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Titlebook: Soft Computing: Biomedical and Related Applications; Nguyen Hoang Phuong,Vladik Kreinovich Book 2021 The Editor(s) (if applicable) and The

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樓主: GLAZE
31#
發(fā)表于 2025-3-26 21:50:09 | 只看該作者
Deep Learning Based COVID-19 Diagnosis by Joint Classification and SegmentationseNet169 model. We applied the proposed model to dataset contains 349 CT scans that are positive for COVID-19 and 397 negative CT scans that are normal or contain other types of diseases. Experiment show that our model outperforms other methods in term of accuracy, sensitivity, F1, and AUC evaluation metrics.
32#
發(fā)表于 2025-3-27 02:07:12 | 只看該作者
Fine-Grained Network Traffic Classification Using Machine Learning: Evaluation and Comparisoned data to solve the fine-grained traffic classification problem. Experimental results showed that the decision tree and random forest got the highest accuracy at 96%. The decision tree also had the lowest prediction time, which is well-suited to be implemented in real-time fine-grained traffic classification applications.
33#
發(fā)表于 2025-3-27 06:23:26 | 只看該作者
34#
發(fā)表于 2025-3-27 12:10:25 | 只看該作者
1860-949X t computing for biomedical problems.Aims at practitioners an.This book lists current and potential biomedical uses of computational intelligence methods. These methods are used in diagnostics and treatment of such diseases as cancer, cardiac diseases, pneumonia, stroke, and COVID-19. Many biomedical
35#
發(fā)表于 2025-3-27 14:06:00 | 只看該作者
Bilattice CADIAG-II: Theory and Experimental Resultsion of 3,131 patients with extended information about patient’s medical history, physical examination, laboratory test results, clinical investigations and—last but not least—clinically confirmed discharge diagnoses.
36#
發(fā)表于 2025-3-27 20:12:11 | 只看該作者
On an Application of Lattice-Valued Integral Transform to Multicriteria Decision Makinge. We use this integral transform as an extended qualitative aggregation operator in multicriteria decision making to get the evaluation of alternatives for a decision-maker. The proposed approach is illustrated and compared with a common approach on a car selection problem.
37#
發(fā)表于 2025-3-27 22:18:43 | 只看該作者
38#
發(fā)表于 2025-3-28 05:56:38 | 只看該作者
A Radial Basis Neural Network Approximation with Extended Precision for Solving Partial Differential functions. They have the properties of universal approximation and mesh-free discretisation. Substantial enhancements in the solution accuracy, matrix condition number, and high convergence rate are achieved.
39#
發(fā)表于 2025-3-28 07:44:27 | 只看該作者
40#
發(fā)表于 2025-3-28 13:33:22 | 只看該作者
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