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024 7 _a10.1088/978-0-7503-4024-3
_2doi
035 _a(CaBNVSL)thg00083484
035 _a(OCoLC)1350649724
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
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050 4 _aQ325.73
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082 0 4 _a006.31
_223
100 1 _aBenedict, Shajulin,
_eauthor.
_971110
245 1 0 _aDeep learning technologies for social impact /
_cShajulin Benedict.
264 1 _aBristol [England] (No.2 The Distillery, Glassfields, Avon Street, Bristol, BS2 0GR, UK) :
_bIOP Publishing,
_c[2022]
300 _a1 online resource (various pagings) :
_billustrations (some color).
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _a[IOP release $release]
490 1 _aIOP series in next generation computing
490 1 _aIOP ebooks. [2022 collection]
500 _a"Version: 20221001"--Title page verso.
504 _aIncludes bibliographical references.
505 0 _apart 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
505 8 _a2. 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
505 8 _a3. 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
505 8 _apart 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
505 8 _a5. 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
505 8 _a6. 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
505 8 _a7. 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
505 8 _a8. 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
505 8 _a9. 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
505 8 _a10. 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
505 8 _apart 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
505 8 _a12. 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
505 8 _a13. Deep learning--future perspectives -- 13.1. Data diversity and generalization -- 13.2. Applications.
520 3 _aArtificial 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.
521 _aGraduate or doctoral students, researchers, and practitioners.
530 _aAlso available in print.
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
545 _aShajulin 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.
588 0 _aTitle from PDF title page (viewed on November 9, 2022).
650 0 _aDeep learning (Machine learning)
_970704
650 0 _aTechnology
_xSocial aspects.
_95136
650 7 _aNeural networks & fuzzy systems.
_2bicssc
_970685
650 7 _aEngineering.
_2bisacsh
_99405
710 2 _aInstitute of Physics (Great Britain),
_epublisher.
_911622
776 0 8 _iPrint version:
_z9780750340229
_z9780750340250
830 0 _aIOP (Series).
_pRelease 22.
_971111
830 0 _aIOP series in next generation computing.
_970513
830 0 _aIOP ebooks.
_p2022 collection.
_971112
856 4 0 _uhttps://iopscience.iop.org/book/mono/978-0-7503-4024-3
942 _cEBK
999 _c82958
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