This study was undertaken with an objective of testing the antibacterial and antifungal activities of Bauhinia purpurea leaves and identifying the bioactive compounds. The antimicrobial activity of leaf extract was determined in aqueous and organic extracts and the minimum inhibitory concentration (MIC) against six species of pathogenic and non-pathogenic microorganisms: Bacillus subtilis, Staphylococcus aureus, Salmonella typhi, Escherichia coli, Pseudomonas aeruginosa and Candida albicans using the disk diffusion method. The chemical constituents of organic plant extract were separated by thin layer chromatography and purified by column chromatography and further identified by gas chromatography-mass spectrometry (GC-MS) analysis. Significant inhibitory activity was observed with methanol extracts of plant against the test microorganisms while less antibacterial activity was observed in hexane, acetone and aqueous extracts. MIC of B. purpurea extract was B1,500 lg/ml against S. aureus and B. subtilis while this extract showed no inhibition against Gram-negative S. typhi, E. coli and P. aeruginosa or against fungus C. albicans. Eleven compounds were identified in B. purpurea leaf extract by GC-MS analysis. The composition of B. purpurea revealed the presence of lupeol, stigmasterol, lanosterol, ergosterol, beta-tocopherol, phytol, hexadeconic acids, hexadeconic acids methyl esters, octadecadienoic acids and octadecatrienoic acid. Stigmasterol and lupeol were the most abundant (34.48 and 15.63 %). Other phytosterols like lanosterol (4.15 %) and ergosterol (2.82 %) were also found to be present in this extract.
Abstract. Selection of attributes has been the current emerging research area for a long while and much work has been finished on. With the making of tremendous dataset and the resulting necessities for good machine learning systems, new issues emerge and ways to deal with feature selection are the area of research and in need. Preprocessed and generated datasets of multimedia event detection with numerical values is the input for our research. This paper recognizes the contribution of each attribute and combinational attributes towards the accuracy of the classifier model. The analysis is done using LIBSVM tool and different kernel modes using Support Vector Machine (SVM) classifier. After the analysis is done, we will get to know that which attribute yields the maximum accuracy in classifying the instances.
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