Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

Molecular and Functional Characterization of Antimicrobial Peptides in Crustaceans

Most living organisms are constantly exposed to potentially harmful pathogens. It is the immune system of the organism that enables it to survive in an environment loaded with dangerous pathogenic microorganisms. The innate immunity provides organisms with a rapid and non-specific first line of defense against pathogens. It includes physical barriers such as skin and mucous membranes and chemical barriers including the high acidity of gastric juice, and specialized soluble molecules that possess antimicrobial activity. One of the well-known innate immune defense mechanisms is the production of antimicrobial substances by specific cells or tissues of the organisms. Antimicrobial peptides (AMPs) are such natural substances that