Artificial Intelligence And Machine Learning For Edge Computing

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Artificial Intelligence and Machine Learning for EDGE Computing

Author : Rajiv Pandey
Publisher : Academic Press
ISBN 13 : 0128240555
Total Pages : 516 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Artificial Intelligence and Machine Learning for EDGE Computing by : Rajiv Pandey

Download or read book Artificial Intelligence and Machine Learning for EDGE Computing written by Rajiv Pandey and published by Academic Press. This book was released on 2022-04-26 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints

Edge AI

Author : Xiaofei Wang
Publisher : Springer Nature
ISBN 13 : 9811561869
Total Pages : 156 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Edge AI by : Xiaofei Wang

Download or read book Edge AI written by Xiaofei Wang and published by Springer Nature. This book was released on 2020-08-31 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.

Machine Learning for Edge Computing

Author : Amitoj Singh
Publisher : CRC Press
ISBN 13 : 1000609243
Total Pages : 235 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Machine Learning for Edge Computing by : Amitoj Singh

Download or read book Machine Learning for Edge Computing written by Amitoj Singh and published by CRC Press. This book was released on 2022-07-29 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book divides edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). It focuses on providing optimal solutions to the key concerns in edge computing through effective AI technologies, and it discusses how to build AI models, i.e., model training and inference, on edge. This book provides insights into this new inter-disciplinary field of edge computing from a broader vision and perspective. The authors discuss machine learning algorithms for edge computing as well as the future needs and potential of the technology. The authors also explain the core concepts, frameworks, patterns, and research roadmap, which offer the necessary background for potential future research programs in edge intelligence. The target audience of this book includes academics, research scholars, industrial experts, scientists, and postgraduate students who are working in the field of Internet of Things (IoT) or edge computing and would like to add machine learning to enhance the capabilities of their work. This book explores the following topics: Edge computing, hardware for edge computing AI, and edge virtualization techniques Edge intelligence and deep learning applications, training, and optimization Machine learning algorithms used for edge computing Reviews AI on IoT Discusses future edge computing needs Amitoj Singh is an Associate Professor at the School of Sciences of Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Punjab, India. Vinay Kukreja is a Professor at the Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India. Taghi Javdani Gandomani is an Assistant Professor at Shahrekord University, Shahrekord, Iran.

Artificial Intelligence for Cloud and Edge Computing

Author : Sanjay Misra
Publisher : Springer
ISBN 13 : 9783030808204
Total Pages : 350 pages
Book Rating : 4.8/5 (82 download)

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Book Synopsis Artificial Intelligence for Cloud and Edge Computing by : Sanjay Misra

Download or read book Artificial Intelligence for Cloud and Edge Computing written by Sanjay Misra and published by Springer. This book was released on 2022-02-19 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning and cloud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.

Artificial Intelligence for Cloud and Edge Computing

Author : Sanjay Misra
Publisher : Springer Nature
ISBN 13 : 3030808211
Total Pages : 358 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Artificial Intelligence for Cloud and Edge Computing by : Sanjay Misra

Download or read book Artificial Intelligence for Cloud and Edge Computing written by Sanjay Misra and published by Springer Nature. This book was released on 2022-01-13 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning and cloud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.

Edge Intelligence in the Making

Author : Sen Lin
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1681739917
Total Pages : 235 pages
Book Rating : 4.6/5 (817 download)

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Book Synopsis Edge Intelligence in the Making by : Sen Lin

Download or read book Edge Intelligence in the Making written by Sen Lin and published by Morgan & Claypool Publishers. This book was released on 2020-10-21 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of edge computing and AI has resulted in innovative solutions, namely edge intelligence or edge AI. Nevertheless, research and practice on this emerging inter-disciplinary field is still in its infancy stage. To facilitate the dissemination of the recent advances in edge intelligence in both academia and industry, this book conducts a comprehensive and detailed survey of the recent research efforts and also showcases the authors' own research progress on edge intelligence. Specifically, the book first reviews the background and present motivation for AI running at the network edge. Next, it provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. Finally, it discusses the applications, marketplace, and future research opportunities of edge intelligence. This emerging interdisciplinary field offers many open problems and yet also tremendous opportunities, and this book only touches the tip of iceberg. Hopefully, this book will elicit escalating attention, stimulate fruitful discussions, and open new directions on edge intelligence.

