Speaker verification is the process of accepting or rejecting claimed identity in terms of its sound features. A speaker verification system can be used for numerous security systems, including bank account accessing, getting to security points, criminology and etc. When a speaker verification system wants to check the identity of individuals remotely, it confronts problems such as noise effect on speech signal and also identity falsification with speech synthesis. In this system, we have proposed a new speaker verification system based on Multi Model GMM, called SV-MMGMM, in which all speakers are divided into seven different age groups, and then an isolated GMM model for each group is created; instead of one model for all speakers. In order to evaluate, the proposed method has been compared with several speaker verification systems based on Naïve, SVM, Random Forest, Ensemble and basic GMM. Experimental results show that the proposed method has so better efficiency than others. Manuscript profile

Nowadays diabetes disease is one of the main problems of health domain and it’s known as the fourth factor of death in the world. The main problem with this dangerous disease is the late or weak diagnosis. The reason of weak diagnosis is because sometimes doctors aren’t able to select the right patterns or they can’t use the standard patterns very well, so the outcome is that the disease will be diagnosed by the patients when it has become late for controlling or curing it. Therefore, implementing a method which can help each person to have an authentic diagnosis of being or not being affected to this disease; can be an important step for prevention and controlling this special disease at the beginning of it. In this paper, a new method is presented for diagnosing diabetes disease which is able to extract the proper knowledge by helping to cluster and analyze the training patterns, after that in recognition phase it can diagnose diabetes disease precisely and fast via a fuzzy reward-penalty mechanism. For evaluating the proposed method, PIMA dataset has been used. The experimental results show that the proposed method has a better performance compared to other existing methods. Manuscript profile