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RNAs play a fundamental role in many biological and medical processes and the activity of RNA is directly dependent to itsstructure. Designing RNA structures is a basic problem in biology that is important in the treatment and nanotechnology. In this regard, some algorithms have been formed to predict RNA secondary structure. In this paper, we present an algorithm to accurately predict RNA secondary structure based on minimum free energy and maximum number of adjacent base pairs. This algorithm stands on a heuristic approach, which employs a dot matrix representation of all possible base pairs in RNA. Afterward, stems are extracted from the dot matrix and decreasingly sorted based on their length. Then the stems with equal length are increasingly sorted according to the free energy. Finally, the stems are orderly selected to form RNA secondary structure. The proposed algorithm is performed on some datasets containing CopA, CopT, R1inv, R2inv, Tar, Tar*, DIS, IncRNA54, and RepZ in the bacteria. Experimental results showed high accuracy of 95.71% of the proposed algorithm. This algorithm is run in lower computational time in comparison to the other similar approaches.

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The goal of facial animation is to synthesize realistic facial animated images using computer graphics. Because of its capability in creating facial animated images using a few amount of information, facial animation using feature vectors was extensively studied in recent years. In general, this method is considered as one of the performance-driven facial animation base methods. In this regard, facial feature vectors, which reflect rigid and non-rigid movements of the face, are extracted using facial analysis methods. These feature vectors are then used for transferring the facial movements to a graphical model. Our approach in this paper is merging keyframing and facial animation using feature vectors. Using this method, the amount of the submitted information is decreased to about 30%; while the synthesized sequences have 4.95% mean squared error and 0.000629 difference of correlation relative to input sequences. This error is negligible compared to the error of synthesized sequences without using interpolation.

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In this paper, we propose a novel method for tracking multiple objects in video sequences. this approach can track objects in crowded scenes with occlusions and random change directions of object movements, efficiently. this method is an extension to particle filter tracking approach that we named it 3-D particle filter. in the proposed method, the features of objects that cannot be used in the process of posterior probability estimation in particle filter, are used for as third dimension and improve the estimation of 2-d particle filter in each frame. we test our method and compare it with 3 other particle filter methods on the famous data sets such as PETS09. The results show the improvement of accuracy and a reduction of error in tracking, about 12%

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Objective: To classify different types of acute leukemia based on cooperative game theory and Shapley value. Materials and Methods: In this study, patients data were collected from Flow Cytometry tests of the Iran Blood Transfusion Organization (IBTO) have been used. 304 different diagnosed samples in 8 classes of acute leukemia were investigated. Samples were initially in numerical format. In the next stage, we transformed them into Boolean format according to the defined threshold. Then, weights were assigned to these samples based on cooperative game theory and Shapley value. In this regard, different samples of acute leukemia were separated and classified (Learning Phase). In the diagnosis phase, using similarity measures, the similarities between new under study and the training samples were assessed and the type of under study leukemia were detected (Diagnosis phase). Results: The accuracy rate of the classification method based on the cooperative game theory for leukemia was 96.3% which indicates that the proposed method has a considerable precision rate to classify the different kind of classes. In order to find the validity and efficiency of the proposed method, the results were compared with neural network, which is one of the useful learning algorithms. The accuracy rate of the classification method based on Radial Basis Function method (RBF) was 91.80%. Conclusion: Considering the data, the proposed method gave very hopeful results for acute leukemia classification. In this regard, it can assist hematologists and physicians in reasonable and accurate diagnosis of the kind of leukemia, to make more suitable decisions.