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Titlebook: Computational Intelligence in Pattern Recognition; Proceedings of CIPR Asit Kumar Das,Janmenjoy Nayak,Danilo Pelusi Conference proceedings

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樓主: 適婚女孩
21#
發(fā)表于 2025-3-25 04:06:30 | 只看該作者
The Role of Hypoxia in Radiation Response, of time is taken to carry out these tests and prediction of COVID 19 is done. The purpose of this work is to propose a model based on CNN and finding out the best fit segmentation algorithm to apply to the chest X-ray scans in order to predict the test result. Most importantly the result is instantaneous.
22#
發(fā)表于 2025-3-25 09:59:18 | 只看該作者
23#
發(fā)表于 2025-3-25 11:54:04 | 只看該作者
Wettbewerbsmatrix (M. E. Porter)rt Vector Machine (SVM) and Artificial Neural Network (ANN). The results show that DT classifier based scheme gives accuracy of 100% with training and testing time of 9.0088?ms and 2.7592?ms respectively and hence can be used as a secure IDM.
24#
發(fā)表于 2025-3-25 18:42:52 | 只看該作者
Strategiewerkzeuge aus der PraxisGamma Correction (PAGC) methodology. With our suggested strategy, we gained absolute supremacy in the entropy score as well as the colourfulness measure over standard gamma correction and histogram equalisation contrast-adjustment techniques.
25#
發(fā)表于 2025-3-25 20:33:19 | 只看該作者
26#
發(fā)表于 2025-3-26 02:33:51 | 只看該作者
27#
發(fā)表于 2025-3-26 07:35:59 | 只看該作者
Perceptive Analysis of Chronic Kidney Disease Data Through Conceptual Visualization,lection algorithms. The results of the perceptive analysis, viz., observed data during imputation and detection of outliers i.e., skewness, threshold values, p-value and other statistical reports are presented. Classifier models have been built after perceptive analysis and the CKD stage prediction results are also reported.
28#
發(fā)表于 2025-3-26 10:28:49 | 只看該作者
29#
發(fā)表于 2025-3-26 14:17:48 | 只看該作者
30#
發(fā)表于 2025-3-26 18:18:37 | 只看該作者
TextUnet: Text Segmentation Using U-net,ital images preprocessed in four different ways - dithering, binarization, edge enhancement and conversion to greyscale. We have also developed a model that outperforms the current topper of the ICDAR 2011 Robust Reading Competition’s Text Segmentation leader board and has an F-score of 83.90% and accuracy of 97.42%.
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