Hybrid Horizons: Advancing Water Potability Prediction Through Hybrid Machine Learning-Reference-Cited by-同舟云学术

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Hybrid Horizons: Advancing Water Potability Prediction Through Hybrid Machine Learning

Published:2024-07-02 Issue: Volume: Page:175-180
ISSN:
Container-title:2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN)
language:
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Author:

Biju Jovita¹,Badgujar Chetan²,Poulose Alwin¹

Affiliation:

1. Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM) Vithura,School of Data Science,Thiruvananthapuram,Kerala,India,695551

2. Biosystems Engineering and Soil Sciences, The University of Tennessee Knoxville,Tennessee,TN,United States,37996

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10624687/10624749/10625242.pdf?arnumber=10625242

Reference16 articles.

1. Safe Drinking Water: Concepts, Benefits, Principles and Standards

2. Detection of contaminants in water supply: A review on state-of-the-art monitoring technologies and their applications

3. Water Quality Assessment and Evaluation of Human Health Risk in Mutangwi River, Limpopo Province, South Africa

4. Drinking water contamination and treatment techniques

5. A Comprehensive Analysis of the Effectiveness of Machine Learning Algorithms for Predicting Water Quality

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