Automatic lip-reading plays an important role in human computer interaction in noisy environments where audio speech recognition may be difficult. However, similar to speech recognition, lip-reading systems also face several challenges due to variances in the inputs, such as with facial features, skin colors, speaking speeds, and intensities. In this study a new method has been proposed for extracting features from a video containing a certain Persian words without any audio signal. The method is based on the fast furrier transform combined with the color specification of the frames in the recorded video of the spoken word. To improve the system performance visual word has been used as the shortest element of visual speech. Five speaker, three men and two women, have participated for capturing the videos of the spoken words. After obtaining features from the videos an artificial neural network has been employed as classifier. The experimental results show the average accuracy about 86.8% in recognition 31 Persian words Manuscript profile

Discrimination between iron deficiency (IDA) anemia and β-thalassemia trait (β-TT) is a time consuming and costly problem. Because, they have approximate similar effects on routine blood test indices, in some cases, the complementary tests, which are expensive and time consuming, would be needed for differentiate the anemia. Complete blood count (CBC) is a fast, inexpensive, and accessible medical test that is used as a primary test for diagnosis anemia. However, when the CBC indices cannot exactly state the subject, more advanced tests such as electrophoresis of hemoglobin must be performed. In this study, the CBC indices have been considered as the inputs of classifier and the chosen architecture is pattern-based input selection artificial neural network (PBIS-ANN). For evaluation the proposed method, traditional methods, which are still using for the problem such as Mentzer Index (MI), and several automated anemia diagnostic systems such as artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multi-layer perceptron (MLP) have been compared with the proposed method. The results indicate that the proposed method significantly outperforms the mentioned methods. Manuscript profile