Handbook of research on machine learning enabled IoT for smart applications across industries:
The handbook of research on machine learning-enabled IoT for smart applications across industries highlights the importance of ML for IoT's success and diverse ML-powered IoT applications. This book addresses the problems and challenges in energy, industry, and healthcare and solutions proposed...
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| Other Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA)
IGI Global
2023.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | The handbook of research on machine learning-enabled IoT for smart applications across industries highlights the importance of ML for IoT's success and diverse ML-powered IoT applications. This book addresses the problems and challenges in energy, industry, and healthcare and solutions proposed for ML-enabled IoT and new algorithms in ML. It further addresses their accuracy for existing real-time applications. |
| Physical Description: | 30 PDFs (542 Seiten) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781668487877 |
| Access: | Restricted to subscribers or individual electronic text purchasers. |
Staff View
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| 246 | 3 | |a Handbook of research on machine learning enabled Internet of things for smart applications across industries | |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) |b IGI Global |c 2023. | |
| 300 | |a 30 PDFs (542 Seiten) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 isbdmedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Chapter 1. Challenges in various applications using IoT -- Chapter 2. Pattern recognition by IoT systems of machine learning -- Chapter 3. Institutional pressures on the oil and gas industry: the role of machine learning -- Chapter 4. Generative adversarial networks: a game changer GAN for machine learning and IoT applications -- Chapter 5. Machine learning-enabled Internet of things solution for smart agriculture operations -- Chapter 6. An investigative study on Internet of things in healthcare -- Chapter 7. Machine learning-based threat identification systems: machine learning-based IDS using decision tree -- Chapter 8. Future outlier detection algorithm for smarter industry application using ML and AI: explainable AI and ML for smart industry evolution using ML/AI algorithms and implementations -- Chapter 9. Edge computing: optimizing performance and enhancing user experience -- Chapter 10. The role of wireless body area networks in smart healthcare system in the context of big data and AI -- Chapter 11. Significance of fog computing to machine learning-enabled IoT for smart applications across industries -- Chapter 12. New cloud computing-based strategy for coordinating multi-robot systems -- Chapter 13. Impact of uavs in agriculture -- Chapter 14. A survey on diagnosis of hazardous gas emission using AI techniques -- Chapter 15. IoVST: internet of vehicles and smart traffic architecture, applications, and challenges -- Chapter 16. Smart cities: redefining urban life through IoT -- Chapter 17. IoT and machine learning on smart home-based data and a perspective on fog computing implementation -- Chapter 18. Activity recognition and IoT-based analysis using time series and CNN -- Chapter 19. A comprehensive review of IoT reliability and its measures: perspective analysis -- Chapter 20. Sustainable IoT for smart environmental control -- Chapter 21. Evolutionized industry with the Internet of things -- Chapter 22. Integration of WSN and IoT: wireless networks architecture and protocols a way to smart agriculture -- Chapter 23. The current generation 5G and evolution of 6G to 7G technologies: the future IoT. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | |a The handbook of research on machine learning-enabled IoT for smart applications across industries highlights the importance of ML for IoT's success and diverse ML-powered IoT applications. This book addresses the problems and challenges in energy, industry, and healthcare and solutions proposed for ML-enabled IoT and new algorithms in ML. It further addresses their accuracy for existing real-time applications. | ||
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | |a Description based on title screen (IGI Global, viewed 07/07/2023). | ||
| 650 | 0 | |a Industrial management |x Data processing. | |
| 650 | 0 | |a Internet of things. | |
| 650 | 0 | |a Machine learning. | |
| 653 | |a Agriculture. | ||
| 653 | |a Equipment. | ||
| 653 | |a HR Analytics. | ||
| 653 | |a Internet of Things (IoT) | ||
| 653 | |a IoT in Healthcare. | ||
| 653 | |a Machine Learning. | ||
| 653 | |a Mechanization. | ||
| 653 | |a Pattern Recognition. | ||
| 653 | |a Smart Applications. | ||
| 653 | |a Solar Panel Tilting System. | ||
| 700 | 1 | |a Goel, Neha |d 1983- |e editor. | |
| 700 | 1 | |a Yadav, Ravindra Kumar |d 1973- |e editor. | |
