Many algorithms have been proposed for the solution of the minimum vertex cover (MVC) problem, but the researchers are unable to find the optimality of an approximation algorithm. In this paper, we have proposed a method to evaluate that either the result returned by an approximation algorithm for the minimum vertex cover problem is optimal or not. The proposed method is tested on three algorithms, i.e., maximum degree greedy (MDG) algorithm, modified vertex support algorithm (MVSA) and clever steady strategy algorithm (CSSA). The proposed method provides an opportunity to test the optimality of an approximation algorithm for MVC problem with low computation complexity. The proposed method has performed well during experimentation, and its results brighten the path of successful implementation of the method for the evaluation of approximation algorithms for the minimum vertex cover (MVC) problem. The testing of the proposed method was carried out on small graph instances. The proposed method has resolved the problem to test the optimality of the approximation algorithm for the minimum vertex cover problem. This technique has digitized the process of finding out the accuracy of the optimal solution returned by approximation algorithms for MVC.

In this paper, new statistical features based approach (SFBA) for hourly energy consumption prediction using Multi-Layer Perceptron is presented. The model consists of four stages: data retrieval, data pre-processing, feature extraction and prediction. In the data retrieval stage, historical hourly consumed energy data has been retrieved from the database. During data pre-processing, filters have been applied to make the data more suitable for further processing. In the feature extraction stage, mean, variance, skewness, and kurtosis are extracted. Finally, Multi-Layer Perceptron has been used for prediction. For experimentation with Multi-Layer Perceptron with different training algorithms, a final model of the network was designed in which the scaled conjugate gradient (trainscg) was used as a network training function, tangent sigmoid (Tansig) as a hidden layer transfer function and linear function as an output layer transfer function. For hourly energy consumption prediction, a total of six weeks data of ten residential buildings has been used. To evaluate the performance of the proposed approach, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), evaluation measurements were applied.

The use of Data mining techniques on medical data is dramatically soar for determining helpful things which are used in decision making and identification. The most extensive data mining techniques which are used in healthcare domain are, classification, clustering, regression, association rule mining, classification and regression tree (CART). The suitable use of data mining algorithm can enhance the quality of prediction, diagnosis and disease classification. Valuation of data mining techniques demand for medical data mining is the major goal here, particularly to examine the local frequent disease like heart ailments, breast cancer, lung cancer and so on. We examine for discovering the locally frequent patterns through data mining technique in terms of cost performance speed and accuracy.