techniques for both static and dynamic scheduling, using emerging paradigms that are inspired by natural phenomena such as fuzzy logic, mean-field annealing, and simulated annealing. Systems that are designed using such techniques are often referred to in the literature as `intelligent' because of their capability to adapt to sudden changes in their environments. Moreover, most of these changes cannot be anticipated in advance or included in the original design of the system. Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques provides results that prove such approaches can become viable alternatives to orthodox solutions to the scheduling problem, which are mostly based on heuristics. Although heuristics are robust and reliable when solving certain instances of the scheduling problem, they do not perform well when one needs to obtain solutions to general forms of the scheduling problem. On the other hand, techniques inspired by natural phenomena have been successfully applied for solving a wide range of combinatorial optimization problems (e.g. traveling salesman, graph partitioning). The success of these methods motivated their use in this book to solve scheduling problems that are known to be formidable combinatorial problems. Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques is an excellent reference and may be used for advanced courses on the topic.

Graduate Studies 1994-1995

Graduate Studies 1994-1995
The Complete Guide to Over 10,000 Postgraduate Courses in the UK (including PGCE)

by Brenda Radcliffe,Claire Waring

  • Publisher : Unknown Publisher
  • Release : 1994
  • Pages : 1355
  • ISBN : 9781853248771
  • Language : En, Es, Fr & De
GET BOOK

Past, Present, Parallel

Past, Present, Parallel
A Survey of Available Parallel Computer Systems

by Arthur Trew,Greg Wilson

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 392
  • ISBN : 1447118421
  • Language : En, Es, Fr & De
GET BOOK

Past, Present, Parallel is a survey of the current state of the parallel processing industry. In the early 1980s, parallel computers were generally regarded as academic curiosities whose natural environment was the research laboratory. Today, parallelism is being used by every major computer manufacturer, although in very different ways, to produce increasingly powerful and cost-effec- tive machines. The first chapter introduces the basic concepts of parallel computing; the subsequent chapters cover different forms of parallelism, including descriptions of vector supercomputers, SIMD computers, shared memory multiprocessors, hypercubes, and transputer-based machines. Each section concentrates on a different manufacturer, detailing its history and company profile, the machines it currently produces, the software environments it supports, the market segment it is targetting, and its future plans. Supplementary chapters describe some of the companies which have been unsuccessful, and discuss a number of the common software systems which have been developed to make parallel computers more usable. The appendices describe the technologies which underpin parallelism. Past, Present, Parallel is an invaluable reference work, providing up-to-date material for commercial computer users and manufacturers, and for researchers and postgraduate students with an interest in parallel computing.

Programming Collective Intelligence

Programming Collective Intelligence
Building Smart Web 2.0 Applications

by Toby Segaran

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2007-08-16
  • Pages : 362
  • ISBN : 0596550685
  • Language : En, Es, Fr & De
GET BOOK

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Parallel Computation and Computers for Artificial Intelligence

Parallel Computation and Computers for Artificial Intelligence
A Book

by J.S. Kowalik

  • Publisher : Springer
  • Release : 2011-09-26
  • Pages : 292
  • ISBN : 9781461291886
  • Language : En, Es, Fr & De
GET BOOK

It has been widely recognized that artificial intelligence computations offer large potential for distributed and parallel processing. Unfortunately, not much is known about designing parallel AI algorithms and efficient, easy-to-use parallel computer architectures for AI applications. The field of parallel computation and computers for AI is in its infancy, but some significant ideas have appeared and initial practical experience has become available. The purpose of this book has been to collect in one volume contributions from several leading researchers and pioneers of AI that represent a sample of these ideas and experiences. This sample does not include all schools of thought nor contributions from all leading researchers, but it covers a relatively wide variety of views and topics and in this sense can be helpful in assessing the state ofthe art. We hope that the book will serve, at least, as a pointer to more specialized literature and that it will stimulate interest in the area of parallel AI processing. It has been a great pleasure and a privilege to cooperate with all contributors to this volume. They have my warmest thanks and gratitude. Mrs. Birgitta Knapp has assisted me in the editorial task and demonstrated a great deal of skill and patience. Janusz S. Kowalik vii INTRODUCTION Artificial intelligence (AI) computer programs can be very time-consuming.