Deep Learning on Edge Computing Devices

Author : Xichuan Zhou
Publisher : Elsevier
ISBN 13 : 0323909272
Total Pages : 200 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Deep Learning on Edge Computing Devices by : Xichuan Zhou

Download or read book Deep Learning on Edge Computing Devices written by Xichuan Zhou and published by Elsevier. This book was released on 2022-02-02 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization. This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design. Focuses on hardware architecture and embedded deep learning, including neural networks Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud Describes how to maximize the performance of deep learning on Edge-computing devices Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring

Explainable Edge AI: A Futuristic Computing Perspective

Author : Aboul Ella Hassanien
Publisher : Springer Nature
ISBN 13 : 3031182928
Total Pages : 187 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Explainable Edge AI: A Futuristic Computing Perspective by : Aboul Ella Hassanien

Download or read book Explainable Edge AI: A Futuristic Computing Perspective written by Aboul Ella Hassanien and published by Springer Nature. This book was released on 2022-11-10 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents explainability in edge AI, an amalgamation of edge computing and AI. The issues of transparency, fairness, accountability, explainability, interpretability, data-fusion, and comprehensibility that are significant for edge AI are being addressed in this book through explainable models and techniques. The concept of explainable edge AI is new in front of the academic and research community, and consequently, it will undoubtedly explore multiple research dimensions. The book presents the concept of explainability in edge AI which is the amalgamation of edge computing and AI. In the futuristic computing scenario, the goal of explainable edge AI will be to execute the AI tasks and produce explainable results at the edge. First, this book explains the fundamental concepts of explainable artificial intelligence (XAI), then it describes the concept of explainable edge AI, and finally, it elaborates on the technicalities of explainability in edge AI. Owing to the quick transition in the current computing scenario and integration with the latest AI-based technologies, it is significant to facilitate people-centric computing through explainable edge AI. Explainable edge AI will facilitate enhanced prediction accuracy with the comprehensible decision and traceability of actions performed at the edge and have a significant impact on futuristic computing scenarios. This book is highly relevant to graduate/postgraduate students, academicians, researchers, engineers, professionals, and other personnel working in artificial intelligence, machine learning, and intelligent systems.

Applied Edge AI

Author : Pethuru Raj
Publisher : CRC Press
ISBN 13 : 1000552691
Total Pages : 334 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Applied Edge AI by : Pethuru Raj

Download or read book Applied Edge AI written by Pethuru Raj and published by CRC Press. This book was released on 2022-04-06 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: The strategically sound combination of edge computing and artificial intelligence (AI) results in a series of distinct innovations and disruptions enabling worldwide enterprises to visualize and realize next-generation software products, solutions and services. Businesses, individuals, and innovators are all set to embrace and experience the sophisticated capabilities of Edge AI. With the faster maturity and stability of Edge AI technologies and tools, the world is destined to have a dazzling array of edge-native, people-centric, event-driven, real-time, service-oriented, process-aware, and insights-filled services. Further on, business workloads and IT services will become competent and cognitive with state-of-the-art Edge AI infrastructure modules, AI algorithms and models, enabling frameworks, integrated platforms, accelerators, high-performance processors, etc. The Edge AI paradigm will help enterprises evolve into real-time and intelligent digital organizations. Applied Edge AI: Concepts, Platforms, and Industry Use Cases focuses on the technologies, processes, systems, and applications that are driving this evolution. It examines the implementation technologies; the products, processes, platforms, patterns, and practices; and use cases. AI-enabled chips are exclusively used in edge devices to accelerate intelligent processing at the edge. This book examines AI toolkits and platforms for facilitating edge intelligence. It also covers chips, algorithms, and tools to implement Edge AI, as well as use cases. FEATURES The opportunities and benefits of intelligent edge computing Edge architecture and infrastructure AI-enhanced analytics in an edge environment Encryption for securing information An Edge AI system programmed with Tiny Machine learning algorithms for decision making An improved edge paradigm for addressing the big data movement in IoT implementations by integrating AI and caching to the edge Ambient intelligence in healthcare services and in development of consumer electronic systems Smart manufacturing of unmanned aerial vehicles (UAVs) AI, edge computing, and blockchain in systems for environmental protection Case studies presenting the potential of leveraging AI in 5G wireless communication

