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Prakash, Indra
- Antifungal Activity of Methanolic and Ethanolic Leaf Extracts of Medicinal Plants
Abstract Views :242 |
PDF Views:1
Authors
Affiliations
1 Department of Molecular and Cellular Engineering, Jacob School of Biotechnology and Bioengineering, Sam Higginbottom Institute of Agriculture, Technology and Sciences, Allahabad (U.P.), IN
1 Department of Molecular and Cellular Engineering, Jacob School of Biotechnology and Bioengineering, Sam Higginbottom Institute of Agriculture, Technology and Sciences, Allahabad (U.P.), IN
Source
International Journal of Plant Protection, Vol 9, No 2 (2016), Pagination: 474-478Abstract
The following plants were screened for thew study viz., Ocimum sanctum (Tulsi), Withania somnifera (Ashwagandha) and Asparagus racemosus (Shatavari) which were traditionally used in India to treat various diseases. In evaluating antioxidant property and phytochemical analysis, all three plants were screened for antifungal activity. It was evaluated using Well diffusion method. The extracts were tested against fungus Aspergillus niger. Inhibition of fungal growth was investigated using PDA well diffusion method. The contents of total flavonoid compounds in crude methanolic and ethanolic extracts obtained from Ocimum sanctum, Withania somnifera and Asparagus racemosus leaves.Keywords
Antifungal, Methanolic Leaf Extracts, Ethanolic Leaf Extracts.
References
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- Morena, E., Mishra, R.P., Arshad, M. and Sami, A. (2000).Antibacterial properties of Rosa indica (L.) stem, leaves and flowers. Electronic J. Biol., 12(15): 4564-4573.
- Ogbulie, J.N., Ogueke, C.C. and Nwanebu, F.C. (2007).Advances ion Applied Science and Research. African J.Biotechnol., 16: 1549 – 1553.
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- Machine Learning Methods of Kernel Logistic Regression and Classification and Regression Trees for Landslide Susceptibility Assessment at Part of Himalayan Area, India
Abstract Views :185 |
PDF Views:0
Authors
Affiliations
1 Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Viet Nam
2 Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar - 382421, Gujarat, IN
1 Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Viet Nam
2 Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar - 382421, Gujarat, IN
Source
Indian Journal of Science and Technology, Vol 11, No 12 (2018), Pagination:Abstract
Objectives: To evaluate performance of machine learning methods for assessment of landslide susceptibility at Himalayan area, India. Methods/Statistical analysis: Machine learning methods namely Kernel Logistic Regression (KLR) and Classification and Regression Trees (CART) were applied and compared in this study. Landslide affecting parameters and 930 historical landslides were used for generating datasets. Receiver Operating Characteristic (ROC) curve and Statistical analysis methods were used for validation and comparison. Findings: Result analysis shows that both the KLR and CART models perform well for landslide susceptibility assessment but the KLR model (AUC = 0.894) outperforms the CART model (AUC = 0.842). Thus, both these methods can be considered as promising machine learning techniques for landslide susceptibility assessment; however, the KLR is better than the CART. Application/Improvements: Results of this study would be useful for susceptibility assessment and landslide hazard management in landslide prone areas.Keywords
Classification and Regression Trees (CART), Kernel Logistic Regression (KLR), Landslides, Machine LearningFull Text