background: the time and frequency features of motor unit action potentials (muaps) extracted from electromyographic (emg) signal provide discriminative information for diagnosis and treatment of neuromuscular disorders. however, the results of conventional automatic diagnosis methods using muap features is not convincing yet. objective: the main goal in designing a muap characterization system...

In this paper, an Observation Points Classifier Ensemble (OPCE) algorithm is proposed to deal with High-Dimensional Imbalanced Classification (HDIC) problems based on data processed using the Multi-Dimensional Scaling (MDS) feature extraction technique. First, dimensionality of original imbalanced reduced MDS so that distances between any two different samples are preserved as well possible. Se...

We present an approach to automatic classification of spontaneously spoken voice messages. During overload periods at call-centers customers are offered a call-back at a later time. A speech dialog asks them to describe their concern on a voice box. The identified topics correspond to the supported service categories, which in turn determine the agent group the customer message is routed to. Ou...

Current inversion condition leads to incorrect operation of current based directional relay in power system with series compensated device. Application of the intelligent system for fault direction classification has been suggested in this paper. A new current directional protection scheme based on intelligent classifier is proposed for the series compensated line. The proposed classifier uses ...

Multiple Kernel Learning (MKL) algorithms are among the most successful classification methods for hyperspectral data. Nevertheless, these suffer from two main drawbacks of computational complexity and debility to admit end-to-end learning paradigm. This paper proposed a Convolutional Classifier (CKC) remote sensing images address issues. The CKC uses Nystrm approximation method estimate low-ra...

Classification is among the core tasks in machine learning. Existing classification algorithms are typically based on assumption of at least roughly balanced data classes. When performing involving imbalanced data, such classifiers ignore minority consideration overall accuracy. The performance traditional distribution insufficient because minority-class samples often more important than others...

since it is essential to deliver smoothed sinusoidal voltage to the customers, diagnosing power quality (pq) events has played important role in power delivery and conversion. this diagnostic scheme should be accurate to classify pq events from other events in power system. also it should be fast enough to rapidly mitigate pq events. in this paper, an algorithm based on core vector machine (cvm...