Time-based smart home controllers govern their environment with a predefined routine, without knowing if this is the most efficient way. Finding suitable model to predict energy consumption could prove be an optimal method manage electricity usage. The work presented in paper outlines development of prediction that controls home, adapting external environmental conditions and occupation. A back...

Potts model is a powerful tool to uncover community structure in complex networks. Here, we propose a new framework to reveal the optimal number of communities and stability of network structure by quantitatively analyzing the dynamics of Potts model. Specifically we model the community structure detection Potts procedure by a Markov process, which has a clear mathematical explanation. Then we ...

A common method to study the dynamic behavior of macroeconomic variables is using linear time series models; however, they are unable to explain nonlinear behavior of the series. Given the dependency between stock market and derivatives, the behavior of the underlying asset price can be modeled using Markov switching process properties and the economic regime significance. In this paper, a two-...

growing amount of information on biological sequences has made application of statistical approaches necessary for modeling and estimation of their functions. in this paper, sensitivity and specificity of the first and second markov chains for prediction of genes was evaluated using the complete double stranded  dna virus. there were two approaches for prediction of each markov model parameter,...

A new type of Hidden Markov Model (HMM) developed based on the fuzzy clustering result is proposed for identification of human motion. By associating the human continuous movements with a series of human motion primitives, the complex human motion could be analysed as the same process as recognizing a word by alphabet. However, because the human movements can be multipaths and inherently stocha...

Recognition of human speech has long been a hot topic among artificial intelligence and signal processing researches. Most of current policies for this subject are based on extraction of precise features of voice signal and trying to make most out of them by heavy computations. But this focus on signal details has resulted in too much sensitivity to noise and as a result, the necessity of compl...