In this study, a biomedical diagnosis system for pattern recognition with normal and abnormal classes has been developed. First, feature extraction processing was made by using the Doppler Ultrasound. During feature extraction stage, Wavelet transforms and shorttime Fourier transform were used. As next step, wavelet entropy were applied to these features. In the classification stage, hidden Mar...

This paper presents a novel approach for fraud detection in mobile phone networks by using a combination of Possibilistic Fuzzy C-Means clustering and Hidden Markov Model (HMM). The clustering technique is first applied on two calling features extracted from the past call records of a subscriber generating a behavioral profile for the user. The HMM parameters are computed from the profile, whic...

Introduction: Evoked potentials arisen by stimulating the brain can be utilized as a communication tool  between humans and machines. Most brain-computer interface (BCI) systems use the P300 component,  which is an evoked potential. In this paper, we evaluate the use of the hidden Markov model (HMM) for  detection of P300.  Materials and Methods: The wavelet transforms, wavelet-enhanced indepen...

Background: Multiple Sclerosis (MS) is the most frequent non-traumatic neurological disease capable of causing disability in young adults. Detection of MS lesions with magnetic resonance imaging (MRI) is the most common technique. However, manual interpretation of vast amounts of data is often tedious and error-prone. Furthermore, changes in lesions are often subtle and extremely unrepresentati...

Sign Languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge, which is interestingly implied to their lexical and syntactic organization levels. Hidden Markov models (HMM’s) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for v...

There are a lot of industrial applications that can be solved competitively by hard computing, while still requiring the tolerance for imprecision and uncertainty that can be exploited by soft computing. This paper presents a novel intrusion detection system (IDS) that models normal behaviors with hidden Markov models (HMM) and attempts to detect intrusions by noting significant deviations from...

The fast and accurate forecasting method can help makers to make appropriate strategy. Zadeh was given the definition of a fuzzy set in 1965. Song and Chissom proposed the definition and the forecasting framework of fuzzy time series in 1993. Sullivan and Woodall first proposed the forecasting method to handle one factor with probability Markov model in 1994. Li and Cheng proposed a stochastic ...

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long divided into several sections and the occurs in different section that defined as state of (HMM). The hybrid HMM, i.e., DNN-HMM, consists (DNN) with multiple layers exploit non-linear data. DNN initialized by using belief (DBN). DBN pre-trained built stacking top-down restr...

Named entity recognition can deeply explore semantic features and enhance the ability of vector representation text data. This paper proposes a named method based on multi-head attention to aim at problem fuzzy lexical boundary in Chinese recognition. Firstly, Word2vec is used extract word vectors, HMM ALBERT character Feedforward-attention mechanism fuse three then fused vectors remove by BiLS...