Deep Learning and Parallel Computing Environment for Bioengineering Systems

Deep Learning and Parallel Computing Environment for Bioengineering Systems
Author: Dr. Arun Kumar Sangaiah
Publsiher: Academic Press
Total Pages: 280
Release: 2019-07-26
ISBN 10: 0128172932
ISBN 13: 9780128172933
Language: EN, FR, DE, ES & NL

Deep Learning and Parallel Computing Environment for Bioengineering Systems Book Review:

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

Deep Learning and Big Data for Intelligent Transportation

Deep Learning and Big Data for Intelligent Transportation
Author: Khaled R. Ahmed
Publsiher: Springer Nature
Total Pages: 329
Release: 2021
ISBN 10: 3030656616
ISBN 13: 9783030656614
Language: EN, FR, DE, ES & NL

Deep Learning and Big Data for Intelligent Transportation Book Review:

Applications of Big Data in Large and Small Scale Systems

Applications of Big Data in Large  and Small Scale Systems
Author: Sam Goundar,Praveen Kumar Rayani
Publsiher: IGI Global
Total Pages: 377
Release: 2021
ISBN 10: 1799866750
ISBN 13: 9781799866756
Language: EN, FR, DE, ES & NL

Applications of Big Data in Large and Small Scale Systems Book Review:

"This book addresses the newest innovative and intelligent applications related to utilizing the large amounts of big data being generated that is increasingly driving decision making and changing the landscape of business intelligence, from governments to private organizations, from communities to individuals"--

Innovations in Smart Cities Applications Volume 4

Innovations in Smart Cities Applications Volume 4
Author: Mohamed Ben Ahmed
Publsiher: Springer Nature
Total Pages: 329
Release: 2021
ISBN 10: 3030668401
ISBN 13: 9783030668402
Language: EN, FR, DE, ES & NL

Innovations in Smart Cities Applications Volume 4 Book Review:

Smart Sensors for Industrial Internet of Things

Smart Sensors for Industrial Internet of Things
Author: Deepak Gupta
Publsiher: Springer Nature
Total Pages: 329
Release: 2021
ISBN 10: 3030526240
ISBN 13: 9783030526245
Language: EN, FR, DE, ES & NL

Smart Sensors for Industrial Internet of Things Book Review:

Intelligent IoT Systems in Personalized Health Care

Intelligent IoT Systems in Personalized Health Care
Author: Arun Kumar Sangaiah,Subhas Chandra Mukhopadhyay
Publsiher: Academic Press
Total Pages: 360
Release: 2020-12-01
ISBN 10: 0128232048
ISBN 13: 9780128232040
Language: EN, FR, DE, ES & NL

Intelligent IoT Systems in Personalized Health Care Book Review:

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

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
Publsiher: Academic Press
Total Pages: 352
Release: 2018-10-30
ISBN 10: 0128162503
ISBN 13: 9780128162507
Language: EN, FR, DE, ES & NL

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

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

Past Present Parallel

Past  Present  Parallel
Author: Arthur Trew,Greg Wilson
Publsiher: Springer Science & Business Media
Total Pages: 392
Release: 2012-12-06
ISBN 10: 1447118421
ISBN 13: 9781447118428
Language: EN, FR, DE, ES & NL

Past Present Parallel Book Review:

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.

Deep Learning

Deep Learning
Author: Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publsiher: MIT Press
Total Pages: 800
Release: 2016-11-10
ISBN 10: 0262337371
ISBN 13: 9780262337373
Language: EN, FR, DE, ES & NL

Deep Learning Book Review:

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. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Distributed and Parallel Systems

Distributed and Parallel Systems
Author: Peter Kacsuk,Robert Lovas,Zsolt Nemeth
Publsiher: Springer Science & Business Media
Total Pages: 208
Release: 2008-08-07
ISBN 10: 0387794484
ISBN 13: 9780387794488
Language: EN, FR, DE, ES & NL

Distributed and Parallel Systems Book Review:

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.

