Normalized Hamming similarity measures for intuitionistic fuzzy multisets and its application in medical diagnosis, Int

ABSTRACT As Similarity measure for fuzzy sets is one of the important research topics of fuzzy set theory, there are several methods to measure similarity between two fuzzy sets (FS), Intuitionistic fuzzy sets (IFS) and Intuitionistic fuzzy multi sets (IFMS). In this paper, the Normalized Hamming Similarity measure of Intuitionistic Fuzzy Multi sets (IFMS) is introduced. This new measure for IFMS is based on the geometrical interpretation of IFS which involves both similarity and dissimilarity. Using this measure, the application of medical diagnosis and pattern recognition are shown

Antimicrobial activity and phytochemical screening of Cynodon dactylon and Carica papaya

Presence of various phytochemical compounds in Cynodon dactylon and Carica papaya was qualitatively and quantitatively determined by various standard method of analysis. The suitable extraction method was identified for phytochemical compound extraction from the above selected plants. The present study revealed that ethanol is a suitable solvent system which showed the presence of high percentage in the range of 0.01 to 1.46 and 0.02 to 10.0 percentage of phytochemicals compared to other solvent extracts. The moderate percentage 0.01 to 1.0 and 0.00 to 0.7 percentages is obtained in chloroform extraction system and least is 0.00 to 1.04 and 0.01 to 0.64 percentage of constituents in acetone extract, very lowest percentage of phytochemical constituents was observed in the range of 0.00 to 0.03 and 0.00 to 0.04 percentage in hot water extraction system. The selected plants have wide range of phytochemical constituents, hence further structural elucidation of such bioactive compounds is essential to study their traditional use

Web Services: A BI Perspective

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Deep Learning Method for Estimation of Morphological Parameters Based on CT Scans

In this study, we propose a Convolutional Neural Network (CNN) with an assembly of non-linear fully connected layers for estimating body height and weight using a limited amount of data. This method can predict the parameters within acceptable clinical limits for most of the cases even when trained with limited data