Outliers and outlier detection are among the most important concepts of data processing in different applications. While there are many methods for outlier detection, each detection problem needs to be solved with the method most suited to its unique characteristics and features. This paper first classifies different outlier detection methods used in different fields and applications to provide a better understanding, and then presents a new fuzzy method for outlier detection. The proposed method uses the fuzzy logic and the local density to assign a point to data instances, and then determines whether a piece of data is normal or outlier based on the value of resulted membership function. Evaluation of the proposed outlier detection algorithm with synthetic datasets demonstrates its good accuracy; moreover, evaluation of the performance in solving real datasets show that the proposed method outperforms the k-means and K-NN algorithms. Manuscript profile

Bioinformatics is a new science that uses algorithms, computer software and databases in order to solve biological problems, especially in the cellular and molecular areas. Bioinformatics is defined as the application of tools of computation and analysis to the capture and interpretation of biological data. Protein Structure Prediction (PSP) is one of the most complex and important issues in bioinformatics, and extensive researches has been done to solve this problem using evolutionary algorithms. In this paper, we propose a genetic based method in order to solve protein structure prediction problem with increasing the accuracy of prediction, using a crossover operator based on pattern mask. Further, we compare two genetic based method to evaluate the proposed method. The results of the implementation of our proposed algorithm on five standard test sequences show that the use of a pattern mask-based crossover operator in the genetic algorithm can significantly improve the accuracy compared to previous similar algorithms. Manuscript profile