Deep Learning Applications With Practical Measured Results In Electronics Industries

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Deep Learning Applications with Practical Measured Results in Electronics Industries

Author : Mong-Fong Horng
Publisher : MDPI
ISBN 13 : 3039288636
Total Pages : 272 pages
Book Rating : 4.0/5 (392 download)

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Book Synopsis Deep Learning Applications with Practical Measured Results in Electronics Industries by : Mong-Fong Horng

Download or read book Deep Learning Applications with Practical Measured Results in Electronics Industries written by Mong-Fong Horng and published by MDPI. This book was released on 2020-05-22 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.

Deep Learning Applications with Practical Measured Results in Electronics Industries

Author : Mong-Fong Horng
Publisher :
ISBN 13 : 9783039288649
Total Pages : 272 pages
Book Rating : 4.2/5 (886 download)

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Book Synopsis Deep Learning Applications with Practical Measured Results in Electronics Industries by : Mong-Fong Horng

Download or read book Deep Learning Applications with Practical Measured Results in Electronics Industries written by Mong-Fong Horng and published by . This book was released on 2020 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.

Advances in Deep Learning Applications for Smart Cities

Author : Kumar, Rajeev
Publisher : IGI Global
ISBN 13 : 1799897125
Total Pages : 335 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Advances in Deep Learning Applications for Smart Cities by : Kumar, Rajeev

Download or read book Advances in Deep Learning Applications for Smart Cities written by Kumar, Rajeev and published by IGI Global. This book was released on 2022-05-13 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the past decade, technology has grown exponentially, and governments have promoted smart cities. Emerging smart cities have become both crucibles and showrooms for the practical application of the internet of things (IoT), cloud computing, and the integration of big data into everyday life. This complex concoction requires new thinking of the synergistic utilization of deep learning and blockchain methods and data-driven decision making with automation infrastructure, autonomous transportation, and more. Advances in Deep Learning Applications for Smart Cities provides a global perspective on current and future trends concerning the integration of deep learning and blockchain for smart cities. It provides valuable insights on the best practices and success factors for smart cities. Covering topics such as digital healthcare, object detection methods, and power consumption, this book is an excellent reference for researchers, scientists, libraries, industry experts, government organizations, students and educators of higher education, business professionals, communication and marketing agencies, entrepreneurs, and academicians.

Sensor Data Analysis and Management

Author : A. Suresh
Publisher : John Wiley & Sons
ISBN 13 : 1119682487
Total Pages : 228 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Sensor Data Analysis and Management by : A. Suresh

Download or read book Sensor Data Analysis and Management written by A. Suresh and published by John Wiley & Sons. This book was released on 2021-11-11 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data An exploration of the benefits of neural networks in real-time environmental sensor data analysis Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition An analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.

Deep Learning Applications

Author : Pier Luigi Mazzeo
Publisher : BoD – Books on Demand
ISBN 13 : 1839623748
Total Pages : 216 pages
Book Rating : 4.8/5 (396 download)

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Book Synopsis Deep Learning Applications by : Pier Luigi Mazzeo

Download or read book Deep Learning Applications written by Pier Luigi Mazzeo and published by BoD – Books on Demand. This book was released on 2021-07-14 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is a branch of machine learning similar to artificial intelligence. The applications of deep learning vary from medical imaging to industrial quality checking, sports, and precision agriculture. This book is divided into two sections. The first section covers deep learning architectures and the second section describes the state of the art of applications based on deep learning.

Convergence of Deep Learning and Artificial Intelligence in Internet of Things

Author : Ajay Rana
Publisher : CRC Press
ISBN 13 : 1000822087
Total Pages : 329 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Convergence of Deep Learning and Artificial Intelligence in Internet of Things by : Ajay Rana

