Deep learning technologies for social impact /

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 alre...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Benedict, Shajulin (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bristol [England] (No.2 The Distillery, Glassfields, Avon Street, Bristol, BS2 0GR, UK) : IOP Publishing, [2022]
Colección:IOP (Series). Release 22.
IOP series in next generation computing.
IOP ebooks. 2022 collection.
Temas:
Deep learning (Machine learning)
Technology > Social aspects.
Neural networks & fuzzy systems.
Engineering.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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