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Titlebook: Intelligent Computing in Bioinformatics; 10th International C De-Shuang Huang,Kyungsook Han,Michael Gromiha Conference proceedings 2014 Spr

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41#
發(fā)表于 2025-3-28 17:36:45 | 只看該作者
Tumor Clustering Using Independent Component Analysis and Adaptive Affinity Propagationr improve the performance of tumor clustering, we introduce a new tumor clustering approach based on independent component analysis (ICA) and affinity propagation (AP). Particularly, ICA is initially employed to select a subset of genes so that the effect of irrelevant or noisy genes can be reduced.
42#
發(fā)表于 2025-3-28 20:02:46 | 只看該作者
Research of Training Feedforward Neural Networks Based on Hybrid Chaos Particle Swarm Optimization-Bicle swarm optimization(ICMICPSO) algorithm. This algorithm made full use of the information of BP’s error back propagation and gradient. It used ICMICPS as the global optimizer to adjust the neural networks’ weights and thresholds, when network parameters converge around global optimum. And it used
43#
發(fā)表于 2025-3-29 00:35:57 | 只看該作者
Training Deep Fourier Neural Networks to Fit Time-Series Datausing a fast Fourier transform, then trained with regularization to improve generalization. A simple dynamic parameter tuning method is employed to adjust both the learning rate and regularization term, such that stability and efficient training are both achieved. We show how deeper layers can be ut
44#
發(fā)表于 2025-3-29 03:10:50 | 只看該作者
Regularized Dynamic Self Organized Neural Network Inspired by the Immune Algorithm for Financial Timgularization technique is used with the Dynamic self-organized multilayer perceptrons network that is inspired by the immune algorithm. The regularization has been addressed to improve the generalization and to solve the over-fitting problem. The results of an average 30 simulations generated from t
45#
發(fā)表于 2025-3-29 09:54:10 | 只看該作者
Multi-scale Level Set Method for Medical Image Segmentation without Re-initializationrated and a new penalty energy term is proposed to eliminate the time-consuming re-initialization procedure. Firstly, the circular window is used to define the local region so as to approximate the image as well as IIH. Then, multi-scale statistical analysis is performed on intensities of local circ
46#
發(fā)表于 2025-3-29 14:16:17 | 只看該作者
47#
發(fā)表于 2025-3-29 16:38:16 | 只看該作者
48#
發(fā)表于 2025-3-29 20:44:18 | 只看該作者
An Incremental Updating Based Fast Phenotype Structure Learning Algorithmnt phenotypes (e.g. disease or normal), and (2) find a subset of genes that can distinguish different groups. Due to the large number of genes and a mass of noise in microarray data, the existing methods are often of some limitations in terms of efficicency and effectiveness. In this paper, we devel
49#
發(fā)表于 2025-3-30 00:07:19 | 只看該作者
50#
發(fā)表于 2025-3-30 04:24:28 | 只看該作者
An Advanced Machine Learning Approach to Generalised Epileptic Seizure Detectionity in the brain manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 20 years. Electroencephalograms have been integral to these studie
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