DNA matching is a crucial step in sequence alignment. Since sequence alignment is an approximate matching process there is a need for good approximate algorithms. The process of matching in sequence alignment is generally finding longest common subsequences. However, finding a longest common subsequence may not be the best solution for either a database match or an assembly. An optimal alignment of subsequences is based on several factors, such as quality of bases, length of overlap, etc. Factors such as quality indicate if the data is an actual read or an experimental error. Fuzzy logic allows tolerance of inexactness or errors in sub sequence matching. We propose fuzzy logic for approximate matching of subsequences. Fuzzy characteristic functions are derived for parameters that influence a match. We develop a prototype for a fuzzy assembler. The assembler is designed to work with low quality data which is generally rejected by most of the existing techniques. We test the assembler on sequences from two genome projects namely, Drosophila melanogaster and Arabidopsis thaliana. The results are compared with other assemblers. The fuzzy assembler successfully assembled sequences and performed similar and in some cases better than existing techniques

K-means is a popular clustering algorithm that requires a huge initial set to start the clustering. K-means is an unsupervised clustering method which does not guarantee convergence. Numerous improvements to K-means have been done to make its performance better. Expectation Maximization is a statistical technique for maximum likelihood estimation using mixture models. It searches for a local maxima and generally converges very well. The proposed algorithm combines these two algorithms to generate optimum clusters which do not require a huge value of K and each cluster attains a more natural shape and guarantee convergence. The paper compares the new method with Fuzzy K-means on benchmark iris data.

Several methods for doing intrusion detection have been developed over the years. However, most of these methods are based on crisp statistical techniques that measure deviation from a norm. Due to the wide range of attacks on computers, statistical methods are not always effective because they aggregate many system variables into a single mathematical measure. Instead, taxonomies of attack features based on the concepts of fuzzy logic can be utilized to classify attacks and build simple response rules based on local system variables. Taxonomies however require correct hierarchial construction from subtaxonomies of attack classifiers. An architecture that defines self organizing taxonomies based on fuzzy logic is therefore developed for future investigation.

A fuzzy entity relationship diagram (ERD) data model for managing information about sets of related data files was converted to an object design. To do this, we developed new methods incorporating object model flattening, entity payload data containerization, and a nonintegrated object model design to allow expression of fuzziness.