Various kind of mental illness is common in all parts of the globe, and all age groups. New diagnostic methods will be vital stage in reducing or eliminating these types of disease. In this paper novel fuzzy rule-base system is designed, and programmed by computer software, for diagnosis of Down Syndrom faces in an image. System input is an arbitrary color image. In first step face regions should be selected. An accurate face detection system is utilized which applies skin color, lip position, face shape information and ear texture properties, as the key parameters. After this stage, detected face regions are processing carefully by proposed fuzzy system, and some features such as face area and eye distance are investigating carefully. Finally the probability of being Down Syndrom is revealed by designed system. 98.33% correct detection is obtained applying this algorithm on various image databases. This system could be considered as the first step to device automatic system for diagnosis of illness and would help psychiatry.
In this paper we propose a clonal selection algorithm (PCSA) to solve a hybrid flow shop scheduling (HFS) problem considering the minimization of the sum of the total earliness and tardiness penalties. In the view of its non-deterministic polynomial-time hard nature, so we propose the clonal selection algorithm to deal with this problem. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. By comparison with solutions, performance obtained by NEH heuristi (Nawaz et al., 1983) and the HC heuristi (Ho and Chang, 1991) is presented. The results show that the proposed approach performs well for this problem.
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