Deep Learning with PyTorch

Deep Learning with PyTorch
Author: Eli Stevens,Luca Antiga,Thomas Viehmann
Publsiher: Manning Publications
Total Pages: 520
Release: 2020-08-04
ISBN 10: 1617295264
ISBN 13: 9781617295263
Language: EN, FR, DE, ES & NL

Deep Learning with PyTorch Book Review:

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 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. Foreword by Soumith Chintala, Cocreator of PyTorch. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Although many deep learning tools use Python, the PyTorch library is truly Pythonic. Instantly familiar to anyone who knows PyData tools like NumPy and scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s excellent for building quick models, and it scales smoothly from laptop to enterprise. Because companies like Apple, Facebook, and JPMorgan Chase rely on PyTorch, it’s a great skill to have as you expand your career options. It’s easy to get started with PyTorch. It minimizes cognitive overhead without sacrificing the access to advanced features, meaning you can focus on what matters the most - building and training the latest and greatest deep learning models and contribute to making a dent in the world. PyTorch is also a snap to scale and extend, and it partners well with other Python tooling. PyTorch has been adopted by hundreds of deep learning practitioners and several first-class players like FAIR, OpenAI, FastAI and Purdue. About the book Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning. What's inside Training deep neural networks Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Exploring code samples in Jupyter Notebooks About the reader For Python programmers with an interest in machine learning. About the author Eli Stevens had roles from software engineer to CTO, and is currently working on machine learning in the self-driving-car industry. Luca Antiga is cofounder of an AI engineering company and an AI tech startup, as well as a former PyTorch contributor. Thomas Viehmann is a PyTorch core developer and machine learning trainer and consultant. consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
Author: S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publsiher: Academic Press
Total Pages: 458
Release: 2017-01-18
ISBN 10: 0128104090
ISBN 13: 9780128104095
Language: EN, FR, DE, ES & NL

Deep Learning for Medical Image Analysis Book Review:

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

Parallel Computing for Bioinformatics and Computational Biology

Parallel Computing for Bioinformatics and Computational Biology
Author: Albert Y. Zomaya
Publsiher: John Wiley & Sons
Total Pages: 1000
Release: 2006-04-14
ISBN 10: 0471756490
ISBN 13: 9780471756491
Language: EN, FR, DE, ES & NL

Parallel Computing for Bioinformatics and Computational Biology Book Review:

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.

Distributed and Parallel Computing

Distributed and Parallel Computing
Author: Michael Hobbs,International Conference on Algorithms and Architectures for Parallel Processing,Andrzej Goscinski
Publsiher: Springer Science & Business Media
Total Pages: 448
Release: 2005-09-19
ISBN 10: 9783540292357
ISBN 13: 3540292357
Language: EN, FR, DE, ES & NL

Distributed and Parallel Computing Book Review:

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 on Distributed Memory Multiprocessors

Parallel Computing on Distributed Memory Multiprocessors
Author: Füsun Özgüner,Fikret Ercal
Publsiher: Springer Science & Business Media
Total Pages: 332
Release: 1993-02-10
ISBN 10: 9783540562955
ISBN 13: 3540562958
Language: EN, FR, DE, ES & NL

Parallel Computing on Distributed Memory Multiprocessors Book Review:

Proceedings of the NATO Advanced Study Institute on Parallel Computing on Distributed Memory Multiprocessors, held at Bilkent University, Ankara, Turkey, July 1-13, 1991

Stanford Bulletin

Stanford Bulletin
Author: Anonim
Publsiher: Unknown
Total Pages: 329
Release: 2006
ISBN 10:
ISBN 13: STANFORD:36105119778335
Language: EN, FR, DE, ES & NL

Stanford Bulletin Book Review:

Advanced Parallel Processing Technologies

Advanced Parallel Processing Technologies
Author: Yong Dou,Ralf Gruber,Josef Joller
Publsiher: Springer Science & Business Media
Total Pages: 478
Release: 2009-08-06
ISBN 10: 3642036430
ISBN 13: 9783642036439
Language: EN, FR, DE, ES & NL

Advanced Parallel Processing Technologies Book Review:

This book constitutes the refereed proceedings of the 8th International Workshop on Advanced Parallel Processing Technologies, APPT 2009, held in Rapperswil, Switzerland, in August 2009. The 36 revised full papers presented were carefully reviewed and selected from 76 submissions. All current aspects in parallel and distributed computing are addressed ranging from hardware and software issues to algorithmic aspects and advanced applications. The papers are organized in topical sections on architecture, graphical processing unit, grid, grid scheduling, mobile application, parallel application, parallel libraries and performance.

