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Venkatesh, K.
- Management of Male Infertility by Neutraceutical: A Review
Abstract Views :268 |
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Authors
Affiliations
1 BM College of Pharmaceutical Education and Research, Indore (MP), IN
2 Radharaman Institute of Pharmacy, Bhopal (M.P), IN
3 Department of Pharmaceutics, Vels College of Pharmacy Chennai, (T.N), IN
1 BM College of Pharmaceutical Education and Research, Indore (MP), IN
2 Radharaman Institute of Pharmacy, Bhopal (M.P), IN
3 Department of Pharmaceutics, Vels College of Pharmacy Chennai, (T.N), IN
Source
Research Journal of Pharmacology and Pharmacodynamics, Vol 3, No 1 (2011), Pagination: 10-14Abstract
Seminal oxidative stress in the male reproductive tract is known to result in peroxidative damage of the sperm plasma membrane and loss of its DNA integrity. Normally, a balance exists between concentrations of reactive oxygen species (ROS) and antioxidant scavenging systems. One of the rational strategies to counteract the oxidative stress is to increase the scavenging capacity of seminal plasma. Numerous studies have evaluated the efficacy of antioxidants in male infertility. In this review, the results of different studies conducted have been analyzed, and the evidence available to date is provided. We outline the role of nutritional and biochemical factors from the nutraceutical of anti oxidant class like lycopenes, selenium, folate, zinc, glutathiones, l-arginine, l-carnitine, coenzyme-Q, Vitamin E,Vitamin C,Vitamin B12 in reproduction and male infertility problem.Keywords
Nutraceutical, Male Infertility, SOS, ROS, Antioxidants.References
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- Sleep Stages Classification using Artificial Neural Network
Abstract Views :154 |
PDF Views:0
Authors
K. Venkatesh
1,
S. Geetha
2
Affiliations
1 Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai - 600100, Tamil Nadu, IN
2 Department of Biomedical Engineering, Bharath University, Chennai - 600073, Tamil Nadu, IN
1 Department of Biomedical Engineering, Jerusalem College of Engineering, Chennai - 600100, Tamil Nadu, IN
2 Department of Biomedical Engineering, Bharath University, Chennai - 600073, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 31 (2015), Pagination:Abstract
The usual method for sleep stages classification is visual inspection method by sleep specialist. It uses eight EEG channels (O1, O2, T3, T4, C3, C4, Fp1 and Fp2), EOG and also EMG for sleep analysis. This method consumes more time (hours) for sleep stages classification. Some brain disorders like narcolepsy (excessive day time sleepiness) requires real-time monitoring of sleep states which is not possible to using conventional techniques. Hence sleep stages classification is done using Artificial Neural Network (ANN). Feature parameters such as Minimum amplitude, maximum amplitude, mean, standard deviation (SD) and energy were extracted using Discrete Wavelet Transform (DWT). This features for training and also for testing ANN, results obtained with this technique is accurate and also less time consuming as compare to other techniques.Keywords
Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Feed Forward Neural Network (FNN), Electroencephalogram (EEG), Electrooculogram (EOG)- A Survey on Machine Learning Algorithms and Finding the Best Out there for the Considered seven Medical Data Sets Scenario
Abstract Views :155 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, IN
1 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 6 (2019), Pagination: 3059-3062Abstract
Today, researchers are focusing on many machine learning algorithms in general over the data’s available. Each and every algorithm will have certain characteristics and we need to test it on specific data set to say about its efficiency in particular. Each and every Algorithm efficiency will get varies according to the data set’s nature. Research based on medical science will be much useful to society in critical situations, so we are taking seven medical data sets scenario for our research work. A detailed survey over variety of machine learning algorithms like SVM, Naïve Bayes, Dession Tree, Random Forecast, K-Means Clustering, Partition Algorithm, Bayesian Algorithm, Hierarchical Algorithm, Missing Values, Low Variance, Principal Component Analysis, Rough Set Theory, etc, over the seven medical data sets scenario which is taken to study and the results will be made on the aspect, which algorithms are good for what kind of medical records.Keywords
Machine Learning Algorithms, SVM, Naïve Bayes, Dession Tree, Random Forecast, K-Means Clustering, Partition Algorithm, Bayesian Algorithm, Hierarchical Algorithm, Missing Values, Low Variance, Principal Component Analysis, Rough Set Theory, Seven Medical Data Sets Scenario.References
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