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Automated and accurate classification of MR brain images is of crucially importance for medical analysis and interpretation. We proposed a novel automatic classification system based on particle swarm optimization (PSO) and artificial bee colony (ABC), with the aim of distinguishing abnormal brains from normal brains in MRI scanning. The proposed method used stationary wavelet transform (SWT) to extract features from MR brain images. SWT is translation-invariant and performed well even the image suffered from slight translation. Next, principal component analysis (PCA) was harnessed to reduce the SWT coefficients. Based on three different hybridization methods of PSO and ABC, we proposed three new variants of feed-forward neural network (FNN), consisting of IABAP-FNN, ABC-SPSO-FNN, and HPA-FNN. The 10 runs of K-fold cross validation result showed the proposed HPA-FNN was superior to not only other two proposed classifiers but also existing state-of-the-art methods in terms of classification accuracy. In addition, the method achieved perfect classification on Dataset-66 and Dataset-160. For Dataset-255, the 10 repetition achieved average sensitivity of 99.37%, average specificity of 100.00%, average precision of 100.00%, and average accuracy of 99.45%. The offline learning cost 219.077 s for Dataset-255, and merely 0.016 s for online prediction. Thus, the proposed SWT 1 PCA 1 HPA-FNN method excelled existing methods. It can be applied to practical use.

It is important to properly select and extract the features of speech emotion, and to reasonably construct the classifier for improving the accuracy of the speech emotion recognition. In this paper, the cubic spline fitting is used to fit curves of prosodic features extracted from speech signals and then the derivative parameters features of these fitting curves are attained. We closely combined the stage of feature selecting and the stage of feature classification, and considered the personal characters of different emotions based on genetic algorithm (GA) and support vector machine (SVM) classification algorithm. Using the optimal searching property of the GA, the system attained the maximum recognition rate by adaptively searching the order of emotion selection and the selection subset of features. This system's average recognition rate can reach as satisfying as 88.15% over six emotions.Fourth International Conference on Natural Computation 978-0-7695-3304-9/08 $25.00

Based on the well known Lenstra Lenstra Lovász (LLL) algorithm, we propose a possible swap LLL algorithm (P-SLLL) for lattice reduction aided (LRA) MIMO detection in this paper. The reduction process of the new algorithm is modified by searching for the next column swap through the whole basis, instead of the sequential implementation in the original LLL algorithm. Two different searching criteria are proposed, i.e. the random selection criterion and the optimal swap selection criterion. Comparing to the LLL algorithm, the PSLLL algorithm enjoys fast termination property and lower computational complexity, which can benefit practical hardware implementation. Simulation results prove our analysis and show that PSLLL aided linear MIMO detectors achieve the same performance as the LLL aided methods.

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