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Machine learning algorithms for IoT services in big data and cloud computing

With an increasing number of devices connected to the internet, the volume of data generated and processed at greater speed has increased significantly, especially with the demand for action in real-time. For these scenarios, the existing cloud infrastructure is a sub-optimal solution as the data generated are sent to various distant cloud centres. Integrating machine learning techniques into the existing cloud can therefore offer improved effectiveness.

There is also a very large amount of data stored in the cloud which can act as input for machine learning algorithms. A simple machine learning method such as clustering can organize and group different data together, after which other cognitive and predictive techniques can be used to improve outcomes. Data scientists have recently begun using various Machine Learning and Artificial Intelligence methods in cloud for efficient computing (examples include Amazon Web Services with Keras, IBM Watson, and Microsoft Cognitive AI).

This collection presents research the various innovations and challenges for the existing cloud system to integrate ML and AI.

Edited by:

Anand Paul, The School of Computer Science and Engineering, Kyungpook National University, South Korea
Naveen Chilamkurti, La Trobe University, Melbourne, Australia
Ching-Hsien (Robert) Hsu, Department of Computer Science, National Chung Cheng University, Taiwan

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