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Articles by M. Gunasekaran
Total Records ( 3 ) for M. Gunasekaran
  R. Yang , N. Mohan , C. Muthukumar , N. Thajuddin and M. Gunasekaran
  The influence of cultural conditions that affect GPX production in Candida albicans grown in Lee's medium was investigated. Optimum temperature and pH for GPX activity were 25°C and 7.2, respectively. Substrate specificity for C. albicans. Glutathion peroxidase was in the order of cumene hydroperoxide>t-butyl hydroperoxide> hydrogen peroxide>benzoyl peroxide. Aeration as well as large head space volume enhanced the growth of C. albicans and GPX production. Arabinose and ammonium sulphate significantly increased the GPX synthesis. Among nitrogen sources, polypeptone enhanced both the growth and GPX synthesis. Various cellular activities are regulated by the level of GSH. Therefore, the level of GPX might be used as one of the criteria in developing new drugs against Candida albicans.
  Binn Jatta , M. Gunasekaran and N. Mohan
  We studied the Lipase (LP) an enzyme which hydrolyses the ester bonds of triacylglycerols to yield glycerol and fatty acids in an opportunistic yeast human pathogen, Candida albicans (CA). The yeast was grown in Sabouraud dextrose broth and Lee synthetic medium at 25°C on a rotary shaker (100 rpm). At 24, 48 and 72 h of inoculation, cells were separated from the media and the intracellular and extracellular LP were measured from cell free homogenate and culture media, respectively. Lipase activity was determined by the rate of hydrolysis of olive oil emulsion by potentiometric titration. The influence of various factors such as growth, pH, temperature and media on the production of extra and intracellular Lipases (LP) has been studied. All the experiments were conducted at least twice and the analyses were carried out in triplicates. Candida albicans produced both extra and intracellular lipases in both the tested media. Although, LP was produced throughout the growth phase, maximum enzyme activity was detected at 24 h of growth. Optimum pH and temperature for the LP activity were 7 and 37°C, respectively.
  M. Gunasekaran and K.S. Ramaswami
  Stock market forecasting provides challenging and interesting task to both investors and academic researchers because trading decision at an appropriate time makes more profit for investors. In present study, a new approach has been proposed to integrate Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Immune Algorithm (AIA) for predicting the future index value of National Stock Exchange (NSE) of India. In order to make an efficient forecasting model, ANFIS is employed to optimize decision-making process and an efficient artificial immune algorithm is adopted to adjust membership function parameters of Fuzzy Inference System (FIS). The proposed system was simulated using daily closing value of NSE Nifty data and well-known technical indicators as input data values and output is the predicted future index value of NSE Nifty. Simulation results of our fusion model have been compared with other soft computing models and actual NSE Nifty data as benchmark. The experimental results showed that the proposed forecasting model yielded significantly higher forecasting accuracy values than other forecasting models.
 
 
 
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