Pronunciation training is an important part of Computer Assisted Training (CAPT) systems. Mispronunciation detection systems recognized pronunciation mistakes from user’s speech and provided them feedback about their pronunciation. Acoustic phonetic features plays a vital role in classification based applications. This research work investigated the suitability various acoustic features: pitch,...

Perovskite materials are central to the fields of energy conversion and storage, especially for fuel cells. However, they challenged by overcomplexity, coupled with a strong desire new discovery at high speed precision. Herein, we propose approach involving combination extreme feature engineering automated machine learning adaptively learn structure-composition-property relationships perovskite...

BACKGROUND In body surface potential mapping, increased spatial sampling is used to allow more accurate detection of a cardiac abnormality. Although diagnostically superior to more conventional electrocardiographic techniques, the perceived complexity of the Body Surface Potential Map (BSPM) acquisition process has prohibited its acceptance in clinical practice. For this reason there is an inte...

Multi-channel Gabor filters (MGFs) and Markov random fields (MRFs) are two common methods for texture analysis. This paper investigates their integration through a novel algorithm using the neighborhood-oscillating tabu search (NOTS) for high-resolution image classification. The NOTS algorithm fuses the texture features extracted by MGF and MRF. This algorithm has been compared with classical m...

In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a localized feature selection algorithm for clustering. The proposed algorithm computes adjusted and normalized scatter separability for ...

Most of the widely used pattern classification algorithms, such as Support Vector Machines (SVM), are sensitive to the presence of irrelevant or redundant features in the training data. Automatic feature selection algorithms aim at selecting a subset of features present in a given dataset so that the achieved accuracy of the following classifier can be maximized. Feature selection algorithms ar...

We consider feature selection and weighting for nearest neighbor classifiers. Atechnical challenge in this scenario is how to cope with discrete update of nearestneighbors when the feature space metric is changed during the learning process.This issue, called the target neighbor change, was not properly addressed in theexisting feature weighting and metric learning literature. I...

Feature selection techniques are often employed for reducing data dimensionality, improving computational efficiency, and most importantly selecting a subset of the important features model building. The present study explored utility Filter-Wrapper (FW) approach feature using terrestrial hyperspectral remote sensing imagery. efficacy FW was evaluated in conjunction with Random Forest (RF) Extr...