انجمن کامپیوتر ایران

Two popular and better performing approaches to language Identification (LID) are Phone Recognition followed by Language Modeling (PRLM) and Parallel PRLM. In this paper, we report several improvements in Phone Recognition which reduces error rate in PRLM and PPRLM based LID systems. In our previous paper, we introduced APRLM approach that reduces error rate for about 1.3% in LID tasks. In this paper, we suggest other solution that overcomes APRLM. This new LID approach is named Generalized PRLM or GPRLM. Several language identification experiments were conducted and the proposed improvements were evaluated using OGI-MLTS corpus. Our results show that GPRLM overcomes PPRLM and APRLM about 2.5% and 1.2% respectively in two language classification tasks.

A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem; this leads to the fitter individual. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation. Results of simulation show that ARO outperforms GA because ARO results good structure in comparison with GA and the speed of convergence in ARO is more than GA. Finally, the ARO performance is statistically shown.

Human can determine optimal behaviors which depend on the ability to make planned and adaptive decisions. In this paper, we have proposed a predictive structure based on neuropsychological evidences to model human decision making process by concentrating on the role of frontal brain regions which are responsible for predictive control of human behavior. We have considered a model-based reinforcement learning framework to implement the relations between these brain areas. Finally, we have designed an experimental test to compare the function of model with human behavior in a maze task. Our results reveal that there is more than reward and punishment in human behavior, and considering higher cognitive functions such as prediction will help to have more reliable models which could better describe human behavior.