Abstract
Various machine learning (ML) techniques have been widely used in different layers of communication systems. ML techniques could provide intelligent functions that adaptively exploit the wireless resources, optimize the network operation, and guarantee the QoS needs in real-time applications [4, 7]. Moreover, ML techniques extend their existence and are applied to underwater communication by employing many methods of classification and regression in various problems.
In this chapter, we will show examples of applying the most common ML approaches of classification and regression to some UWSN challenges.
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Aziz El-Banna, A.A., Wu, K. (2021). ML: Modeling for Underwater Communication in IoUT Systems. In: Machine Learning Modeling for IoUT Networks. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-68567-6_5
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DOI: https://doi.org/10.1007/978-3-030-68567-6_5
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