Deep Learning and Parallel Computing Environment for Bioengineering Systems

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

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  • Author : Dr. Arun Kumar Sangaiah
  • Publisher : Academic Press
  • Pages : 280 pages
  • ISBN : 0128172932
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKDeep Learning and Parallel Computing Environment for Bioengineering Systems

Deep Learning and Parallel Computing Environment for Bioengineering Systems

Deep Learning and Parallel Computing Environment for Bioengineering Systems
  • Author : Dr. Arun Kumar Sangaiah
  • Publisher : Academic Press
  • Release : 26 July 2019
GET THIS BOOKDeep Learning and Parallel Computing Environment for Bioengineering Systems

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

Intelligent IoT Systems in Personalized Health Care

Intelligent IoT Systems in Personalized Health Care
  • Author : Arun Kumar Sangaiah,Subhas Chandra Mukhopadhyay
  • Publisher : Academic Press
  • Release : 01 December 2020
GET THIS BOOKIntelligent IoT Systems in Personalized Health Care

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

Artificial Intelligence in the Age of Neural Networks and Brain Computing

Artificial Intelligence in the Age of Neural Networks and Brain Computing
  • Author : Robert Kozma,Cesare Alippi,Yoonsuck Choe,Francesco Carlo Morabito
  • Publisher : Academic Press
  • Release : 30 October 2018
GET THIS BOOKArtificial Intelligence in the Age of Neural Networks and Brain Computing

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

Distributed and Parallel Systems

Distributed and Parallel Systems
  • Author : Péter Kacsuk,Gabriele Kotsis
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKDistributed and Parallel Systems

Distributed and Parallel Systems: From Instruction Parallelism to Cluster Computing is the proceedings of the third Austrian-Hungarian Workshop on Distributed and Parallel Systems organized jointly by the Austrian Computer Society and the MTA SZTAKI Computer and Automation Research Institute. This book contains 18 full papers and 12 short papers from 14 countries around the world, including Japan, Korea and Brazil. The paper sessions cover a broad range of research topics in the area of parallel and distributed systems, including software development environments, performance

Distributed and Parallel Systems

Distributed and Parallel Systems
  • Author : Peter Kacsuk,Robert Lovas,Zsolt Nemeth
  • Publisher : Springer Science & Business Media
  • Release : 07 August 2008
GET THIS BOOKDistributed and Parallel Systems

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

Deep Learning with PyTorch

Deep Learning with PyTorch
  • Author : Eli Stevens,Luca Antiga,Thomas Viehmann
  • Publisher : Manning Publications
  • Release : 04 August 2020
GET THIS BOOKDeep Learning with PyTorch

Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Summary Every other day we hear about new

Distributed and Parallel Computing

Distributed and Parallel Computing
  • Author : Michael Hobbs,International Conference on Algorithms and Architectures for Parallel Processing,Andrzej Goscinski
  • Publisher : Springer Science & Business Media
  • Release : 19 September 2005
GET THIS BOOKDistributed and Parallel Computing

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

Deep Learning

Deep Learning
  • Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
  • Publisher : MIT Press
  • Release : 10 November 2016
GET THIS BOOKDeep Learning

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts.

Techniques and Environments for Big Data Analysis

Techniques and Environments for Big Data Analysis
  • Author : B. S.P. Mishra,Satchidananda Dehuri,Euiwhan Kim,Gi-Name Wang
  • Publisher : Springer
  • Release : 05 February 2016
GET THIS BOOKTechniques and Environments for Big Data Analysis

This volume is aiming at a wide range of readers and researchers in the area of Big Data by presenting the recent advances in the fields of Big Data Analysis, as well as the techniques and tools used to analyze it. The book includes 10 distinct chapters providing a concise introduction to Big Data Analysis and recent Techniques and Environments for Big Data Analysis. It gives insight into how the expensive fitness evaluation of evolutionary learning can play a vital role

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
  • Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
  • Publisher : Academic Press
  • Release : 18 January 2017
GET THIS BOOKDeep Learning for Medical Image Analysis

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

Parallel Computing for Bioinformatics and Computational Biology

Parallel Computing for Bioinformatics and Computational Biology
  • Author : Albert Y. Zomaya
  • Publisher : John Wiley & Sons
  • Release : 14 April 2006
GET THIS BOOKParallel Computing for Bioinformatics and Computational Biology

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

Parallel Computing in Science and Engineering

Parallel Computing in Science and Engineering
  • Author : Dieter Müller-Wichards
  • Publisher : Springer Science & Business Media
  • Release : 11 May 1988
GET THIS BOOKParallel Computing in Science and Engineering

It was the aim of the conference to present issues in parallel computing to a community of potential engineering/scientific users. An overview of the state-of-the-art in several important research areas is given by leading scientists in their field. The classification question is taken up at various points, ranging from parametric characterizations, communication structure, and memory distribution to control and execution schemes. Central issues in multiprocessing hardware and operation, such as scalability, techniques of overcoming memory latency and synchronization overhead,