Empowering sustainable industrial 4.0 systems with machine intelligence:
"This book addresses a wide range of topics related to industry 4.0 issues, its challenges and employment of smart and intelligent solutions to contain the errors and their impacts affecting stakeholders of such systems including, health industry, pharmaceutical industry, education industry, de...
Gespeichert in:
| Weitere beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Hershey, Pennsylvania
IGI Global
2022
|
| Schlagwörter: | |
| Links: | https://doi.org/10.4018/978-1-7998-9201-4 https://doi.org/10.4018/978-1-7998-9201-4 https://doi.org/10.4018/978-1-7998-9201-4 https://doi.org/10.4018/978-1-7998-9201-4 https://doi.org/10.4018/978-1-7998-9201-4 https://doi.org/10.4018/978-1-7998-9201-4 https://doi.org/10.4018/978-1-7998-9201-4 |
| Zusammenfassung: | "This book addresses a wide range of topics related to industry 4.0 issues, its challenges and employment of smart and intelligent solutions to contain the errors and their impacts affecting stakeholders of such systems including, health industry, pharmaceutical industry, education industry, defense systems, manufacturing industry, logistic support systems, supply chain management, social media industry, and disaster management systems etc |
| Beschreibung: | Includes bibliographical references and index Chapter 1. Time-series analysis and prediction of water quality through multisource data -- Chapter 2. Temporal analysis and prediction of ambient air quality using remote sensing, deep learning, and geospatial technologies -- Chapter 3. Automated multi-sensor board for IoT and ML-enabled livestock monitoring -- Chapter 4. How artificial intelligence can enhance predictive maintenance in smart factories -- Chapter 5. A review of artificial intelligence models in prognosticating abdominal aorta aneurysms -- Chapter 6. Enhanced water quality monitoring and estimation using a multi-modal approach -- Chapter 7. Machine intelligence in customer relationship management in small and large companies -- Chapter 8. Machine intelligence as a foundation of self-driving automotive (SDA) systems -- Chapter 9. Machine learning-based wearable devices for smart healthcare application with risk factor monitoring -- Chapter 10. Predicting the early stage of diabetes and finding the association of the symptoms -- Chapter 11. Role of machine learning in handling the COVID-19 pandemic -- Chapter 12. A rural healthcare mobile app: urdu voice-enabled mobile app for disease diagnosis. - Mode of access: World Wide Web |
| Umfang: | 1 Online-Ressource (315 Seiten) |
| ISBN: | 9781799892038 |
| DOI: | 10.4018/978-1-7998-9201-4 |
Internformat
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| 245 | 1 | 0 | |a Empowering sustainable industrial 4.0 systems with machine intelligence |c Muneer Ahmad, and Noor Zaman Jhanjhi, editor |
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| 500 | |a Includes bibliographical references and index | ||
| 500 | |a Chapter 1. Time-series analysis and prediction of water quality through multisource data -- Chapter 2. Temporal analysis and prediction of ambient air quality using remote sensing, deep learning, and geospatial technologies -- Chapter 3. Automated multi-sensor board for IoT and ML-enabled livestock monitoring -- Chapter 4. How artificial intelligence can enhance predictive maintenance in smart factories -- Chapter 5. A review of artificial intelligence models in prognosticating abdominal aorta aneurysms -- Chapter 6. Enhanced water quality monitoring and estimation using a multi-modal approach -- Chapter 7. Machine intelligence in customer relationship management in small and large companies -- Chapter 8. Machine intelligence as a foundation of self-driving automotive (SDA) systems -- Chapter 9. Machine learning-based wearable devices for smart healthcare application with risk factor monitoring -- Chapter 10. Predicting the early stage of diabetes and finding the association of the symptoms -- Chapter 11. Role of machine learning in handling the COVID-19 pandemic -- Chapter 12. A rural healthcare mobile app: urdu voice-enabled mobile app for disease diagnosis. - Mode of access: World Wide Web | ||
| 520 | |a "This book addresses a wide range of topics related to industry 4.0 issues, its challenges and employment of smart and intelligent solutions to contain the errors and their impacts affecting stakeholders of such systems including, health industry, pharmaceutical industry, education industry, defense systems, manufacturing industry, logistic support systems, supply chain management, social media industry, and disaster management systems etc | ||
| 650 | 4 | |a Artificial intelligence |x Industrial applications | |
| 650 | 4 | |a Management information systems | |
| 650 | 4 | |a Sociotechnical systems | |
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Datensatz im Suchindex
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| _version_ | 1821936332573769728 |
| any_adam_object | |
| author2 | Jhanjhi, Noor Zaman Ahmad, Muneer |
| author2_role | edt edt |
| author2_variant | n z j nz nzj m a ma |
| author_facet | Jhanjhi, Noor Zaman Ahmad, Muneer |
| building | Verbundindex |
| bvnumber | BV048240230 |
| collection | ZDB-98-IGB ZDB-1-IGE |
| ctrlnum | (ZDB-98-IGB)00276464 (OCoLC)1322805162 (DE-599)BVBBV048240230 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.4018/978-1-7998-9201-4 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-12-20T19:39:20Z |
| institution | BVB |
| isbn | 9781799892038 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033620737 |
| oclc_num | 1322805162 |
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| physical | 1 Online-Ressource (315 Seiten) |
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| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | IGI Global |
| record_format | marc |
| spellingShingle | Empowering sustainable industrial 4.0 systems with machine intelligence Artificial intelligence Industrial applications Management information systems Sociotechnical systems |
| title | Empowering sustainable industrial 4.0 systems with machine intelligence |
| title_auth | Empowering sustainable industrial 4.0 systems with machine intelligence |
| title_exact_search | Empowering sustainable industrial 4.0 systems with machine intelligence |
| title_full | Empowering sustainable industrial 4.0 systems with machine intelligence Muneer Ahmad, and Noor Zaman Jhanjhi, editor |
| title_fullStr | Empowering sustainable industrial 4.0 systems with machine intelligence Muneer Ahmad, and Noor Zaman Jhanjhi, editor |
| title_full_unstemmed | Empowering sustainable industrial 4.0 systems with machine intelligence Muneer Ahmad, and Noor Zaman Jhanjhi, editor |
| title_short | Empowering sustainable industrial 4.0 systems with machine intelligence |
| title_sort | empowering sustainable industrial 4 0 systems with machine intelligence |
| topic | Artificial intelligence Industrial applications Management information systems Sociotechnical systems |
| topic_facet | Artificial intelligence Industrial applications Management information systems Sociotechnical systems |
| url | https://doi.org/10.4018/978-1-7998-9201-4 |
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