| Description |
1 online resource |
| Series |
SpringerBriefs in computer science |
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SpringerBriefs in computer science.
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| Contents |
Intro; About This Book; Aims and Scope of the Book; Contents; About the Authors; List of Figures; 1 Introduction; 1.1 Machine Learning; 1.1.1 Supervised Learning; 1.1.2 Unsupervised Learning; 1.2 Deep Learning; 1.3 Conventional Data Processing Techniques; 1.4 Data Mining Techniques: A Big Data Analysis Approach; 1.5 Big Data Analytics; 1.6 Deep Learning in Big Data Analytics; References; 2 Big Data Analytics; 2.1 Overview; 2.2 Characteristics of Big Data; 2.3 Big Data Processing; 2.4 Data Analysis Problems in Big Data; 2.5 Applications of Big Data; 2.5.1 Healthcare; 2.5.2 Manufacturing |
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2.5.3 Government2.5.4 Internet of Things; 2.6 Data Types of Big Data; 2.7 Big Data Tools; 2.8 Opportunities and Challenges; References; 3 Deep Learning Methods and Applications; 3.1 Background; 3.2 Categorization of Deep Learning Networks; 3.3 Deep Networks for Supervised Learning; 3.4 Deep Networks for Unsupervised Learning; 3.5 Hybrid Approach; 3.6 Transfer Learning Techniques; 3.6.1 Homogenous Transfer Learning; 3.6.2 Heterogeneous Transfer Learning; 3.7 Applications of Deep Learning; 3.7.1 Computer Vision; 3.7.2 Information Retrieval; 3.7.3 Natural Language Processing |
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3.7.4 Multitask LearningReferences; 4 Integration of Big Data and Deep Learning; 4.1 Machine Learning in Big Data Analytics; 4.1.1 Machine Learning and Big Data Applications; 4.2 Efficient Deep Learning Algorithms in Big Data Analytics; 4.3 From Machine to Deep Learning: A Comparative Approach; 4.3.1 Performance on Data Size; 4.3.2 Hardware Requirements; 4.3.3 Feature Selection; 4.3.4 Problem-Solving Approach; 4.3.5 Execution Time; 4.4 Applications of Deep and Transfer Learning in Big Data; 4.4.1 Healthcare; 4.4.2 Finance; 4.5 Deep Learning Challenges in Big Data |
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4.5.1 Internet of Things (IoT) Data4.5.2 Enterprise Data; 4.5.3 Medical and Biomedical Data; References; 5 Future of Big Data and Deep Learning for Wireless Body Area Networks; 5.1 Introduction; 5.2 Feed-Forward Network Model; 5.2.1 Deep Learning Frameworks; 5.3 Future of Deep Learning; 5.4 Introduction to Wireless Body Area Networks; 5.5 Applications of Wireless Body Area Networks; 5.5.1 Future Applications of Wireless Body Area Networks; 5.5.2 Use of Biomedical Sensors in Wireless Body Area Networks; 5.6 Existing Challenges in Wireless Body Area Networks; 5.6.1 Routing Protocols |
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5.7 MAC Protocols5.7.1 Superframe Structure of IEEE 802.15.4; 5.7.2 Superframe Structure of IEEE 802.15.6; 5.8 Introduction to Big Data; 5.9 Applications of Big Data in WBAN; 5.9.1 Monitoring of Vital Signs and Analysis; 5.9.2 Early Detection of Abnormal Conditions of Patient; 5.9.3 Daily Basis Activity Monitoring of a Patient Using BMSs; 5.10 Open Issues of WBAN; 5.10.1 Resource-Constraint Architecture of BMS; 5.10.2 Hotspot Paths; 5.10.3 QoS in WBAN; 5.10.4 Path Loss in WBAN; 5.10.5 Data Protection in WBAN; 5.10.6 Step-Down in Energy Consumption |
| Summary |
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 |
| Notes |
5.10.7 Channel Access Allocation and Its Complexity |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from digital title page (viewed on February 14, 2019) |
| Subject |
Machine learning.
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Big data.
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Big data
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Machine learning
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| Form |
Electronic book
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| Author |
Jan, Bilal, author
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Farman, Haleem, author
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| ISBN |
9789811334597 |
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9811334595 |
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9783030317560 |
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3030317560 |
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9789811334603 |
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9811334609 |
|
