Unsupervised Learning of Time-Series Classification Using Machine Learning Through Fertigation System-Reference-Cited by-同舟云学术

Unsupervised Learning of Time-Series Classification Using Machine Learning Through Fertigation System

Published:2024 Issue: Volume: Page:81-89
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:en
Short-container-title:

Author:

Shapiee Muhammad Nur Aiman^ORCID,Shapari Mohd Akid Shazri Mohd,Rahman Mohd Izzat Mohd,Jalil Azaini Aizat Abdul,Razman Mohd Azraai Mohd^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-8498-5_7

Reference12 articles.

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2. Liakos, K., Busato, P., Moshou, D., Pearson, S., Bochtis, D.: Machine learning in agriculture: a review. Sensors 18, 2674 (2018). https://doi.org/10.3390/s18082674

3. Sayari, S., Mahdavi-Meymand, A., Zounemat-Kermani, M.: Irrigation water infiltration modeling using machine learning. Comput. Electron. Agric. 180, 105921 (2021). https://doi.org/10.1016/j.compag.2020.105921

4. Isa, W.H.M., Muhammad, K.F., Khairuddin, I.M., Ishak, I., Yusoff, A.R.: Geometrical analysis on cap-shaped coils for power optimization of the vibration-based electromagnetic harvesting system. IOP Conf. Ser. Mater. Sci. Eng. 114, 12141 (2016). https://doi.org/10.1088/1757-899X/114/1/012141

5. Abdul Majeed, A.P.P., Taha, Z., Mohd Khairuddin, I., Wong, M.Y., Abdullah, M.A., Mohd Razman, M.A.: The control of an upper-limb exoskeleton by means of a particle swarm optimized active force control for motor recovery. IFMBE Proc. 58, 56–62 (2017). https://doi.org/10.1007/978-981-10-3737-5_12/COVER