39 research outputs found
Topics in statistical finance
No full textThis thesis is divided into three parts. The first part investigates the presence of long term dependence in stock price data via a permutation test based on the correlation structure of the underlying stock prices. These tests reveal the short term nature of stock price dependence structure. The second part extends
Ramprasath and Singh(2007)'s `statistical options' to define a group of American type options based on robust estimators of location. The payoff functions of these path dependent options are based on a new set of stochastic processes which are defined using various robust estimators of location. The asymptotic distributional behavior of these new processes is ascertained which in turn is used in pricing
the options. Markov Chain Monte Carlo (MCMC) methods were used to compute the prices of the statistical options. The third part explores a stock price model parameter estimation problem and interprets a growth rate parameter.Ph.D.Includes bibliographical references (p. 81-83)
Giant sized rays landed at Cochin Fisheries Harbour
No full textOn 4th March 2017, three huge rays - two Mobula
tarpacana and one Manta birostris were landed at
Cochin Fisheries Harbour. They were caught in long
lines, which were operated for skipjack tuna. These
rays caught off Ratnagiri coast at a depth of 500m
weighed around 400 kg each. Of these, Mobula
tarpacana locally called 'Kakkathirandi' measured
2.4 m in disc width (DW)
Not Available
No full textNot AvailableOn 4th March 2017, three huge rays - two Mobula
tarpacana and one Manta birostris were landed at
Cochin Fisheries Harbour. They were caught in long
lines, which were operated for skipjack tuna. These
rays caught off Ratnagiri coast at a depth of 500m
weighed around 400 kg each. Of these, Mobula
tarpacana locally called 'Kakkathirandi' measured
2.4 m in disc width (DW).Not Availabl
Four Quadrant Operation of Direct Torque Control-SVPWM based three phase Induction Motor Drive in MATLAB/SIMULINK environment
No full text
Four quadrant comparative evaluation of classical and space vector PWM-directtorque control of a VSI fed three phase induction motor drive in MATLAB/SIMULINK environment
No full textCommentary on a trial comparing krill oil versus fish oil
No full textConsiderable interest exists presently in comparing the performance of krill oil (KO) and fish oil (FO) supplements. Ramprasath et al. (Lipids Health Dis 12:178, 2013) have recently compared use of KO and FO in a trial with healthy individuals to examine which oil is more effective in increasing n-3 PUFA, decreasing the n-6:n-3 ratio and improving the omega-3 index. The authors concluded that KO was more effective than FO for all three criteria. However, careful examination of the fatty acid profiles of the oils used showed that the FO used was not a typical FO; it contained linoleic acid as the dominant fatty acid (32%) and an n-6:n-3 ratio of >1. Due to the fatty acid profile being non-representative of typically commercially marketed FO, the conclusions presented by Ramrasath et al. (Lipids Health Dis 12:178, 2013) are not justified and misleading. Considerable care is needed in ensuring that such comparative trials do not use inappropriate ingredients
Knowledge attitude and practice towards prevention and early detection of chronic kidney disease among high risk patients
No full textBackground: It is well recognized that chronic kidney disease (CKD), if left, untreated would slowly progress to end-stage renal disease (ESRD)., A targeted approach is to enhance the knowledge of CKD among the public, especially in high risk population, and encourage them to practice a healthy attitude and practice that may help in early detection and thereby better management of CKD. Such a study to assess the baseline data has not been done in India. Aims and Objectives: To inculcate the knowledge attitude and practice towards prevention and early detection of CKD among high risk patients attending a tertiary care centre. This Observational study was carried out on all patients with diabetes and/or hypertension attending General medicine out-patient or in-patient in a given period of time. Patients more than 18 years of age with diabetes and/or hypertension were included while patients with chronic kidney disease were excluded. Methodology: A standard questionnaire obtained from an author of similar study is given to patients fulfilling the inclusion criteria. Is this National Kidney Foundation's Kidney Disease Outcome Quality Initiative (KDOQI)? If so mention it. 
White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification.
No full textUnmanned aerial vehicles (UAVs) become a promising enabler for the next generation of wireless networks with the tremendous growth in electronics and communications. The application of UAV communications comprises messages relying on coverage extension for transmission networks after disasters, Internet of Things (IoT) devices, and dispatching distress messages from the device positioned within the coverage hole to the emergency centre. But there are some problems in enhancing UAV clustering and scene classification using deep learning approaches for enhancing performance. This article presents a new White Shark Optimizer with Optimal Deep Learning based Effective Unmanned Aerial Vehicles Communication and Scene Classification (WSOODL-UAVCSC) technique. UAV clustering and scene categorization present many deep learning challenges in disaster management: scene understanding complexity, data variability and abundance, visual data feature extraction, nonlinear and high-dimensional data, adaptability and generalization, real-time decision making, UAV clustering optimization, sparse and incomplete data. the need to handle complex, high-dimensional data, adapt to changing environments, and make quick, correct decisions in critical situations drives deep learning in UAV clustering and scene categorization. The purpose of the WSOODL-UAVCSC technique is to cluster the UAVs for effective communication and scene classification. The WSO algorithm is utilized for the optimization of the UAV clustering process and enables to accomplish effective communication and interaction in the network. With dynamic adjustment of the clustering, the WSO algorithm improves the performance and robustness of the UAV system. For the scene classification process, the WSOODL-UAVCSC technique involves capsule network (CapsNet) feature extraction, marine predators algorithm (MPA) based hyperparameter tuning, and echo state network (ESN) classification. A wide-ranging simulation analysis was conducted to validate the enriched performance of the WSOODL-UAVCSC approach. Extensive result analysis pointed out the enhanced performance of the WSOODL-UAVCSC method over other existing techniques. The WSOODL-UAVCSC method achieved an accuracy of 99.12%, precision of 97.45%, recall of 98.90%, and F1-score of 98.10% when compared to other existing techniques
