In today"s world data mining plays a vital role for prediction of diseases in medical industry. Stroke is a lifethreatning disease that has been ranked third leading cause of death in states and in developing countries. The stroke is a leading cause of serious, long term disability in US. The time taken to recover from stroke disease depends on patients" severity. Number of work has been carried out for predicting various diseases by comparing the performance of predictive data mining. Here the classification algorithms like Decision Tree, Naive Bayes and Neural Network is used for predicting the presence of stroke disease with related number of attributes. In our work, principle component analysis algorithm is used for reducing the dimensions and it determines the attributes involving more towards the prediction of stroke disease and predicts whether the patient is suffering from stroke disease or not.

No abstract

An edge irregular total k-labeling f : V ∪ E → {1, 2, 3, . . . , k} of a graph G = (V, E) is a labeling of vertices and edges of G in such a way that for any two different edges uv and u 0 v 0 their weightsThe total edge irregularity strength tes(G) is defined as the minimum k for which the graph G has an edge irregular total k-labeling. In this paper, we determine the total edge irregularity strength of disjoint union of p isomorphic double wheel graphs and disjoint union of p consecutive non-isomorphic double wheel graphs. Keywords: Irregularity strength; total edge irregularity strength; edge irregular total labeling, disjoint union of double wheel graphs. (2010): 05C78. AMS Classification

The level sum method is based on the multi objective linear programming problem(MOLPP) and here pentagon fuzzy numbers is used for computing an optimal solution to the fuzzy linear programming problem(FLPP) without ranking functions. This is illustrated with a numerical example.

In this paper, we propose a new method for solving Fully Fuzzy linear programming Problem (FFLP) using ranking method .In this proposed ranking method, the given FFLPP is converted into a crisp linear programming (CLP) Problem with bound variable constraints and solved by using Robust's ranking technique and the optimal solution to the given FFLP problem is obtained and then compared between our proposed method and the existing method. Numerical examples are used to demonstrate the effectiveness and accuracy of this method.