In-Vitro Anti-Fungal Activity and Phytochemical Screening of Stem Bark Extracts from Ventilago denticulata

The objective of the present study was to assess the antifungal activity of pet. Ether extract, acetone extract, ethyl acetate, and ethanol bark extract of Ventilago denticulata (VD).The material was dried in shade made to a coarse powder and weighted quantity of the powder   (1000 g) was subjected to hot percolation in a soxhlet apparatus using petroleum ether, ethyl acetate, acetone and ethanol, at a temperature range of 40-800C. Phytochemical tests were done in presence of phytoconstituents like glycosides, alkaloids, tannins, steroids, flavonoids. The anti-fungal activity was carried out by using cup method using Sabraud’s agar as medium. Plates were incubated at 250C for 42hr and later observed for zones of inhibition. The effect of the extracts on fungal isolates was compared with Griseofluvin at a concentration of 10 mg/ml. The Ethyl acetate extract at low as well as high doses gives antifungal effect. Pet-ether extract, acetone extract and ethanolic extract did not produce any antifungal effect at both doses. Ethyl acetate extract shows zone of inhibition at low dose (T1 10 mg/ml) 10 mm and at high dose (T2  20 mg/ml) 16 mm. Keyword: Ventilago denticulata, Anti- fungal, Griseofluvin

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person