Table Of ContentDeep Learning Technologies
for Social Impact
Online at: https://doi.org/10.1088/978-0-7503-4024-3
IOP Series in Next Generation Computing
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Prateek Agrawal
University of Klagenfurt, Austria and Lovely Professional University, India
Anand Sharma
Mody University of Science and Technology, India
Vishu Madaan
Lovely Professional University, India
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Deep Learning Technologies
for Social Impact
Shajulin Benedict
Indian Institute of Information Technology Kottayam,
Valavoor P.O. Kottayam District, Kerala, India
IOP Publishing, Bristol, UK
ªIOPPublishingLtd2022
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ISBN 978-0-7503-4024-3(ebook)
ISBN 978-0-7503-4022-9(print)
ISBN 978-0-7503-4025-0(myPrint)
ISBN 978-0-7503-4023-6(mobi)
DOI 10.1088/978-0-7503-4024-3
Version:20221001
IOPebooks
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fromtheBritishLibrary.
PublishedbyIOPPublishing,whollyownedbyTheInstituteofPhysics,London
IOPPublishing,TempleCircus,TempleWay,Bristol,BS16HG,UK
USOffice:IOPPublishing,Inc.,190NorthIndependenceMallWest,Suite601,Philadelphia,
PA19106,USA
To my father C Benedict, mother J Mary Alphonsal, wife Rejitha R S,
son Shaun Benedict, and daughter Sharlina R S.
Contents
Preface xviii
Acknowledgement xix
Author biography xx
Part I Introduction
1 Deep learning for social good—an introduction 1-1
1.1 Deep learning—a subset of AI 1-1
1.2 History of deep learning 1-3
1.3 Trends—deep learning for social good 1-4
1.3.1 Increasing data; increasing machines 1-4
1.3.2 Increasing publications 1-5
1.3.3 International trends 1-6
1.4 Motivations 1-6
1.5 Deep learning for social good—a need 1-7
1.6 Intended audience 1-7
1.6.1 Students 1-7
1.6.2 Researchers 1-8
1.6.3 Practitioners/startup entrepreneurs 1-8
1.6.4 Government/smart city officials 1-8
1.7 Chapters and descriptions 1-8
1.7.1 About deep learning 1-8
1.7.2 Social-good applications 1-9
1.7.3 Computing architectures—base technologies 1-9
1.7.4 Convolutional neural network techniques 1-9
1.7.5 Object detection techniques and algorithms 1-10
1.7.6 Sentiment analysis—algorithms and frameworks 1-10
1.7.7 Autoencoders and variational autoencoders 1-10
1.7.8 Generative adversarial networks and disentangled mechanisms 1-11
1.7.9 Deep reinforcement learning architectures 1-11
1.7.10 Facial recognition and applications 1-11
1.7.11 Data security and platforms 1-11
1.7.12 Performance monitoring and analysis 1-12
1.7.13 Deep learning—future perspectives 1-12
1.8 Reading flow 1-12
References 1-13
vii
DeepLearningTechnologiesforSocialImpact
2 Applications for social good 2-1
2.1 Characteristics of social-good applications 2-1
2.2 Generic architecture—entities 2-2
2.2.1 User interface 2-2
2.2.2 Sensor connectivity 2-2
2.2.3 Hierarchical intelligence 2-3
2.2.4 Data security—immutability 2-3
2.2.5 Notification/visualization 2-3
2.3 Applications for social good 2-3
2.3.1 Economic forecasting 2-3
2.3.2 Personal assistants 2-4
2.3.3 Language assistance 2-6
2.3.4 Speech, text, and object recognition 2-6
2.3.5 Smart transportation 2-7
2.3.6 Wildlife conservation 2-7
2.3.7 Healthcare applications 2-7
2.4 Technologies and techniques 2-8
2.5 Technology—blockchain 2-9
2.5.1 Types of transaction 2-9
2.6 AI/machine learning/deep learning techniques 2-10
2.7 The Internet of things/sensor technology 2-11
2.7.1 The industrial IoT 2-11
2.7.2 The consumer IoT 2-12
2.7.3 The social IoT 2-12
2.7.4 The semantic IoT 2-13
2.7.5 The productization IoT 2-13
2.8 Robotic technology 2-13
2.9 Computing infrastructures—a needy technology 2-13
2.10 Security-related techniques 2-14
References 2-14
3 Computing architectures—base technologies 3-1
3.1 History of computing 3-1
3.2 Types of computing 3-2
3.3 Hardware support for deep learning 3-4
3.3.1 CPUs versus GPUs 3-4
3.3.2 CPUs versus TPUs 3-5
viii
DeepLearningTechnologiesforSocialImpact
3.4 Microcontrollers, microprocessors, and FPGAs 3-6
3.5 Cloud computing—an environment for deep learning 3-7
3.6 Virtualization—a base for cloud computing 3-8
3.6.1 Virtualization—an analogous example 3-8
3.6.2 Objectives of virtualization 3-9
3.6.3 VMs—comparison to physical machines 3-9
3.6.4 VMs—case studies 3-10
3.7 Hypervisors—impact on deep learning 3-10
3.7.1 Bare-metal hypervisor 3-10
3.7.2 Hosted hypervisors 3-11
3.7.3 Full virtualization 3-11
3.7.4 Paravirtualization 3-13
3.7.5 Hardware-assisted virtualization 3-13
3.8 Containers and Dockers 3-14
3.8.1 Docker instances 3-14
3.8.2 Docker building blocks 3-16
3.8.3 Docker storage characteristics 3-19
3.8.4 Docker working model 3-20
3.8.5 Docker tools 3-21
3.9 Cloud execution models 3-21
3.9.1 Serverless cloud execution model 3-22
3.9.2 Kubernetes solutions 3-22
3.9.3 DL-as-a-service 3-23
3.10 Programming deep learning tasks—libraries 3-23
3.10.1 Features of TensorFlow 3-23
3.10.2 TensorFlow components 3-24
3.11 Sensor-enabled data collection for DLs 3-25
3.11.1 Required mechanisms 3-25
3.11.2 Sensors to DL services—data connectivity 3-25
3.11.3 Application-layer protocols 3-26
3.11.4 Lower-layer protocols 3-34
3.12 Edge-level deep learning systems 3-39
3.12.1 About the ESP32 3-39
3.12.2 Programming ESP boards 3-40
References 3-41
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