Deep learning technologies for social impact / (Record no. 82958)
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| fixed length control field | 09578nam a2200769 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 9780750340243 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | IOP |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230516170335.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m eo d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cn |||m|||a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221109s2022 enka fob 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780750340243 |
| Qualifying information | ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780750340236 |
| Qualifying information | mobi |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780750340229 |
| Qualifying information | |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780750340250 |
| Qualifying information | myPrint |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1088/978-0-7503-4024-3 |
| Source of number or code | doi |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (CaBNVSL)thg00083484 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1350649724 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | CaBNVSL |
| Language of cataloging | eng |
| Description conventions | rda |
| Transcribing agency | CaBNVSL |
| Modifying agency | CaBNVSL |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q325.73 |
| Item number | .B464 2022eb |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UYQN |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM044000 |
| Source | bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Benedict, Shajulin, |
| Relator term | author. |
| 9 (RLIN) | 71110 |
| 245 10 - TITLE STATEMENT | |
| Title | Deep learning technologies for social impact / |
| Statement of responsibility, etc. | Shajulin Benedict. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Bristol [England] (No.2 The Distillery, Glassfields, Avon Street, Bristol, BS2 0GR, UK) : |
| Name of producer, publisher, distributor, manufacturer | IOP Publishing, |
| Date of production, publication, distribution, manufacture, or copyright notice | [2022] |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (various pagings) : |
| Other physical details | illustrations (some color). |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | electronic |
| Source | isbdmedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Source | rdacarrier |
| 490 1# - SERIES STATEMENT | |
| Series statement | [IOP release $release] |
| 490 1# - SERIES STATEMENT | |
| Series statement | IOP series in next generation computing |
| 490 1# - SERIES STATEMENT | |
| Series statement | IOP ebooks. [2022 collection] |
| 500 ## - GENERAL NOTE | |
| General note | "Version: 20221001"--Title page verso. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Includes bibliographical references. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 13. Deep learning--future perspectives -- 13.1. Data diversity and generalization -- 13.2. Applications. |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 521 ## - TARGET AUDIENCE NOTE | |
| Target audience note | Graduate or doctoral students, researchers, and practitioners. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Additional physical form available note | Also available in print. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Mode of access: World Wide Web. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader. |
| 545 ## - BIOGRAPHICAL OR HISTORICAL DATA | |
| Biographical or historical data | 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. |
| 588 0# - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Title from PDF title page (viewed on November 9, 2022). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Deep learning (Machine learning) |
| 9 (RLIN) | 70704 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Technology |
| General subdivision | Social aspects. |
| 9 (RLIN) | 5136 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Neural networks & fuzzy systems. |
| Source of heading or term | bicssc |
| 9 (RLIN) | 70685 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Engineering. |
| Source of heading or term | bisacsh |
| 9 (RLIN) | 9405 |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Institute of Physics (Great Britain), |
| Relator term | publisher. |
| 9 (RLIN) | 11622 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| International Standard Book Number | 9780750340229 |
| -- | 9780750340250 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | IOP (Series). |
| Name of part/section of a work | Release 22. |
| 9 (RLIN) | 71111 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | IOP series in next generation computing. |
| 9 (RLIN) | 70513 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | IOP ebooks. |
| Name of part/section of a work | 2022 collection. |
| 9 (RLIN) | 71112 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://iopscience.iop.org/book/mono/978-0-7503-4024-3">https://iopscience.iop.org/book/mono/978-0-7503-4024-3</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
No items available.