On Euclidean and Noetherian Entropies in AdS Space

We examine the Euclidean action approach, as well as that of Wald, to the entropy of black holes in asymptotically $AdS$ spaces. From the point of view of holography these two approaches are somewhat complementary in spirit and it is not obvious why they should give the same answer in the presence of arbitrary higher derivative gravity corrections. For the case of the $AdS_5$ Schwarzschild black hole, we explicitly study the leading correction to the Bekenstein-Hawking entropy in the presence of a variety of higher derivative corrections studied in the literature, including the Type IIB $R^4$ term. We find a non-trivial agreement between the two approaches in every case. Finally, we give a general way of understanding the equivalence of these two approaches.Comment: 36 pages, 1 figure, LaTex, v2: references added as well as clarificatory remarks in the introductio

Prediction of Heart Disease by Clustering and Classification Techniques Prediction of Heart Disease by Clustering and Classification Techniques

International audienceEvery year 19 million people approximately die from heart disease worldwide. A heart patient shows several symptoms and it is very tough to attribute them to the heart disease in so many steps of disease progression. Data mining, as an answer to extract a hidden pattern from the clinical dataset, are applied to a database in this analysis. All available algorithms in classification technique are compared to each other to achieve the highest accuracy. To further increase the correctness of the solution, the dataset is preprocessed by different unsupervised and supervised algorithms. The two important tasks which are needed for the development of classifier come under data mining and they are clustering and classification. In K-means clustering the initial point selection effects on the results of the algorithm, both in the number of clusters found and their centroids. Methods to enhance the k-means clustering algorithm are discussed. With the help of these methods efficiency, accuracy and performance are improved. So, to improve the performance of clusters the Normalization which is a pre-processing stage is used to enhance the Euclidean distance by calculating more nearer centers, which result in a reduced number of iterations which will reduce the computational time as compared to k-means clustering. Finally, the classifiers are developed with Logistic regression by using the data extracted by K-Means Clustering. The techniques adopted in the design of classifier perform relatively well in terms of classification results better compared to clustering techniques