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Perspectives of Machine Learning and Deep Learning in Internet of Things and Cloud: Artificial Intelligence-Based Internet of Things System

Perspectives of Machine Learning and Deep Learning in Internet of Things and Cloud: Artificial Intelligence-Based Internet of Things System

Preethi Sambandam Raju, Revathi Arumugam Rajendran, Murugan Mahalingam
Copyright: © 2021 |Pages: 17
ISBN13: 9781799831112|ISBN10: 1799831116|ISBN13 Softcover: 9781799831129|EISBN13: 9781799831136
DOI: 10.4018/978-1-7998-3111-2.ch014
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MLA

Raju, Preethi Sambandam, et al. "Perspectives of Machine Learning and Deep Learning in Internet of Things and Cloud: Artificial Intelligence-Based Internet of Things System." Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing, edited by Sathiyamoorthi Velayutham, IGI Global, 2021, pp. 248-264. https://doi.org/10.4018/978-1-7998-3111-2.ch014

APA

Raju, P. S., Rajendran, R. A., & Mahalingam, M. (2021). Perspectives of Machine Learning and Deep Learning in Internet of Things and Cloud: Artificial Intelligence-Based Internet of Things System. In S. Velayutham (Ed.), Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing (pp. 248-264). IGI Global. https://doi.org/10.4018/978-1-7998-3111-2.ch014

Chicago

Raju, Preethi Sambandam, Revathi Arumugam Rajendran, and Murugan Mahalingam. "Perspectives of Machine Learning and Deep Learning in Internet of Things and Cloud: Artificial Intelligence-Based Internet of Things System." In Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing, edited by Sathiyamoorthi Velayutham, 248-264. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-3111-2.ch014

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Abstract

For centuries, the concept of a smart, autonomous learning machine has fascinated people. The machine learning philosophy is to automate the development of analytical models so that algorithms can learn continually with the assistance of accessible information. Machine learning (ML) and deep learning (DL) methods are implemented to further improve an application's intelligence and capacities as the quantity of the gathered information rises. Because IoT will be one of the main sources of information, data science will make a significant contribution to making IoT apps smarter. There is a rapid development of both technologies, cloud computing and the internet of things, considering the field of wireless communication. This chapter answers the questions: How can IoT intelligent information be applied to ML and DL algorithms? What is the taxonomy of IoT's ML and DL and profound learning algorithms? And what are real-world IoT data features that require data analytics?

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