Edge Intelligence

Author : Javid Taheri
Publisher : Springer
ISBN 13 : 9783031221545
Total Pages : 0 pages
Book Rating : 4.2/5 (215 download)

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Book Synopsis Edge Intelligence by : Javid Taheri

Download or read book Edge Intelligence written by Javid Taheri and published by Springer. This book was released on 2023-05-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This graduate-level textbook is ideally suited for lecturing the most relevant topics of Edge Computing and its ties to Artificial Intelligence (AI) and Machine Learning (ML) approaches. It starts from basics and gradually advances, step-by-step, to ways AI/ML concepts can help or benefit from Edge Computing platforms. The book is structured into seven chapters; each comes with its own dedicated set of teaching materials (practical skills, demonstration videos, questions, lab assignments, etc.). Chapter 1 opens the book and comprehensively introduces the concept of distributed computing continuum systems that led to the creation of Edge Computing. Chapter 2 motivates the use of container technologies and how they are used to implement programmable edge computing platforms. Chapter 3 introduces ways to employ AI/ML approaches to optimize service lifecycles at the edge. Chapter 4 goes deeper in the use of AI/ML and introduces ways to optimize spreading computational tasks along edge computing platforms. Chapter 5 introduces AI/ML pipelines to efficiently process generated data on the edge. Chapter 6 introduces ways to implement AI/ML systems on the edge and ways to deal with their training and inferencing procedures considering the limited resources available at the edge-nodes. Chapter 7 motivates the creation of a new orchestrator independent object model to descriptive objects (nodes, applications, etc.) and requirements (SLAs) for underlying edge platforms. To provide hands-on experience to students and step-by-step improve their technical capabilities, seven sets of Tutorials-and-Labs (TaLs) are also designed. Codes and Instructions for each TaL is provided on the book website, and accompanied by videos to facilitate their learning process.

Artificial Intelligence for Edge Computing

Author : Mudhakar Srivatsa
Publisher : Springer Nature
ISBN 13 : 3031407873
Total Pages : 373 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Artificial Intelligence for Edge Computing by : Mudhakar Srivatsa

Download or read book Artificial Intelligence for Edge Computing written by Mudhakar Srivatsa and published by Springer Nature. This book was released on 2024-01-10 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is undeniable that the recent revival of artificial intelligence (AI) has significantly changed the landscape of science in many application domains, ranging from health to defense and from conversational interfaces to autonomous cars. With terms such as “Google Home”, “Alexa”, and “ChatGPT” becoming household names, the pervasive societal impact of AI is clear. Advances in AI promise a revolution in our interaction with the physical world, a domain where computational intelligence has always been envisioned as a transformative force toward a better tomorrow. Depending on the application family, this domain is often referred to as Ubiquitous Computing, Cyber-Physical Computing, or the Internet of Things. The underlying vision is driven by the proliferation of cheap embedded computing hardware that can be integrated easily into myriads of everyday devices from consumer electronics, such as personal wearables and smart household appliances, to city infrastructure and industrial process control systems. One common trait across these applications is that the data that the application operates on come directly (typically via sensors) from the physical world. Thus, from the perspective of communication network infrastructure, the data originate at the network edge. From a performance standpoint, there is an argument to be made that such data should be processed at the point of collection. Hence, a need arises for Edge AI -- a genre of AI where the inference, and sometimes even the training, are performed at the point of need, meaning at the edge where the data originate. The book is broken down into three parts: core problems, distributed problems, and other cross-cutting issues. It explores the challenges arising in Edge AI contexts. Some of these challenges (such as neural network model reduction to fit resource-constrained hardware) are unique to the edge environment. They need a novel category of solutions that do not parallel more typical concerns in mainstream AI. Others are adaptations of mainstream AI challenges to the edge space. An example is overcoming the cost of data labeling. The labeling problem is pervasive, but its solution in the IoT application context is different from other contexts. This book is not a survey of the state of the art. With thousands of publications appearing in AI every year, such a survey is doomed to be incomplete on arrival. It is also not a comprehensive coverage of all the problems in the space of Edge AI. Different applications pose different challenges, and a more comprehensive coverage should be more application specific. Instead, this book covers some of the more endemic challenges across the range of IoT/CPS applications. To offer coverage in some depth, we opt to cover mainly one or a few representative solutions for each of these endemic challenges in sufficient detail, rather that broadly touching on all relevant prior work. The underlying philosophy is one of illustrating by example. The solutions are curated to offer insight into a way of thinking that characterizes Edge AI research and distinguishes its solutions from their more mainstream counterparts.

