عنوان

Deep learning :

(Number (ISBN

3030317560

(Number (ISBN

9783030317560

(Number (ISBN

9789811334597

(Number (ISBN

9789811334603

(Number (ISBN

9811334595

(Number (ISBN

9811334609

Erroneous ISBN

9783030317553

Erroneous ISBN

9789811334580

Erroneous ISBN

9811334587

Title Proper

Deep learning :

General Material Designation

[Book]

Other Title Information

convergence to big data analytics /

First Statement of Responsibility

Murad Khan, Bilal Jan, Haleem Farman.

Place of Publication, Distribution, etc.

Singapore :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

[2019]

Specific Material Designation and Extent of Item

1 online resource

Series Title

SpringerBriefs in computer science

Text of Note

5.10.7 Channel Access Allocation and Its Complexity

Text of Note

Includes bibliographical references and index.

Text of Note

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

Text of Note

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

Text of Note

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

Text of Note

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

Text of Note

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

0

8

8

8

8

Text of Note

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.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9789811334597

Title

Deep Learning: Convergence to Big Data Analytics.

International Standard Book Number

9789811334580

Big data.

Machine learning.

Big data.

Machine learning.

COM021000

UMT

UN

UN

Number

Edition

Class number

Book number

Khan, Murad

Farman, Haleem

Jan, Bilal

Date of Transaction

20200823094312.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y