A Study on Machine Learning Techniques for Internet of Things in Societal Applications
| Autor: | K. Indu, Kumar M. Aswatha |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Realization (linguistics) Societal impact of nanotechnology 020206 networking & telecommunications 020207 software engineering Context (language use) Unstructured data 02 engineering and technology Machine learning computer.software_genre Data resources 0202 electrical engineering electronic engineering information engineering Artificial intelligence Electronics business Internet of Things computer Pace |
| Zdroj: | 2019 International Conference on Data Science and Communication (IconDSC). |
| DOI: | 10.1109/icondsc.2019.8816970 |
| Popis: | Until recent years, monitoring and analysing system inputs, responses were merely based on Sensor Systems. Gradually, Embedded Systems and other Data Resources including Remote Monitoring Units started gaining momentum. But, with advent of Internet of Things (IoT), the outlook and expectations are broadened. IoT introduced incredible volumes of structured and unstructured data of different formats. There is a need to investigate, the underlying concepts of Machine Learning, Internet of Things (IoT) and Embedded Systems. These domains grow and expand its frontiers at a very fast pace. This paper attempts to throw light on possibilities of combining different technological domains, for design and development of Smarter and Context Aware Intelligent Electronics Systems for Societal Utility. Effective implementation and realization of such systems by suitable fusion of essential inter-disciplinary concepts is expected to have considerable potential for societal impact in the years to come. |
| Databáze: | OpenAIRE |
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