Edge Intelligence in the Making

Author : Sen Lin
Publisher :
ISBN 13 : 9781681739922
Total Pages : 234 pages
Book Rating : 4.7/5 (399 download)

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Book Synopsis Edge Intelligence in the Making by : Sen Lin

Download or read book Edge Intelligence in the Making written by Sen Lin and published by . This book was released on 2020-10-21 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of edge computing and AI has resulted in innovative solutions, namely edge intelligence or edge AI. Nevertheless, research and practice on this emerging inter-disciplinary field is still in its infancy stage. To facilitate the dissemination of the recent advances in edge intelligence in both academia and industry, this book conducts a comprehensive and detailed survey of the recent research efforts and also showcases the authors' own research progress on edge intelligence. Specifically, the book first reviews the background and present motivation for AI running at the network edge. Next, it provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. Finally, it discusses the applications, marketplace, and future research opportunities of edge intelligence. This emerging interdisciplinary field offers many open problems and yet also tremendous opportunities, and this book only touches the tip of iceberg. Hopefully, this book will elicit escalating attention, stimulate fruitful discussions, and open new directions on edge intelligence.

Mobile Edge Artificial Intelligence

Author : Yuanming Shi
Publisher : Academic Press
ISBN 13 : 0128238356
Total Pages : 208 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Mobile Edge Artificial Intelligence by : Yuanming Shi

Download or read book Mobile Edge Artificial Intelligence written by Yuanming Shi and published by Academic Press. This book was released on 2021-08-07 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains. As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources. Presents advanced key enabling techniques, including model compression, wireless MapReduce and wireless cooperative transmission Provides advanced 6G wireless techniques, including over-the-air computation and reconfigurable intelligent surface Includes principles for designing communication-efficient edge inference systems, communication-efficient training systems, and communication-efficient optimization algorithms for edge machine learning

Deep Learning and Edge Computing Solutions for High Performance Computing

Author : A. Suresh
Publisher : Springer Nature
ISBN 13 : 3030602656
Total Pages : 286 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Deep Learning and Edge Computing Solutions for High Performance Computing by : A. Suresh

Download or read book Deep Learning and Edge Computing Solutions for High Performance Computing written by A. Suresh and published by Springer Nature. This book was released on 2021-01-27 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The book is a comprehensive reference that provides an overview of the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Topics include deep learning methods for applications in object detection and identification, object tracking, human action recognition, and cross-modal and multimodal data analysis. High performance computing systems for applications in healthcare are also discussed. The contributors also include information on microarray data analysis, sequence analysis, genomics based analytics, disease network analysis, and techniques for big data Analytics and health information technology.

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Author :
Publisher : Academic Press
ISBN 13 : 0128231246
Total Pages : 416 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Hardware Accelerator Systems for Artificial Intelligence and Machine Learning by :

Download or read book Hardware Accelerator Systems for Artificial Intelligence and Machine Learning written by and published by Academic Press. This book was released on 2021-03-28 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance

Advanced Analytics and Deep Learning Models

Author : Archana Mire
Publisher : John Wiley & Sons
ISBN 13 : 1119791758
Total Pages : 436 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Advanced Analytics and Deep Learning Models by : Archana Mire

Download or read book Advanced Analytics and Deep Learning Models written by Archana Mire and published by John Wiley & Sons. This book was released on 2022-06-01 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.

Deep Neural Networks for Multimodal Imaging and Biomedical Applications

Author : Annamalai Suresh
Publisher : Medical Information Science Reference
ISBN 13 : 9781799835936
Total Pages : 294 pages
Book Rating : 4.8/5 (359 download)

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Book Synopsis Deep Neural Networks for Multimodal Imaging and Biomedical Applications by : Annamalai Suresh

Download or read book Deep Neural Networks for Multimodal Imaging and Biomedical Applications written by Annamalai Suresh and published by Medical Information Science Reference. This book was released on 2020 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.