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In den WarenkorbZustand: New. AudienceResearchers and industry engineers in computer science and artificial intelligence, IT professionals, network administrators, cybersecurity experts.Sachi Nandan Mohanty received his PhD from IIT Kharagpur 2015 and he is now an associate professor in.
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Verlag: John Wiley & Sons Inc, New York, 2021
ISBN 10: 1119785804 ISBN 13: 9781119785804
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: new. Hardcover. Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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In den WarenkorbZustand: New. 2021. 1st Edition. Hardcover. . . . . .
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In den WarenkorbZustand: New.
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In den WarenkorbBuch. Zustand: Neu. Neuware - Im Zeitalter des Internet of Things (IoT) erzeugen Edge-Geräte in jedem Sekundenbruchteil gigantische Datenmengen. Dabei besteht das Hauptziel dieser Netzwerke darin, aus den gesammelten Daten sinnvolle Informationen abzuleiten. Gleichzeitig werden gewaltige Datenmengen in die Cloud übertragen, was extrem teuer und zeitaufwändig ist. Es ist somit notwendig, effiziente Mechanismen für die Verarbeitung dieser gewaltigen Datenmengen zu entwickeln, und dafür sind effiziente Datenverarbeitungstechniken erforderlich. Nachhaltige Paradigmen wie Cloud Computing und Fog Computing tragen zu einem geschickten Umgang mit Themen wie Leistung, Speicher- und Verarbeitungskapazitäten, Wartung, Sicherheit, Effizienz, Integration, Kosten, Energieverbrauch und Latenzzeiten bei. Allerdings werden ausgefeilte Analysetools benötigt, um die Anfragen in einer optimalen Zeit zu bearbeiten. Daher wird derzeit eifrig an der Entwicklung eines effektiven und effizienten Rahmens geforscht, um den größtmöglichen Nutzen zu erhalten.Bei der Verarbeitung der gewaltigen Datenmengen steht das maschinelle Lernen besonders hoch im Kurs und wird in zahlreichen Disziplinen angewandt, auch in den sozialen Medien.In Machine Learning Approach for Cloud Data Analytics in IoT werden sämtliche Aspekte des IoT, des Cloud Computing und der Datenanalyse ausführlich erläutert und aus verschiedenen Perspektiven betrachtet. Das Buch präsentiert den neuesten Stand der Forschung und fortschrittliche Themen. So erhalten die Leserinnen und Leser aktuelle Informationen und können das gesamte Spektrum der Anwendungen von IoT, Cloud Computing und Datenanalyse erfassen.
Verlag: John Wiley & Sons Inc, New York, 2021
ISBN 10: 1119785804 ISBN 13: 9781119785804
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: new. Hardcover. Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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In den WarenkorbHardcover. Zustand: Brand New. 500 pages. 9.25x6.22x1.34 inches. In Stock.
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In den WarenkorbZustand: New. 2021. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
Verlag: John Wiley & Sons Inc, New York, 2021
ISBN 10: 1119785804 ISBN 13: 9781119785804
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: new. Hardcover. Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.