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Arumugam, P.
- Comparative Analysis of Antioxidant Activity and Phytochemical Potential of Cassia Absus linn., Cassia Auriculata linn. and Cassia Fistula linn
Abstract Views :324 |
PDF Views:195
Authors
Affiliations
1 Department of Botany Government Arts College for Men (Autonomous) Nandanam, Chennai 600 035, Tamil Nadu, IN
2 Department of Botany Government Arts college, Thiruvannamalai 606 603, Tamil Nadu, IN
3 Armats Biotech Pvt. Ltd., No.14, Mettu street, Maduvankarai, Guindy, Chennai 600 032, IN
1 Department of Botany Government Arts College for Men (Autonomous) Nandanam, Chennai 600 035, Tamil Nadu, IN
2 Department of Botany Government Arts college, Thiruvannamalai 606 603, Tamil Nadu, IN
3 Armats Biotech Pvt. Ltd., No.14, Mettu street, Maduvankarai, Guindy, Chennai 600 032, IN
Source
Indian Journal of Drugs and Diseases, Vol 3, No 1 (2014), Pagination: 298-304Abstract
Dried seeds of Cassia auriculata Linn., C.absus Linn. and C. fistula Linn. were collected from Javaathu hills, Tiruvannamalai District, Tamil Nadu, India for the comparative analysis of phyto-chemical potential with special reference to antioxidant activity. The active compounds were extracted with ethyl acetate, hexane and methanol. The methanol was found as suitable solvent and hence used for further extraction and analysis. The preliminary phytochemical screening of C. auriculata, C. absus C. fistula has revealed for the presence of alkaloids, phenolics and flavanoids in all the species. Absence of reducing sugars in C. absus, glucosides in C. auriculata and saponins in C. fistula were also observed. The quantitative determination of phenol and flavanoids was carried out in C. auriculata, C.absus and C. fistula and found that the total phenolic content as 0.18, 0.15, 0.11 and flavonoids 0.08, 0.092, 0.087% respectively. The antioxidant potential of the seed extracts recorded as much as 50% compared to α- tocopherol as standard. The Super oxide assay for the sample shows as 3% comparable to the standard α-tocopherol (5%). The Phosphomolybdenum assay and metal chelating activity indicate that the plant extract is a potential antioxidant. The details are presented in the present study.Keywords
Phytochemical Analysis, Antioxidant Activity, Radical Scavenging Activity, Cassia Auriculata, Cassia Absus and Cassia FistulaFull Text
- Stochastic Modelling Based Monthly Rainfall Prediction Using Seasonal Artificial Neural Networks
Abstract Views :171 |
PDF Views:3
Authors
S. M. Karthik
1,
P. Arumugam
1
Affiliations
1 Department of Statistics, Manonmaniam Sundaranar University, IN
1 Department of Statistics, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 2 (2017), Pagination: 1421-1426Abstract
India is an agrarian society where 13.7% of GDP and 50% of workforce are involved with agriculture. Rainfall plays a vital role in irrigating the land and replenishing the rivers and underground water sources. Therefore the study of rainfall is vital to the economic development and wellbeing of the nation. Accurate prediction of rainfall leads to better agricultural planning, flood prevention and control. The seasonal artificial neural networks can predict monthly rainfall by exploiting the cyclical nature of the weather system. It is dependent on historical time series data and therefore independent of changes in the fundamental models of climate known collectively as manmade climate change. This paper presents the seasonal artificial neural networks applied on the prediction of monthly rainfall. The amounts of rainfall in the twelve months of a year are fed to the neural networks to predict the next twelve months. The gradient descent method is used for training the neural networks. Four performance measures viz. MSE, RMSE, MAD and MAPE are used to assess the system. Experimental results indicate that monthly rainfall patterns can be predicted accurately by seasonal neural networks.Keywords
Seasonal Artificial Neural Networks, Annual Rainfall, Rainfall Prediction, Matlab, Stochastic Modelling.
References
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- An Empirical Analysis of Trading Strategy Based on Simple Moving Average Crossovers
Abstract Views :226 |
PDF Views:0
Authors
P. Arumugam
1,
R. Saranya
1
Affiliations
1 Department of Statistics, Manonmaniam Sundaranar University, IN
1 Department of Statistics, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Management Studies, Vol 3, No 1 (2017), Pagination: 423-426Abstract
Technical analysis is based on the assumption that the future price of a stock can be predicted from its history. Several technical trading systems exist for generating buy and sell signals in stock prices. Simple moving average crossovers are popular tools for trading. In this study, simple moving average crossovers with different periods are analyzed empirically on historical daily data of NIFTY 50 index. The profit and loss distribution in these trades are studied to identify profitable and stable crossover periods. The choppy price action known as whipsaws incur large number of small losses in the crossover based trading system. The phenomenon of rare trending price movements and its impact on the trading system are demonstrated.Keywords
Stock Trading, Simple Moving Average, SMA Crossover, NIFTY 50, National Stock Exchange.References
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