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Deep Learning for Internet of Things Infrastructure by Mamoun Alazab (2021, Hardcover)

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Number of Pages: 266. Weight: 0 lbs. Publication Date: 2021-09-08. Publisher: CRC Press.

About this product

Product Identifiers

PublisherTaylor & Francis Group

ISBN-100367457334

ISBN-139780367457334

eBay Product ID (ePID)12058353939

Product Key Features

Number of Pages248 Pages

LanguageEnglish

Publication NameDeep Learning for Internet of Things Infrastructure

Publication Year2021

SubjectEngineering (General), Machine Theory, Networking / General, Information Technology

TypeTextbook

AuthorMamoun Alazab

Subject AreaComputers, Technology & Engineering

FormatHardcover

Dimensions

Item Weight35.5 Oz

Item Length9.2 in

Item Width6.1 in

Additional Product Features

Dewey Edition23

IllustratedYes

Dewey Decimal004.678

Table Of Content1. Data Caching at Fog Nodes under IoT Networks: Review of Machine Learning Approaches 2. ECC-Based Privacy-Preserving Mechanisms Using Deep Learning for Industrial IoT: A State-of-the-Art Approaches 3. Contemporary Developments and Technologies in Deep Learning-Based IoT 4. Deep Learning-Assisted Vehicle Counting for Intersection and Traffic Management in Smart Cities 5. Toward Rapid Development and Deployment of Machine Learning Pipelines across Cloud-Edge 6. Category Identification Technique by a Semantic Feature Generation Algorithm 7. Role of Deep Learning Algorithms in Securing Internet of Things Applications 8. Deep Learning and IoT in Ophthalmology 9. Deep Learning in IoT-Based Healthcare Applications 10. Authentication and Access Control for IoT Devices and Its Applications 11. Deep Neural Network-Based Security Model for IoT Device Network

SynopsisThis book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning (DL)-based data analytics of IoT (Internet of Things) infrastructures., This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning (DL)-based data analytics of IoT (Internet of Things) infrastructures. Deep Learning for Internet of Things Infrastructure addresses emerging trends and issues on IoT systems and services across various application domains. The book investigates the challenges posed by the implementation of deep learning on IoT networking models and services. It provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT. The book also explores new functions and technologies to provide adaptive services and intelligent applications for different end users. FEATURES Promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of DL-based data analytics of IoT infrastructures Addresses emerging trends and issues on IoT systems and services across various application domains Investigates the challenges posed by the implementation of deep learning on IoT networking models and services Provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT Explores new functions and technologies to provide adaptive services and intelligent applications for different end users Uttam Ghosh is an Assistant Professor in the Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA. Mamoun Alazab is an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia. Ali Kashif Bashir is a Senior Lecturer/Associate Professor and Program Leader of BSc (H) Computer Forensics and Security at the Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom. Al-Sakib Khan Pathan is an Adjunct Professor of Computer Science and Engineering at the Independent University, Bangladesh.

LC Classification NumberTK5105.8857