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| 912 | |a ZDB-98-IGB | ||
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| contents | Chapter 1. Challenges in various applications using IoT -- Chapter 2. Pattern recognition by IoT systems of machine learning -- Chapter 3. Institutional pressures on the oil and gas industry: the role of machine learning -- Chapter 4. Generative adversarial networks: a game changer GAN for machine learning and IoT applications -- Chapter 5. Machine learning-enabled Internet of things solution for smart agriculture operations -- Chapter 6. An investigative study on Internet of things in healthcare -- Chapter 7. Machine learning-based threat identification systems: machine learning-based IDS using decision tree -- Chapter 8. Future outlier detection algorithm for smarter industry application using ML and AI: explainable AI and ML for smart industry evolution using ML/AI algorithms and implementations -- Chapter 9. Edge computing: optimizing performance and enhancing user experience -- Chapter 10. The role of wireless body area networks in smart healthcare system in the context of big data and AI -- Chapter 11. Significance of fog computing to machine learning-enabled IoT for smart applications across industries -- Chapter 12. New cloud computing-based strategy for coordinating multi-robot systems -- Chapter 13. Impact of uavs in agriculture -- Chapter 14. A survey on diagnosis of hazardous gas emission using AI techniques -- Chapter 15. IoVST: internet of vehicles and smart traffic architecture, applications, and challenges -- Chapter 16. Smart cities: redefining urban life through IoT -- Chapter 17. IoT and machine learning on smart home-based data and a perspective on fog computing implementation -- Chapter 18. Activity recognition and IoT-based analysis using time series and CNN -- Chapter 19. A comprehensive review of IoT reliability and its measures: perspective analysis -- Chapter 20. Sustainable IoT for smart environmental control -- Chapter 21. Evolutionized industry with the Internet of things -- Chapter 22. Integration of WSN and IoT: wireless networks architecture and protocols a way to smart agriculture -- Chapter 23. The current generation 5G and evolution of 6G to 7G technologies: the future IoT. |
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| dewey-ones | 006 - Special computer methods |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-98-IGB-00315776 |
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| indexdate | 2025-03-18T14:30:36Z |
| institution | BVB |
| isbn | 9781668487877 |
| language | English |
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| spelling | Handbook of research on machine learning enabled IoT for smart applications across industries edited by Neha Goel, Ravindra Kumar Yadav. Handbook of research on machine learning enabled Internet of things for smart applications across industries Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) IGI Global 2023. 30 PDFs (542 Seiten) text rdacontent electronic isbdmedia online resource rdacarrier Includes bibliographical references and index. Chapter 1. Challenges in various applications using IoT -- Chapter 2. Pattern recognition by IoT systems of machine learning -- Chapter 3. Institutional pressures on the oil and gas industry: the role of machine learning -- Chapter 4. Generative adversarial networks: a game changer GAN for machine learning and IoT applications -- Chapter 5. Machine learning-enabled Internet of things solution for smart agriculture operations -- Chapter 6. An investigative study on Internet of things in healthcare -- Chapter 7. Machine learning-based threat identification systems: machine learning-based IDS using decision tree -- Chapter 8. Future outlier detection algorithm for smarter industry application using ML and AI: explainable AI and ML for smart industry evolution using ML/AI algorithms and implementations -- Chapter 9. Edge computing: optimizing performance and enhancing user experience -- Chapter 10. The role of wireless body area networks in smart healthcare system in the context of big data and AI -- Chapter 11. Significance of fog computing to machine learning-enabled IoT for smart applications across industries -- Chapter 12. New cloud computing-based strategy for coordinating multi-robot systems -- Chapter 13. Impact of uavs in agriculture -- Chapter 14. A survey on diagnosis of hazardous gas emission using AI techniques -- Chapter 15. IoVST: internet of vehicles and smart traffic architecture, applications, and challenges -- Chapter 16. Smart cities: redefining urban life through IoT -- Chapter 17. IoT and machine learning on smart home-based data and a perspective on fog computing implementation -- Chapter 18. Activity recognition and IoT-based analysis using time series and CNN -- Chapter 19. A comprehensive review of IoT reliability and its measures: perspective analysis -- Chapter 20. Sustainable IoT for smart