Since their formulation by Andrei Markov in 1906 [7], Markov chains (MC) and hidden Markov models (HMM) have found a place in diverse fields of science and engineering, from speech recognition to weather prediction to protein sequence alignment. Wherever a data set can be expressed as a string of discrete symbols, and when the data has a common source or common underlying principle, then for th...

An important step in fingerprint recognition is segmentation. During segmentation the fingerprint image is decomposed into foreground, background and low-quality regions. The foreground is used in the recognition process, the background is ignored. The low-quality regions may or may not be used, dependent on the recognition method. Pixel features of the gray-scale image form the basis of segmen...

We present a comparison of 6 methods for classification of sports audio. For the feature extraction we have two choices: MPEG-7 audio features and Mel-scale Frequency Cepstrum Coefficients(MFCC). For the classification we also have two choices: Maximum Likelihood Hidden Markov Models(ML-HMM) and Entropic Prior HMM(EP-HMM). EP-HMM, in turn, have two variations: with and without trimming of the m...

The purchase of eco-friendly products is encouraged by the governments due to its contributions sustainable development environment. It therefore important examine factors influencing products. Based on attitude-behavior-context (ABC) theory, this paper constructs a conceptual model, which explores how consumer’s perceived effectiveness affects individuals’ In details, attempts mediating role c...

background: routinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. we aimed at using a hidden markov model in order to detect the abnormal states of pulmonary tuberculosis in iran. methods: data for this study were the weekly number of newly diagnosed cases with sputum smear-positive p...

This paper presents a hidden Markov model (HMM) based approach to on-line handwritten digit recognition using stroke sequences. In this approach, a character instance is represented by a sequence of symbolic strokes, and the representation is obtained by component segmentation and stroke classification. The component segmentation is based on the delta lognormal model of handwriting generation. ...

We focus on the problem of speech recognition in the presence of nonstationary sudden noise, which is very likely to happen in home environments. As a model compensation method for this problem, we investigated the use of factorial hidden Markov model (FHMM) architecture developed from a clean-speech hidden Markov model (HMM) and a sudden-noise HMM. While in conventional studies this architectu...

The Geometric Brownian motion (GBM) is a standard method for modeling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, GBM has been considered unable to properly capture important features, like extreme behaviour or volatility clustering. We propose an approach by which, the parameters of the GBM follow...

MOTIVATION Multiply correlated datasets have become increasingly common in genome-wide location analysis of regulatory proteins and epigenetic modifications. Their correlation can be directly incorporated into a statistical model to capture underlying biological interactions, but such modeling quickly becomes computationally intractable. RESULTS We present sparsely correlated hidden Markov mo...

ÐMultiaspect target identification is effected by fusing the features extracted from multiple scattered waveforms; these waveforms are characteristic of viewing the target from a sequence of distinct orientations. Classification is performed in the maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). We utilize a continuous-H...