Distributed and Parallel Systems

Distributed and Parallel Systems
In Focus: Desktop Grid Computing

by Peter Kacsuk,Robert Lovas,Zsolt Nemeth

  • Publisher : Springer Science & Business Media
  • Release : 2008-08-07
  • Pages : 208
  • ISBN : 0387794484
  • Language : En, Es, Fr & De
GET BOOK

DAPSYS (International Conference on Distributed and Parallel Systems) is an international biannual conference series dedicated to all aspects of distributed and parallel computing. DAPSYS 2008, the 7th International Conference on Distributed and Parallel Systems was held in September 2008 in Hungary. Distributed and Parallel Systems: Desktop Grid Computing, based on DAPSYS 2008, presents original research, novel concepts and methods, and outstanding results. Contributors investigate parallel and distributed techniques, algorithms, models and applications; present innovative software tools, environments and middleware; focus on various aspects of grid computing; and introduce novel methods for development, deployment, testing and evaluation. This volume features a special focus on desktop grid computing as well. Designed for a professional audience composed of practitioners and researchers in industry, this book is also suitable for advanced-level students in computer science.

Peterson's Guide to Graduate Programs in Engineering and Applied Sciences 1991

Peterson's Guide to Graduate Programs in Engineering and Applied Sciences 1991
A Book

by Peterson

  • Publisher : Peterson Nelnet Company
  • Release : 1990
  • Pages : 1366
  • ISBN : 9781560790259
  • Language : En, Es, Fr & De
GET BOOK

The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems

The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems
A Book

by Dinesh Peter,Amir Alavi,Steven L. Fernandes,Bahman Javadi

  • Publisher : Academic Press
  • Release : 2020-03
  • Pages : 320
  • ISBN : 9780128163856
  • Language : En, Es, Fr & De
GET BOOK

The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems discusses the recent, rapid development of Internet of things (IoT) and its focus on research in smart cities, especially on surveillance tracking systems in which computing devices are widely distributed and huge amounts of dynamic real-time data are collected and processed. Efficient surveillance tracking systems in the Big Data era require the capability of quickly abstracting useful information from the increasing amounts of data. Real-time information fusion is imperative and part of the challenge to mission critical surveillance tasks for various applications. This book presents all of these concepts, with a goal of creating automated IT systems that are capable of resolving problems without demanding human aid. Examines the current state of surveillance tracking systems, cognitive cloud architecture for resolving critical issues in surveillance tracking systems, and research opportunities in cognitive computing for surveillance tracking systems Discusses topics including cognitive computing architectures and approaches, cognitive computing and neural networks, complex analytics and machine learning, design of a symbiotic agent for recognizing real space in ubiquitous environments, and more Covers supervised regression and classification methods, clustering and dimensionality reduction methods, model development for machine learning applications, intelligent machines and deep learning networks includes coverage of cognitive computing models for scalable environments, privacy and security aspects of surveillance tracking systems, strategies and experiences in cloud architecture and service platform design

Stanford Bulletin

Stanford Bulletin
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2006
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
A Book

by S. Kevin Zhou,Hayit Greenspan,Dinggang Shen

  • Publisher : Academic Press
  • Release : 2017-01-18
  • Pages : 458
  • ISBN : 0128104090
  • Language : En, Es, Fr & De
GET BOOK

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Euro-Par 2019: Parallel Processing

Euro-Par 2019: Parallel Processing
25th International Conference on Parallel and Distributed Computing, Göttingen, Germany, August 26–30, 2019, Proceedings

by Ramin Yahyapour

  • Publisher : Springer
  • Release : 2019-10-10
  • Pages : 524
  • ISBN : 3030294005
  • Language : En, Es, Fr & De
GET BOOK

This book constitutes the proceedings of the 25th International Conference on Parallel and Distributed Computing, Euro-Par 2019, held in Göttingen, Germany, in August 2019. The 36 full papers presented in this volume were carefully reviewed and selected from 142 submissions. They deal with parallel and distributed computing in general, focusing on support tools and environments; performance and power modeling, prediction and evaluation; scheduling and load balancing; high performance architectures and compilers; data management, analytics and deep learning; cluster and cloud computing; distributed systems and algorithms; parallel and distributed programming, interfaces, and languages; multicore and manycore parallelism; theory and algorithms for parallel computation and networking; parallel numerical methods and applications; accelerator computing; algorithms and systems for bioinformatics; and algorithms and systems for digital humanities.