Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology-Reference-Cited by-同舟云学术

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Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology

Published:2019-07-02 Issue:12 Volume:32 Page:8315-8366
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Zaidan A. A.,Zaidan B. B.,Alsalem M. A.,Albahri O. S.,Albahri A. S.,Qahtan M. Y.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-019-04325-3.pdf

Reference142 articles.

1. Zaidan A, Zaidan B et al (2018) A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations. Artif Intell Rev. https://doi.org/10.1007/s10462-018-9648-9

2. Tan J, Koo SG (2014) A survey of technologies in internet of things. In: 2014 IEEE international conference on distributed computing in sensor systems. IEEE

3. Gia TN et al (2018) Energy efficient wearable sensor node for IoT-based fall detection systems. Microprocess Microsyst 56:34–46

4. Ishii H et al (2016) An early detection system for dementia using the M2M/IoT platform. Procedia Comput Sci 96:1332–1340

5. Kapoor A et al (2016) Implementation of IoT (Internet of Things) and image processing in smart agriculture. In: 2016 international conference on computation system and information technology for sustainable solutions (CSITSS). IEEE

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