Download or read book Convergence of Deep Learning and Artificial Intelligence in Internet of Things written by Ajay Rana and published by CRC Press. This book was released on 2022-12-20 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers advances and applications of smart technologies including the Internet of Things (IoT), artificial intelligence, and deep learning in areas such as manufacturing, production, renewable energy, and healthcare. It also covers wearable and implantable biomedical devices for healthcare monitoring, smart surveillance, and monitoring applications such as the use of an autonomous drone for disaster management and rescue operations. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology. • Covers concepts, theories, and applications of artificial intelligence and deep learning, from the perspective of the Internet of Things. • Discusses powers predictive analysis, predictive maintenance, and automated processes for making manufacturing plants more efficient, profitable, and safe. • Explores the importance of blockchain technology in the Internet of Things security issues. • Discusses key deep learning concepts including trust management, identity management, security threats, access control, and privacy. • Showcases the importance of intelligent algorithms for cloud-based Internet of Things applications. This text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the Internet of Things. It further discusses applications of smart technologies in diverse sectors such as agriculture, smart home, production, manufacturing, transport, and healthcare.

Machine Learning and Deep Learning in Real-Time Applications

Author : Mahrishi, Mehul
Publisher : IGI Global
ISBN 13 : 1799830977
Total Pages : 344 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Machine Learning and Deep Learning in Real-Time Applications by : Mahrishi, Mehul

Download or read book Machine Learning and Deep Learning in Real-Time Applications written by Mahrishi, Mehul and published by IGI Global. This book was released on 2020-04-24 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Big Data Analysis and Deep Learning Applications

Author : Thi Thi Zin
Publisher : Springer
ISBN 13 : 9811308691
Total Pages : 386 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Big Data Analysis and Deep Learning Applications by : Thi Thi Zin

Download or read book Big Data Analysis and Deep Learning Applications written by Thi Thi Zin and published by Springer. This book was released on 2018-06-06 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities.

Deep Neural Network Applications

Author : Hasmik Osipyan
Publisher : CRC Press
ISBN 13 : 0429556209
Total Pages : 158 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Deep Neural Network Applications by : Hasmik Osipyan

Download or read book Deep Neural Network Applications written by Hasmik Osipyan and published by CRC Press. This book was released on 2022-04-28 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world is on the verge of fully ushering in the fourth industrial revolution, of which artificial intelligence (AI) is the most important new general-purpose technology. Like the steam engine that led to the widespread commercial use of driving machineries in the industries during the first industrial revolution; the internal combustion engine that gave rise to cars, trucks, and airplanes; electricity that caused the second industrial revolution through the discovery of direct and alternating current; and the Internet, which led to the emergence of the information age, AI is a transformational technology. It will cause a paradigm shift in the way’s problems are solved in every aspect of our lives, and, from it, innovative technologies will emerge. AI is the theory and development of machines that can imitate human intelligence in tasks such as visual perception, speech recognition, decision-making, and human language translation. This book provides a complete overview on the deep learning applications and deep neural network architectures. It also gives an overview on most advanced future-looking fundamental research in deep learning application in artificial intelligence. Research overview includes reasoning approaches, problem solving, knowledge representation, planning, learning, natural language processing, perception, motion and manipulation, social intelligence and creativity. It will allow the reader to gain a deep and broad knowledge of the latest engineering technologies of AI and Deep Learning and is an excellent resource for academic research and industry applications.

Deep Learning Applications in Image Analysis

Author : Sanjiban Sekhar Roy
Publisher : Springer Nature
ISBN 13 : 9819937841
Total Pages : 218 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Deep Learning Applications in Image Analysis by : Sanjiban Sekhar Roy

Download or read book Deep Learning Applications in Image Analysis written by Sanjiban Sekhar Roy and published by Springer Nature. This book was released on 2023-07-08 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3. The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.

Examining the Impact of Deep Learning and IoT on Multi-Industry Applications

Author : Raut, Roshani
Publisher : IGI Global
ISBN 13 : 1799875172
Total Pages : 304 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Examining the Impact of Deep Learning and IoT on Multi-Industry Applications by : Raut, Roshani

