Deep learning applications for cyber-physical systems:
"This book focuses on multidisciplinary aspects of computational engineering and industrial management engineering such as deep learning and supply chain management, covering trending applications such as Smart Agriculture, Smart Healthcare System, Cyber Physical Systems
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pennsylvania
IGI Global
[2021]
|
| Schlagworte: | |
| Online-Zugang: | DE-1050 DE-573 DE-898 DE-1049 DE-83 DE-91 DE-706 Volltext |
| Zusammenfassung: | "This book focuses on multidisciplinary aspects of computational engineering and industrial management engineering such as deep learning and supply chain management, covering trending applications such as Smart Agriculture, Smart Healthcare System, Cyber Physical Systems |
| Beschreibung: | Includes bibliographical references and index Chapter 1. COVID-19 spread prediction using prophet and data fusion algorithm -- Chapter 2. House plant leaf disease detection and classification using machine learning -- Chapter 3. Deep learning approaches for sentiment analysis challenges and future issues -- Chapter 4. Detection and classification of leaf disease using deep neural network -- Chapter 5. Development of an efficient monitoring system using fog computing and machine learning algorithms on healthcare 4.0 -- Chapter 6. Efficient facial expression recognition using deep learning techniques -- Chapter 7. Hypertensive retinopathy classification using improved clustering algorithm and the improved convolution neural network -- Chapter 8. Industrial automation using mobile cyber physical systems -- Chapter 9. Intrusion detection system using deep learning -- Chapter 10. Machine learning-based approach for predictive analytics in healthcare -- Chapter 11. Medical cyber physical system architecture for smart medical pumps -- Chapter 12. Predictive analytics for healthcare -- Chapter 13. Consistent hashing and real-time task scheduling in fog computing. - Mode of access: World Wide Web |
| Beschreibung: | 1 Online-Ressource (293 Seiten) |
| ISBN: | 9781799881636 |
| DOI: | 10.4018/978-1-7998-8161-2 |
Internformat
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| 245 | 1 | 0 | |a Deep learning applications for cyber-physical systems |c Monica Mundada, Seema Shedole, K G Srinivasa, and M. Shilpa, editor |
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Datensatz im Suchindex
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| id | DE-604.BV047856089 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:16:18Z |
| indexdate | 2024-07-20T06:38:32Z |
| institution | BVB |
| isbn | 9781799881636 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033238828 |
| oclc_num | 1302319276 |
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| physical | 1 Online-Ressource (293 Seiten) |
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| publisher | IGI Global |
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| spelling | Deep learning applications for cyber-physical systems Monica Mundada, Seema Shedole, K G Srinivasa, and M. Shilpa, editor Hershey, Pennsylvania IGI Global [2021] 1 Online-Ressource (293 Seiten) txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index Chapter 1. COVID-19 spread prediction using prophet and data fusion algorithm -- Chapter 2. House plant leaf disease detection and classification using machine learning -- Chapter 3. Deep learning approaches for sentiment analysis challenges and future issues -- Chapter 4. Detection and classification of leaf disease using deep neural network -- Chapter 5. Development of an efficient monitoring system using fog computing and machine learning algorithms on healthcare 4.0 -- Chapter 6. Efficient facial expression recognition using deep learning techniques -- Chapter 7. Hypertensive retinopathy classification using improved clustering algorithm and the improved convolution neural network -- Chapter 8. Industrial automation using mobile cyber physical systems -- Chapter 9. Intrusion detection system using deep learning -- Chapter 10. Machine learning-based approach for predictive analytics in healthcare -- Chapter 11. Medical cyber physical system architecture for smart medical pumps -- Chapter 12. Predictive analytics for healthcare -- Chapter 13. Consistent hashing and real-time task scheduling in fog computing. - Mode of access: World Wide Web "This book focuses on multidisciplinary aspects of computational engineering and industrial management engineering such as deep learning and supply chain management, covering trending applications such as Smart Agriculture, Smart Healthcare System, Cyber Physical Systems Systems engineering Machine learning Industrial applications Cooperating objects (Computer systems) Industrial applications Mundada, Monica R. edt Shedole, Seema edt Shilpa, M. edt IGI Global Sonstige oth Erscheint auch als Druck-Ausgabe 9781799881612 Erscheint auch als Druck-Ausgabe 1799881628 Erscheint auch als Druck-Ausgabe 9781799881629 Erscheint auch als Druck-Ausgabe 179988161X https://doi.org/10.4018/978-1-7998-8161-2 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Deep learning applications for cyber-physical systems Systems engineering Machine learning Industrial applications Cooperating objects (Computer systems) Industrial applications |
| title | Deep learning applications for cyber-physical systems |
| title_auth | Deep learning applications for cyber-physical systems |
| title_exact_search | Deep learning applications for cyber-physical systems |
| title_exact_search_txtP | Deep learning applications for cyber-physical systems |
| title_full | Deep learning applications for cyber-physical systems Monica Mundada, Seema Shedole, K G Srinivasa, and M. Shilpa, editor |
| title_fullStr | Deep learning applications for cyber-physical systems Monica Mundada, Seema Shedole, K G Srinivasa, and M. Shilpa, editor |
| title_full_unstemmed | Deep learning applications for cyber-physical systems Monica Mundada, Seema Shedole, K G Srinivasa, and M. Shilpa, editor |
| title_short | Deep learning applications for cyber-physical systems |
| title_sort | deep learning applications for cyber physical systems |
| topic | Systems engineering Machine learning Industrial applications Cooperating objects (Computer systems) Industrial applications |
| topic_facet | Systems engineering Machine learning Industrial applications Cooperating objects (Computer systems) Industrial applications |
| url | https://doi.org/10.4018/978-1-7998-8161-2 |
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