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Concepts And Real Time Applications Of Deep Learning

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Concepts And Real Time Applications Of Deep Learning

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Author : Smriti Srivastava
language : en
Publisher: Springer Nature
Release Date : 2021-09-23

Concepts And Real Time Applications Of Deep Learning written by Smriti Srivastava and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-23 with Technology & Engineering categories.

This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more. The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields. Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures; Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies; Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.

Deep Learning And Neural Networks Concepts Methodologies Tools And Applications

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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2019-10-11

Deep Learning And Neural Networks Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-11 with Computers categories.

Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.

Machine Learning And Deep Learning In Real Time Applications

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Author : Mahrishi, Mehul
language : en
Publisher: IGI Global
Release Date : 2020-04-24

Machine Learning And Deep Learning In Real Time Applications written by Mahrishi, Mehul and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-24 with Computers categories.

Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

Real Time Applications Of Machine Learning In Cyber Physical Systems

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Author : Easwaran, Balamurugan
language : en
Publisher: IGI Global
Release Date : 2022-03-11

Real Time Applications Of Machine Learning In Cyber Physical Systems written by Easwaran, Balamurugan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-11 with Computers categories.

Technological advancements of recent decades have reshaped the way people socialize, work, learn, and ultimately live. The use of cyber-physical systems (CPS) specifically have helped people lead their lives with greater control and freedom. CPS domains have great societal significance, providing crucial assistance in industries ranging from security to healthcare. At the same time, machine learning (ML) algorithms are known for being substantially efficient, high performing, and have become a real standard due to greater accessibility, and now more than ever, multidisciplinary applications of ML for CPS have become a necessity to help uncover constructive solutions for real-world problems. Real-Time Applications of Machine Learning in Cyber-Physical Systems provides a relevant theoretical framework and the most recent empirical findings on various real-time applications of machine learning in cyber-physical systems. Covering topics like intrusion detection systems, predictive maintenance, and seizure prediction, this book is an essential resource for researchers, machine learning professionals, independent researchers, scholars, scientists, libraries, and academicians.

Deep Learning And Natural Language Processing Based Real Time Applications

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Author : Mandakini Chagamreddy
language : en
Publisher: Archers & Elevators Publishing House
Release Date :

Deep Learning And Natural Language Processing Based Real Time Applications written by Mandakini Chagamreddy and has been published by Archers & Elevators Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on with Antiques & Collectibles categories.

Concepts And Real Time Applications Of Deep Learning

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Author : Smriti Srivastava
language : en
Publisher:
Release Date : 2021

Concepts And Real Time Applications Of Deep Learning written by Smriti Srivastava and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

A Practical Approach For Machine Learning And Deep Learning Algorithms

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Author : Abhishek Kumar Pandey
language : en
Publisher: BPB Publications
Release Date : 2019-09-18

A Practical Approach For Machine Learning And Deep Learning Algorithms written by Abhishek Kumar Pandey and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-18 with Computers categories.

Guide covering topics from machine learning, regression models, neural network to tensor flow DESCRIPTION Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite processing. In this book, we have made an honest effort to make the concepts of machine learning easy and give basic programs in MATLAB right from the installation part. Although the real-time application of machine learning is endless, however, the basic concepts and algorithms are discussed using MATLAB language so that not only graduation students but also researchers are benefitted from it. KEY FEATURES Machine learning in MATLAB using basic concepts and algorithms. Deriving and accessing of data in MATLAB and next, pre-processing and preparation of data. Machine learning workflow for health monitoring. The neural network domain and implementation in MATLAB with explicit explanation of code and results. How predictive model can be improved using MATLAB? MATLAB code for an algorithm implementation, rather than for mathematical formula. Machine learning workflow for health monitoring. WHAT WILL YOU LEARN Pre-requisites to machine learning Finding natural patterns in data Building classification methods Data pre-processing in Python Building regression models Creating neural networks Deep learning WHO THIS BOOK IS FOR The book is basically meant for graduate and research students who find the algorithms of machine learning difficult to implement. We have touched all basic algorithms of machine learning in detail with a practical approach. Primarily, beginners will find this book more effective as the chapters are subdivided in a manner that they find the building and implementation of algorithms in MATLAB interesting and easy at the same time. Table of Contents _1. Ê Ê Pre-requisite to Machine Learning 2. Ê Ê An introduction to Machine Learning 3. Ê Ê Finding Natural Patterns in Data 4. Ê Ê Building Classification Methods 5. Ê Ê Data Pre-Processing in Python 6. Ê Ê Building Regression Models 7. Ê Ê Creating Neural Networks 8. Ê Ê Introduction to Deep Learning

Practical Applications Of Electrocardiogram

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Author : Umashankar Lakshmanadoss
language : en
Publisher: BoD – Books on Demand
Release Date : 2020-02-26

Practical Applications Of Electrocardiogram written by Umashankar Lakshmanadoss and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-26 with Medical categories.

