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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM29197

Machine Learning Based Smart Water Vending Machine for Convenient and Sustainable Hydration Solution

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM29197

Volume:

8 |

3 |

Mar 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

NK

N.Rajagopala Krishnan

DKM

DP Kodun Maring

VK

Vicky Kumar

VK

Vinay Kumar

BK

Bipin Kumar

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Abstract

This paper proposes a Machine Learning-Enhanced Smart Water Vending Machine (MLSWVM) designed to address the growing demand for accessible clean water while promoting sustainable resource management. By integrating advanced machine learning algorithms with IoT technology, the MLSWVM offers an intelligent and efficient water vending system. Key features include predictive maintenance to ensure uninterrupted operation, demand forecasting for proactive water replenishment, and personalized recommendations tailored to individual hydration needs. Additionally, the MLSWVM monitors water quality in real-time and minimizes energy consumption through efficient components and algorithms. A user-friendly interface enhances accessibility, supporting multiple languages and offering features such as payment options and hydration tips. The MLSWVM represents a significant step towards revolutionizing water access and consumption practices, providing a convenient and sustainable solution to hydration challenges. This initiative contributes to broader environmental conservation efforts and aligns with global goals for resource optimization and sustainability.

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Cite This Article

N.Rajagopala Krishnan, DP Kodun Maring, Vicky Kumar, Vinay Kumar, Bipin Kumar, "Machine Learning Based Smart Water Vending Machine for Convenient and Sustainable Hydration Solution," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 3, Mar 2024.

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12/31/45361

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