-PDF Download- The International Conference On Deep Learning Big Data And Blockchain Deep Bdb 2021 EBOOK

The International Conference on Deep Learning, Big Data and Blockchain (Deep-BDB 2021)

Author: Irfan Awan

Publisher: Springer Nature

Published: 2021-08-07

Total Pages: 182

ISBN-13: 3030843378

The role of deep learning for the analysis and learning of massive amounts of data from all aspects of daily-life has dramatically changed over the last few years. It is increasingly helping uncover trends leading to great successes. This book includes a collection of research manuscripts presenting state-of-the-art work in the areas of deep learning, blockchain and big data. All the manuscripts included in this book have been peer-reviewed based on aspects of novelty, originality and rigour. The main topics covered in the book include machine learning and time series, blockchain technologies and applications, data security, deep learning, and Internet of Things.

The International Conference on Deep Learning, Big Data and Blockchain (DBB 2022)

Author: Irfan Awan

Publisher: Springer Nature

Published: 2022-08-31

Total Pages: 140

ISBN-13: 3031160355

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Deep and machine learning is the state-of-the-art at providing models, methods, tools and techniques for developing autonomous and intelligent systems which can revolutionise industrial and commercial applications in various fields such as online commerce, intelligent transportation, healthcare and medicine, etc. The ground-breaking technology of blockchain also enables decentralisation, immutability, and transparency of data and applications. This event aims to enable synergy between these areas and provide a leading forum for researchers, developers, practitioners, and professionals from public sectors and industries to meet and share the latest solutions and ideas in solving cutting-edge problems in the modern information society and the economy. The conference focuses on specific challenges in deep (and machine) learning, big data and blockchain. Some of the key topics of interest include (but are not limited to): Deep/Machine learning based models Statistical models and learning Data analysis, insights and hidden pattern Data visualisation Security threat detection Data classification and clustering Blockchain security and trust Blockchain data management

The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023)

Author: Muhammad Younas

Publisher: Springer Nature

Published: 2023-10-01

Total Pages: 148

ISBN-13: 3031423178

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This book constitutes refereed articles which present research work on new and emerging topics such as distributed ledger technology, blockchains and architectures, smart cities, machine learning and deep learning techniques and application areas such as flight pricing, energy demand and healthcare. The intended readership of the book include researchers, developers and practitioners in the areas of deep learning, big data and blockchains technologies and their applications.

The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy

Author: John Macintyre

Publisher: Springer Nature

Published: 2021-10-27

Total Pages: 1169

ISBN-13: 3030895084

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This book presents the proceedings of the 2020 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2021), online conference, on 30 October 2021. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.

Proceedings of the International Conference on Big Data, IoT, and Machine Learning

Author: Mohammad Shamsul Arefin

Publisher: Springer Nature

Published: 2021-12-03

Total Pages: 784

ISBN-13: 9811666369

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This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2021), held in Cox’s Bazar, Bangladesh, during 23–25 September 2021. The book covers research papers in the field of big data, IoT and machine learning. The book will be helpful for active researchers and practitioners in the field.

Big Data Analytics

Author: Satish Narayana Srirama

Publisher: Springer Nature

Published: 2022-01-01

Total Pages: 360

ISBN-13: 3030936201

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This book constitutes the proceedings of the 8th International Conference on Big Data Analytics, BDA 2021, which took place during December 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 full and 3 short papers included in this volume were carefully reviewed and selected from 41 submissions. The contributions were organized in topical sections named as follows: medical and health applications; machine/deep learning; IoTs, sensors, and networks; fundamentation; pattern mining and data analytics.

Big Data Analysis and Deep Learning Applications

Author: Thi Thi Zin

Publisher: Springer

Published: 2018-06-06

Total Pages: 386

ISBN-13: 9811308691

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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.

Big Data, Machine Learning, and Applications

Author: Malaya Dutta Borah

Publisher: Springer Nature

Published: 2024-01-06

Total Pages: 758

ISBN-13: 9819934818

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This book constitutes refereed proceedings of the Second International Conference on Big Data, Machine Learning, and Applications, BigDML 2021. The volume focuses on topics such as computing methodology; machine learning; artificial intelligence; information systems; security and privacy. This volume will benefit research scholars, academicians, and industrial people who work on data storage and machine learning.

Deep Learning: Convergence to Big Data Analytics

Author: Murad Khan

Publisher: Springer

Published: 2018-12-30

Total Pages: 79

ISBN-13: 9811334595

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This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.

Responsible AI

Author: CSIRO

Publisher: Addison-Wesley Professional

Published: 2023-12-08

Total Pages: 424

ISBN-13: 0138073880

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THE FIRST PRACTICAL GUIDE FOR OPERATIONALIZING RESPONSIBLE AI ̃FROM MUL TI°LEVEL GOVERNANCE MECHANISMS TO CONCRETE DESIGN PATTERNS AND SOFTWARE ENGINEERING TECHNIQUES. AI is solving real-world challenges and transforming industries. Yet, there are serious concerns about its ability to behave and make decisions in a responsible way. Operationalizing responsible AI is about providing concrete guidelines to a wide range of decisionmakers and technologists on how to govern, design, and build responsible AI systems. These include governance mechanisms at the industry, organizational, and team level; software engineering best practices; architecture styles and design patterns; system-level techniques connecting code with data and models; and trade-offs in design decisions. Responsible AI includes a set of practices that technologists (for example, technology-conversant decision-makers, software developers, and AI practitioners) can undertake to ensure the AI systems they develop or adopt are trustworthy throughout the entire lifecycle and can be trusted by those who use them. The book offers guidelines and best practices not just for the AI part of a system, but also for the much larger software infrastructure that typically wraps around the AI. First book of its kind to cover the topic of operationalizing responsible AI from the perspective of the entire software development life cycle. Concrete and actionable guidelines throughout the lifecycle of AI systems, including governance mechanisms, process best practices, design patterns, and system engineering techniques. Authors are leading experts in the areas of responsible technology, AI engineering, and software engineering. Reduce the risks of AI adoption, accelerate AI adoption in responsible ways, and translate ethical principles into products, consultancy, and policy impact to support the AI industry. Online repository of patterns, techniques, examples, and playbooks kept up-to-date by the authors. Real world case studies to demonstrate responsible AI in practice. Chart the course to responsible AI excellence, from governance to design, with actionable insights and engineering prowess found in this defi nitive guide.