Download or read book Examining the Impact of Deep Learning and IoT on Multi-Industry Applications written by Raut, Roshani and published by IGI Global. This book was released on 2021-01-29 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning, as a recent AI technique, has proven itself efficient in solving many real-world problems. Deep learning algorithms are efficient, high performing, and an effective standard for solving these problems. In addition, with IoT, deep learning is in many emerging and developing domains of computer technology. Deep learning algorithms have brought a revolution in computer vision applications by introducing an efficient solution to several image processing-related problems that have long remained unresolved or moderately solved. Various significant IoT technologies in various industries, such as education, health, transportation, and security, combine IoT with deep learning for complex problem solving and the supported interaction between human beings and their surroundings. Examining the Impact of Deep Learning and IoT on Multi-Industry Applications provides insights on how deep learning, together with IoT, impacts various sectors such as healthcare, agriculture, cyber security, and social media analysis applications. The chapters present solutions to various real-world problems using these methods from various researchers’ points of view. While highlighting topics such as medical diagnosis, power consumption, livestock management, security, and social media analysis, this book is ideal for IT specialists, technologists, security analysts, medical practitioners, imaging specialists, diagnosticians, academicians, researchers, industrial experts, scientists, and undergraduate and postgraduate students who are working in the field of computer engineering, electronics, and electrical engineering.

Trends in Deep Learning Methodologies

Author : Vincenzo Piuri
Publisher : Academic Press
ISBN 13 : 0128232684
Total Pages : 308 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Trends in Deep Learning Methodologies by : Vincenzo Piuri

Download or read book Trends in Deep Learning Methodologies written by Vincenzo Piuri and published by Academic Press. This book was released on 2020-11-12 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. Provides insights into the theory, algorithms, implementation and the application of deep learning techniques Covers a wide range of applications of deep learning across smart healthcare and smart engineering Investigates the development of new models and how they can be exploited to find appropriate solutions

Advanced Deep Learning for Engineers and Scientists

Author : Kolla Bhanu Prakash
Publisher : Springer Nature
ISBN 13 : 3030665194
Total Pages : 294 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Advanced Deep Learning for Engineers and Scientists by : Kolla Bhanu Prakash

Download or read book Advanced Deep Learning for Engineers and Scientists written by Kolla Bhanu Prakash and published by Springer Nature. This book was released on 2021-07-24 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book. Presents practical basics to advanced concepts in deep learning and how to apply them through various projects; Discusses topics such as deep learning in smart grids and renewable energy & sustainable development; Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.

Deep Learning Systems

Author : Andres Rodriguez
Publisher : Springer Nature
ISBN 13 : 3031017692
Total Pages : 245 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Deep Learning Systems by : Andres Rodriguez

Download or read book Deep Learning Systems written by Andres Rodriguez and published by Springer Nature. This book was released on 2022-05-31 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.

Deep Learning Networks

Author : Jayakumar Singaram
Publisher : Springer Nature
ISBN 13 : 3031392442
Total Pages : 173 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Deep Learning Networks by : Jayakumar Singaram

Download or read book Deep Learning Networks written by Jayakumar Singaram and published by Springer Nature. This book was released on 2023-12-03 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners. It introduces a deep learning tool set with deep learning concepts interwoven to enhance understanding. It also presents the design and technical aspects of programming along with a practical way to understand the relationships between programming and technology for a variety of applications. It offers a tutorial for the reader to learn wide-ranging conceptual modeling and programming tools that animate deep learning applications. The book is especially directed to students taking senior level undergraduate courses and to industry practitioners interested in learning about and applying deep learning methods to practical real-world problems.

Deep Learning for Power System Applications

Author : Fangxing Li
Publisher : Springer Nature
ISBN 13 : 3031453573
Total Pages : 111 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Deep Learning for Power System Applications by : Fangxing Li

Download or read book Deep Learning for Power System Applications written by Fangxing Li and published by Springer Nature. This book was released on 2023-12-12 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control. Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers. Provides a history of AI in power grid operation and planning; Introduces deep learning algorithms and applications in power systems; Includes several representative case studies.

Concepts and Real-Time Applications of Deep Learning

Author : Smriti Srivastava
Publisher : Springer Nature
ISBN 13 : 3030761673
Total Pages : 212 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Concepts and Real-Time Applications of Deep Learning by : Smriti Srivastava

Download or read book Concepts and Real-Time Applications of Deep Learning written by Smriti Srivastava and published by Springer Nature. This book was released on 2021-09-23 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.