environmental control -- Chapter 21. Evolutionized industry with the Internet of things -- Chapter 22. Integration of WSN and IoT: wireless networks architecture and protocols a way to smart agriculture -- Chapter 23. The current generation 5G and evolution of 6G to 7G technologies: the future IoT. Restricted to subscribers or individual electronic text purchasers. The handbook of research on machine learning-enabled IoT for smart applications across industries highlights the importance of ML for IoT's success and diverse ML-powered IoT applications. This book addresses the problems and challenges in energy, industry, and healthcare and solutions proposed for ML-enabled IoT and new algorithms in ML. It further addresses their accuracy for existing real-time applications. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 07/07/2023). Industrial management Data processing. Internet of things. Machine learning. Agriculture. Equipment. HR Analytics. Internet of Things (IoT) IoT in Healthcare. Machine Learning. Mechanization. Pattern Recognition. Smart Applications. Solar Panel Tilting System. Goel, Neha 1983- editor. Yadav, Ravindra Kumar 1973- editor. IGI Global, publisher. Print version: 1668487853 9781668487853 |
| spellingShingle | Handbook of research on machine learning enabled IoT for smart applications across industries Chapter 1. Challenges in various applications using IoT -- Chapter 2. Pattern recognition by IoT systems of machine learning -- Chapter 3. Institutional pressures on the oil and gas industry: the role of machine learning -- Chapter 4. Generative adversarial networks: a game changer GAN for machine learning and IoT applications -- Chapter 5. Machine learning-enabled Internet of things solution for smart agriculture operations -- Chapter 6. An investigative study on Internet of things in healthcare -- Chapter 7. Machine learning-based threat identification systems: machine learning-based IDS using decision tree -- Chapter 8. Future outlier detection algorithm for smarter industry application using ML and AI: explainable AI and ML for smart industry evolution using ML/AI algorithms and implementations -- Chapter 9. Edge computing: optimizing performance and enhancing user experience -- Chapter 10. The role of wireless body area networks in smart healthcare system in the context of big data and AI -- Chapter 11. Significance of fog computing to machine learning-enabled IoT for smart applications across industries -- Chapter 12. New cloud computing-based strategy for coordinating multi-robot systems -- Chapter 13. Impact of uavs in agriculture -- Chapter 14. A survey on diagnosis of hazardous gas emission using AI techniques -- Chapter 15. IoVST: internet of vehicles and smart traffic architecture, applications, and challenges -- Chapter 16. Smart cities: redefining urban life through IoT -- Chapter 17. IoT and machine learning on smart home-based data and a perspective on fog computing implementation -- Chapter 18. Activity recognition and IoT-based analysis using time series and CNN -- Chapter 19. A comprehensive review of IoT reliability and its measures: perspective analysis -- Chapter 20. Sustainable IoT for smart environmental control -- Chapter 21. Evolutionized industry with the Internet of things -- Chapter 22. Integration of WSN and IoT: wireless networks architecture and protocols a way to smart agriculture -- Chapter 23. The current generation 5G and evolution of 6G to 7G technologies: the future IoT. Industrial management Data processing. Internet of things. Machine learning. |
| title | Handbook of research on machine learning enabled IoT for smart applications across industries |
| title_alt | Handbook of research on machine learning enabled Internet of things for smart applications across industries |
| title_auth | Handbook of research on machine learning enabled IoT for smart applications across industries |
| title_exact_search | Handbook of research on machine learning enabled IoT for smart applications across industries |
| title_full | Handbook of research on machine learning enabled IoT for smart applications across industries edited by Neha Goel, Ravindra Kumar Yadav. |
| title_fullStr | Handbook of research on machine learning enabled IoT for smart applications across industries edited by Neha Goel, Ravindra Kumar Yadav. |
| title_full_unstemmed | Handbook of research on machine learning enabled IoT for smart applications across industries edited by Neha Goel, Ravindra Kumar Yadav. |
| title_short | Handbook of research on machine learning enabled IoT for smart applications across industries |
| title_sort | handbook of research on machine learning enabled iot for smart applications across industries |
| topic | Industrial management Data processing. Internet of things. Machine learning. |
| topic_facet | Industrial management Data processing. Internet of things. Machine learning. |
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