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Computer Science > Machine Learning

arXiv:2109.02645 (cs)
COVID-19 e-print

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[Submitted on 6 Sep 2021]

Title:Backpropagation and fuzzy algorithm Modelling to Resolve Blood Supply Chain Issues in the Covid-19 Pandemic

Authors:Aan Erlansari, Rusdi Effendi, Funny Farady C, Andang Wijanarko, Boko Susilo, Reza Hardiansyah
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Abstract:Bloodstock shortages and its uncertain demand has become a major problem for all countries worldwide. Therefore, this study aims to provide solution to the issues of blood distribution during the Covid-19 Pandemic at Bengkulu, Indonesia. The Backpropagation algorithm was used to improve the possibility of discovering available and potential donors. Furthermore, the distances, age, and length of donation were measured to obtain the right person to donate blood when it needed. The Backpropagation uses three input layers to classify eligible donors, namely age, body, weight, and bias. In addition, the system through its query automatically counts the variables via the Fuzzy Tahani and simultaneously access the vast database.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Information Theory (cs.IT)
Cite as: arXiv:2109.02645 [cs.LG]
  (or arXiv:2109.02645v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.02645
Related DOI: https://doi.org/10.5281/zenodo.4940183

Submission history

From: Aan Erlansari [view email]
[v1] Mon, 6 Sep 2021 15:00:12 UTC (524 KB)
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