No abstract

Feature subset selection is a technique for reducing the attribute space of a feature set. In other words, it is identifying a subset of features by removing irrelevant or redundant features. A good feature set that contains highly correlated features with the class improves not only the efficiency of the classification algorithms but also the classification accuracy. A novel metric that integrates the correlation and reliability information between each feature and each class obtained from multiple correspondence analysis (MCA) is currently the popular solution to score the features for feature selection. However, it has the disadvantage that p-value which examines the reliability is conventional confidence interval. In this paper, modified multiple correspondence analysis (M-MCA) is used to improve the reliability. The efficiency and effectiveness of proposed method is demonstrated through extensive comparisons with MCA using five benchmark datasets provided by WEKA and UCI repository. Naïve bayes, decision tree and jrip are used as the classifiers. The classification results, in terms of classification accuracy and size of feature subspace, show that the proposed Modified-MCA outperforms three other feature selection methods, MCA, information gain, and relief.

We report a patient with juvenile myoclonic epilepsy who subsequently developed temporal lobe epilepsy, which gradually became clinically dominant. Video telemetry revealed both myoclonic seizures and temporal lobe seizures. The temporal lobe seizures were accompanied by a focal recruiting rhythm with rapid generalisation on EEG, in which the ictal EEG pattern during the secondary generalised phase was morphologically similar to the ictal pattern during myoclonic seizures. The secondary generalised seizures of the focal epilepsy responded to sodium valproate, similar to the myoclonic epilepsy. In this rare case of coexistent Juvenile Myoclonic Epilepsy and Temporal lobe epilepsy, the possibility of focal epilepsy recruiting a generalised epileptic network was proposed and discussed.

Translating the human speech signal into the text words is also known as Automatic Speech Recognition System (ASR) that is still many challenges in the processes of continuous speech recognition. Recognition System for Continuous speech develops with the four processes: segmentation, extraction the feature, classification and then recognition. Nowadays, because of the various changes of weather condition, the weather news becomes very important part for everybody. Mostly, the deaf people can't hear weather news when the weather news is broadcast by using radio and television channel but the deaf people also need to know about that news report. This system designed to classify and recognize the weather news words as the Myanmar texts on the sounds of Myanmar weather news reporting. In this system, two types of input features are used based on Mel Frequency Cepstral Coefficient (MFCC) feature extraction method such MFCC features and MFCC features images. Then these two types of features are trained to build the acoustic model and are classified these features using the Convolutional Neural Network (CNN) classifiers. As the experimental result, The Word Error Rate (WER) of this entire system is 18.75% on the MFCC features and 11.2% on the MFCC features images.