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Design and Analysis of Deep Learning based Water Potability..:
, In:
2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)
,
M, Arumugam
;
P, Vijayakumar
;
C, Kannan A
... - p. 477-482 , 2023
Link:
https://doi.org/10.1109/ICSSAS57918.2023.10331807
RT T1 <cTitle>2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)</cTitle>: T1 <Title>Design and Analysis of Deep Learning based Water Potability Prediction</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10331807&Exemplar=1&LAN=DE A1 M, Arumugam A1 P, Vijayakumar A1 C, Kannan A A1 J, Velmurugan A1 G, Arun A1 C, Rohith Bhat YR 2023 K1 Correlation coefficient K1 Correlation K1 Predictive models K1 Solids K1 Public healthcare K1 Water resources K1 Standards K1 Multi-Layer Perceptron (MLP) K1 Neural networks K1 the Adam optimizer K1 binary cross-entropy loss function SP 477 OP 482 LK http://dx.doi.org/https://doi.org/10.1109/ICSSAS57918.2023.10331807 DO https://doi.org/10.1109/ICSSAS57918.2023.10331807 SF ELIB - SuUB Bremen
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