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Titlebook: Intelligent Computing Theories and Application; 12th International C De-Shuang Huang,Vitoantonio Bevilacqua,Prashan Pre Conference proceedi

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41#
發(fā)表于 2025-3-28 18:05:09 | 只看該作者
42#
發(fā)表于 2025-3-28 19:21:13 | 只看該作者
Detecting Ventricular Fibrillation and Ventricular Tachycardia for Small Samples Based on EMD and Sytricular tachycardia (VT). Initially, we applied the EMD to decompose VF and VT signals into five sub-bands respectively. And then, we calculated the Symbol Entropy of each sub-bans as the feature to detect VT and VF. We employed the public data set to assess the proposed method. Experimental result
43#
發(fā)表于 2025-3-29 01:11:21 | 只看該作者
Coronary Heart Disease Recognition Based on Dynamic Pulse Rate Variability the idea of sliding window iterative. . Firstly, the principle of the feature extraction method(including time domain method, Poincare plot and information entropy) that combined with the idea of sliding window iterative is described. Secondly, The continuous blood pressure signals from the website
44#
發(fā)表于 2025-3-29 06:58:41 | 只看該作者
Multi-dictionary Based Collaborative Representation for 3D Biometrics concentrated on solving one-to-one verification problems, but they cannot deal with the one-to-many identification problems quite well. Quite recently, collaborative representation (CR) framework has been exploited to solve such identification problems. The original CR based method used the whole r
45#
發(fā)表于 2025-3-29 10:44:27 | 只看該作者
An Adaptive Multi-algorithm Ensemble for Fingerprint Matchinggorithms to achieve better performance on such databases. One of the major challenges is to design a fusion strategy which is both adaptive and improving with respect to the candidate database. This paper proposes an adaptive ensemble using statistical properties of two well known state-of-the-art m
46#
發(fā)表于 2025-3-29 11:41:52 | 只看該作者
47#
發(fā)表于 2025-3-29 17:15:06 | 只看該作者
48#
發(fā)表于 2025-3-29 20:56:59 | 只看該作者
49#
發(fā)表于 2025-3-30 03:51:49 | 只看該作者
50#
發(fā)表于 2025-3-30 07:20:40 | 只看該作者
SIPSO: Selectively Informed Particle Swarm Optimization Based on Mutual Information to Determine SNP the mechanism underlying susceptibility to complex diseases. Though many works have been done for their detection, the algorithmic development is still ongoing due to their computational complexities. In this study, we apply selectively informed particle swarm optimization (SIPSO) to determine SNP-
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