Development of a TinyML based four-chamber refrigerator (TBFCR) for efficiently storing pharmaceutical products: Case Study: Pharmacies in Rwanda-Reference-Cited by-同舟云学术

Development of a TinyML based four-chamber refrigerator (TBFCR) for efficiently storing pharmaceutical products: Case Study: Pharmacies in Rwanda

Published:2022-02-18 Issue: Volume: Page:
ISSN:
Container-title:2022 14th International Conference on Machine Learning and Computing (ICMLC)
language:
Short-container-title:

Author:

HABIYAREMYE Joseph¹,ZENNARO Marco²,MIKEKA Chomora³,MASABO Emmanuel⁴

Affiliation:

1. ACEIoT, Univerity of Rwanda, Rwanda

2. ICTP, Italy

3. Science, Technology and Innovation (DSTI), Ministry of Ed, Malawi

4. ACE-DS, Univery of Rwanda, Rwanda

Funder

African Center of Excellence in Internet of Things

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3529836.3529932

Reference31 articles.

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3. Signoretti G.; Silva M.; Andrade P.; Silva I.; Sisinni E.; Ferrari P. An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity. Sensors 2021 21 4153. https://doi.org/10.3390/s21124153 Signoretti G.; Silva M.; Andrade P.; Silva I.; Sisinni E.; Ferrari P. An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity. Sensors 2021 21 4153. https://doi.org/10.3390/s21124153

4. Raza W.; Osman A.; Ferrini F.; Natale F.D. Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs. Drones 2021 5 127. https://doi.org/10.3390/drones5040127 Raza W.; Osman A.; Ferrini F.; Natale F.D. Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs. Drones 2021 5 127. https://doi.org/10.3390/drones5040127

5. Sanchez-Iborra R. LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices. Sensors 2021 21 5218. https://doi.org/10.3390/s21155218 Sanchez-Iborra R. LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices. Sensors 2021 21 5218. https://doi.org/10.3390/s21155218

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