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1
Harnessing the Power of CNN -SVM for Kidney Disease Classif..:
, In:
2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)
,
Sariyal, Ranjan
;
Singh, Harpal
;
Kaur, Mandeep
. - p. 1-6 , 2024
Link:
https://doi.org/10.1109/ICRITO61523.2024.10522311
RT T1 <cTitle>2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)</cTitle>: T1 <Title>Harnessing the Power of CNN -SVM for Kidney Disease Classification: A Comprehensive Study</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10522311&Exemplar=1&LAN=DE A1 Sariyal, Ranjan A1 Singh, Harpal A1 Kaur, Mandeep A1 Lamba, Shweta YR 2024 SN 2769-2884 K1 Support vector machines K1 Neural networks K1 Predictive models K1 Data models K1 Robustness K1 Convolutional neural networks K1 Kidney K1 Support Vector Machine (SVM) K1 End-stage kidney disease (ESKD) K1 Convolutional Neural Network (CNN) SP 1 OP 6 LK http://dx.doi.org/https://doi.org/10.1109/ICRITO61523.2024.10522311 DO https://doi.org/10.1109/ICRITO61523.2024.10522311 SF ELIB - SuUB Bremen
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