Kooperativer Bibliotheksverbund

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Format: 1 online resource (various pagings) : , illustrations (some color).

ISBN: 9780750340243 , 9780750340236

Series Statement: [IOP release $release]

Content: 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.

Note: "Version: 20221001"--Title page verso. , 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 available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750340229

Additional Edition: ISBN 9780750340250

Language: English

DOI: 10.1088/978-0-7503-4024-3

URL: https://iopscience.iop.org/book/mono/978-0-7503-4024-3