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Deep learning technologies for social impact
Author
Title
Deep learning technologies for social impact / Shajulin Benedict.
ISBN
9780750340243
9780750340236
9780750340229
9780750340250
Publication
Bristol [England] (No.2 The Distillery, Glassfields, Avon Street, Bristol, BS2 0GR, UK) : IOP Publishing, [2022]
Physical Description
1 online resource : illustrations (some color).
Local Notes
Access is available to the Yale community.
Notes
"Version: 20221001"--Title page verso.
Access and use
Access restricted by licensing agreement.
Biographical / Historical Note
Shajulin Benedict graduated in 2001 from Manonmaniam Sunderanar University, India, with Distinction. In 2004, he received an ME degree in Digital Communication and Computer Networking from A.K.C.E, Anna University, Chennai. He did his PhD in the area of grid scheduling at Anna University, Chennai. After his PhD, he joined a research team in Germany to pursue post-doctorate research under the guidance of Professor Gerndt. He served as a professor at SXCCE Research Centre of Anna University-Chennai. Later, he visited TUM Germany to teach cloud computing as a Guest Professor of TUM-Germany. Currently, he works at the Indian Institute of Information Technology Kottayam, Kerala, India, an institute of national importance in India, and as a Guest Professor of TUM-Germany. Additionally, he serves as Director/PI/Representative Officer of AIC-IIITKottayam for nourishing young entrepreneurs in India. His research interests include deep learning, HPC/cloud/grid scheduling, performance analysis of parallel applications (including exascale), IoT cloud, and so forth.
Summary
Artificial intelligence is gaining traction in areas of social responsibility. From climate change to social polarization to epidemics, humankind has been seeking new solutions to these ever-present problems. Deep learning (DL) techniques have increased in power in recent years, with algorithms already exhibiting tremendous possibilities in domains such as scientific research, agriculture, smart cities, finance, healthcare, conservation, the environment, industry and more. Innovative ideas using appropriate DL frameworks are now actively employed for the development of and delivering a positive impact on smart cities and societies. This book highlights the importance of specific frameworks such as IoT-enabled frameworks or serverless cloud frameworks that are applying DL techniques for solving persistent societal problems. It addresses the challenges of DL implementation, computation time, and the complexity of reasoning and modelling different types of data. In particular, the book explores and emphasises techniques involved in DL such as image classification, image enhancement, word analysis, human-machine emotional interfaces and the applications of these techniques for smart cities and societal problems. To extend the theoretical description, the book is enhanced through case studies, including those implemented using tensorflow2 and relevant IoT-specific sensor/actuator frameworks. The broad coverage will be essential reading not just to advanced students and academic researchers but also to practitioners and engineers looking to deliver an improved society and global health. Part of IOP Series in Next Generation Computing.
Variant and related titles
IOP ebooks.
Other formats
Also available in print.
Print version:
Format
Books / Online
Language
English
Added to Catalog
December 15, 2022
Series
[IOP release $release]
IOP series in next generation computing
IOP ebooks. [2022 collection]
Bibliography
Includes bibliographical references.
Audience
Graduate or doctoral students, researchers, and practitioners.
Contents
part I. Introduction. 1. Deep learning for social good
an introduction
1.1. Deep learning
a subset of AI
1.2. History of deep learning
1.3. Trends
deep learning for social good
1.4. Motivations
1.5. Deep learning for social good
a need
1.6. Intended audience
1.7. Chapters and descriptions
1.8. Reading flow
2. Applications for social good
2.1. Characteristics of social-good applications
2.2. Generic architecture
entities
2.3. Applications for social good
2.4. Technologies and techniques
2.5. Technology
blockchain
2.6. AI/machine learning/deep learning techniques
2.7. The Internet of things/sensor technology
2.8. Robotic technology
2.9. Computing infrastructures
a needy technology
2.10. Security-related techniques
3. Computing architectures
base technologies
3.1. History of computing
3.2. Types of computing
3.3. Hardware support for deep learning
3.4. Microcontrollers, microprocessors, and FPGAs
3.5. Cloud computing
an environment for deep learning
3.6. Virtualization
a base for cloud computing
3.7. Hypervisors
impact on deep learning
3.8. Containers and Dockers
3.9. Cloud execution models
3.10. Programming deep learning tasks
libraries
3.11. Sensor-enabled data collection for DLs
3.12. Edge-level deep learning systems
part II. Deep learning techniques. 4. CNN techniques
4.1. CNNs
introduction
4.2. CNNs
nuts and bolts
4.3. Social-good applications
a CNN perspective
4.4. CNN use case
climate change problem
4.5. CNN challenges
5. Object detection techniques and algorithms
5.1. Computer vision
taxonomy
5.2. Object detection
objectives
5.3. Object detection
challenges
5.4. Object detection
major steps or processes
5.5. Object detection methods
5.6. Applications
5.7. Exam proctoring
YOLOv5
5.8. Proctoring system
implementation stages
6. Sentiment analysis
algorithms and frameworks
6.1. Sentiment analysis
an introduction
6.2. Levels and approaches
6.3. Sentiment analysis
processes
6.4. Recommendation system
sentiment analysis
6.5. Movie recommendation
a case study
6.6. Metrics
6.7. Tools and frameworks
6.8. Sentiment analysis
sarcasm detection
7. Autoencoders and variational autoencoders
7.1. Introduction
autoencoders
7.2. Autoencoder architectures
7.3. Types of autoencoder
7.4. Applications of autoencoders
7.5. Variational autoencoders
7.6. Autoencoder implementation
code snippet explanation
8. GANs and disentangled mechanisms
8.1. Introduction to GANs
8.2. Concept
generative and descriptive
8.3. Major steps involved
8.4. GAN architecture
8.5. Types of GAN
8.6. StyleGAN
8.7. A simple implementation of a GAN
8.8. Quality of GANs
8.9. Applications and challenges
9. Deep reinforcement learning architectures
9.1. Deep reinforcement learning
an introduction
9.2. The difference between deep reinforcement learning and machine learning
9.3. The difference between deep learning and reinforcement learning
9.4. Reinforcement learning applications
9.5. Components of RL frameworks
9.6. Reinforcement learning techniques
9.7. Reinforcement learning algorithms
9.8. Integration into real-world systems
10. Facial recognition and applications
10.1. Facial recognition
a historical view
10.2. Biometrics using faces
10.3. Facial detection versus recognition
10.4. Facial recognition
processes
10.5. Applications
10.6. Emotional intelligence
a facial recognition application
10.7. Emotion detection
database creation
10.8. Challenges and future work
part III. Security, performance, and future directions. 11. Data security and platforms
11.1. Security breaches
11.2. Security attacks
11.3. Deep-learning-related security attacks
11.4. Metrics
11.5. Execution environments
11.6. Using deep learning to enhance security
12. Performance monitoring and analysis
12.1. Performance monitoring
12.2. The need for performance monitoring
12.3. Performance analysis methods/approaches
12.4. Performance metrics
12.5. Evaluation platforms
13. Deep learning
future perspectives
13.1. Data diversity and generalization
13.2. Applications.
Also listed under
Institute of Physics (Great Britain), publisher.
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