Distributed and Parallel Systems

Distributed and Parallel Systems
Author: Péter Kacsuk,Gabriele Kotsis
Publsiher: Springer Science & Business Media
Total Pages: 233
Release: 2012-12-06
ISBN 10: 1461544890
ISBN 13: 9781461544890
Language: EN, FR, DE, ES & NL

Distributed and Parallel Systems Book Review:

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 evaluation, architectures, languages, algorithms, web and cluster computing. This volume will be useful to researchers and scholars interested in all areas related to parallel and distributed computing systems.

Patterns and Skeletons for Parallel and Distributed Computing

Patterns and Skeletons for Parallel and Distributed Computing
Author: Fethi A. Rabhi,Sergei Gorlatch
Publsiher: Springer Science & Business Media
Total Pages: 334
Release: 2011-06-28
ISBN 10: 1447100972
ISBN 13: 9781447100973
Language: EN, FR, DE, ES & NL

Patterns and Skeletons for Parallel and Distributed Computing Book Review:

Patterns and Skeletons for Parallel and Distributed Computing is a unique survey of research work in high-level parallel and distributed computing over the past ten years. Comprising contributions from the leading researchers in Europe and the US, it looks at interaction patterns and their role in parallel and distributed processing, and demonstrates for the first time the link between skeletons and design patterns. It focuses on computation and communication structures that are beyond simple message-passing or remote procedure calling, and also on pragmatic approaches that lead to practical design and programming methodologies with their associated compilers and tools. The book is divided into two parts which cover: skeletons-related material such as expressing and composing skeletons, formal transformation, cost modelling and languages, compilers and run-time systems for skeleton-based programming.- design patterns and other related concepts, applied to other areas such as real-time, embedded and distributed systems. It will be an essential reference for researchers undertaking new projects in this area, and will also provide useful background reading for advanced undergraduate and postgraduate courses on parallel or distributed system design.

Massively Parallel Evolutionary Computation on GPGPUs

Massively Parallel Evolutionary Computation on GPGPUs
Author: Shigeyoshi Tsutsui,Pierre Collet
Publsiher: Springer Science & Business Media
Total Pages: 453
Release: 2013-12-05
ISBN 10: 3642379591
ISBN 13: 9783642379598
Language: EN, FR, DE, ES & NL

Massively Parallel Evolutionary Computation on GPGPUs Book Review:

Evolutionary algorithms (EAs) are metaheuristics that learn from natural collective behavior and are applied to solve optimization problems in domains such as scheduling, engineering, bioinformatics, and finance. Such applications demand acceptable solutions with high-speed execution using finite computational resources. Therefore, there have been many attempts to develop platforms for running parallel EAs using multicore machines, massively parallel cluster machines, or grid computing environments. Recent advances in general-purpose computing on graphics processing units (GPGPU) have opened up this possibility for parallel EAs, and this is the first book dedicated to this exciting development. The three chapters of Part I are tutorials, representing a comprehensive introduction to the approach, explaining the characteristics of the hardware used, and presenting a representative project to develop a platform for automatic parallelization of evolutionary computing (EC) on GPGPUs. The 10 chapters in Part II focus on how to consider key EC approaches in the light of this advanced computational technique, in particular addressing generic local search, tabu search, genetic algorithms, differential evolution, swarm optimization, ant colony optimization, systolic genetic search, genetic programming, and multiobjective optimization. The 6 chapters in Part III present successful results from real-world problems in data mining, bioinformatics, drug discovery, crystallography, artificial chemistries, and sudoku. Although the parallelism of EAs is suited to the single-instruction multiple-data (SIMD)-based GPU, there are many issues to be resolved in design and implementation, and a key feature of the contributions is the practical engineering advice offered. This book will be of value to researchers, practitioners, and graduate students in the areas of evolutionary computation and scientific computing.