Deep learning : convergence to big data analytics /: convergence to big data analytics. (2019)

• Record Type: Book
• Further Information: Note: Murad Khan, Bilal Jan, Haleem Farman.
• Authors: Khan, Murad; Jan, Bilal; Farman, Haleem
• 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 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 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 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 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
• Publisher Details: Cham : Springer
• Publication Date: 2019
• Copyright Date: 2020
• Extent: 1 online resource (342 pages)
• Subjects: 006.3/1; Machine learning; Big data; Big data; Machine learning; Electronic books
• Languages: English
• ISBNs: 9783030317560
• Related ISBNs: 9783030317553
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.