This book provides an excellent overview of the diagnosis of abnormal electrocardiograms (ECGs) through deep learning methods. These methods include optimal techniques that can link the processing and analysis of nonstationary ECG signals, the various statistical methods of converting ECG data into variant maps, and the application of various ways of identifying premature atrial beats, ECG characteristics of right and left ventricular tachyarrhythmia, and conditions producing left ventricular hypertrophy, including hypertrophic cardiomyopathy. This book is divided into two sections, including basic and practical applications of ECGs. We hope that it will serve as a reference for the techniques used to obtain and process electrical signals for ECGs. This book will also function as an excellent reference for atrial and ventricular tachyarrhythmia.

Application Of Fpga To Real Time Machine Learning

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Author : Piotr Antonik
language : en
Publisher: Springer
Release Date : 2018-05-18

Application Of Fpga To Real Time Machine Learning written by Piotr Antonik and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-18 with Science categories.

This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

Edge Ai Merging Iot And Machine Learning For Real Time Analytics

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Author : Dr. D. Srinivasa Rao
language : en
Publisher: Xoffencer International Book Publication house
Release Date : 2024-10-10

Edge Ai Merging Iot And Machine Learning For Real Time Analytics written by Dr. D. Srinivasa Rao and has been published by Xoffencer International Book Publication house this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-10 with Computers categories.

In order to provide real-time analytics directly at the edge of the network, edge artificial intelligence (AI) is a disruptive technique that combines the capabilities of Internet of Things (IoT) devices with the power of machine learning (ML). As a result of this paradigm shift away from conventional cloud-centric approaches, latency is reduced, privacy is improved, and operational efficiency is increased. Information is processed locally on devices. The Internet of Things (IoT) is experiencing exponential expansion, which presents a problem for centralized cloud processing due to the sheer amount of data created by sensors, cameras, and linked equipment of all kinds. By putting artificial intelligence closer to the source of the data, Edge AI makes it possible to make decisions more quickly and reduces the need for continual data transmission to the cloud, which in turn reduces the expenses associated with bandwidth and cloud storage. Innovation is fostered across a variety of sectors, including healthcare, smart cities, autonomous cars, and industrial automation, via the integration of the Internet of Things (IoT) and machine learning at the edge. Real-time analytics makes it possible to identify trends and irregularities, which in turn leads to improvements in accessibility and efficiency in areas such as tailored services, increased security, and predictive maintenance. Utilizing on-device machine learning models enables quick insights, which is essential in applications that are time-sensitive. This is also true as Internet of Things devices grow more sophisticated. Furthermore, the infrastructure for edge computing is capable of supporting dispersed systems, which not only ensures increased system resilience but also reduces the likelihood of downtime. Nevertheless, putting Edge AI into practice is not without its difficulties. The management of the computational needs of machine learning models on devices with limited resources, the maintenance of scalability, and the guarantee of security across dispersed nodes are all key concerns that need to be addressed. The development of lightweight machine learning models, hardware that has been optimized, and security mechanisms that have been improved are all essential components in promoting the widespread use of this technology. Furthermore, the continuing developments in 5G networks and edge computing frameworks promise to push the frontiers of edge artificial intelligence, which will offer up new opportunities for real-time, decentralized intelligence. In conclusion, Edge AI is able to bridge the gap between the increasing needs of Internet of Things ecosystems and the requirement for real-time insights that can be put into action. With the ability to facilitate decision-making processes that are quicker, more intelligent, and more secure, it has the potential to completely transform whole sectors. Artificial intelligence at the edge of the network will play a crucial part in determining the future of intelligent systems as technology continues to advance

Concepts And Real Time Applications Of Deep Learning

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