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- 저자 : P. Swapna (검색결과 4 건)
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V. V. Sathibabu Uddandrao,Brahmanaidu Parim,P. R. Nivedha,K. Swapna,P. Rameshreddy,S. Vadivukkarasi,Mustapha Shabana Begum,Saravanan Ganapathy 경희대학교 융합한의과학연구소 2019 Oriental Pharmacy and Experimental Medicine Vol.19 No.3
Pomegranate (Punica granatum Linn), has been widely used in India’s ancient Ayurveda system of traditional medicine which is commonly portrayed as a constituent in remedies. The present study was aimed to investigate the anticancer activity of the aqueous extract of P. granatum fruits (PGET) against ehrlich-ascites-carcinoma (EAC)-bearing Swiss albino mice. The PGET were administered to EAC bearing mice at the doses of 100, 200 and 400 mg/kg body weight (BW) intraperitonially for 14 successive days and 24 h of last dose and 18 h of fasting, the mice were sacrifced and the anticancer efect of PGET was appraised by evaluating tumor volume, viable, nonviable tumor cell count, tumor weight, hematological, biochemical parameters and histopathological changes of EAC mice. PGET showed momentous decrease in tumor volume, viable cell count, tumor weight and elevated the life span of EAC bearing mice. Hematological profle such as RBC, hemoglobin and lymphocyte count reverted to normal level in PGET treated mice. The extract at 400 mg/kg BW showed a noteworthy reduction in the level of lipid peroxidation and considerably increased the levels of antioxidant enzymes in the liver and observed signifcant restoration of histopathological changes in experimental animals. Hence, the current study revealed that the PGET was efcient in inhibiting the tumor growth in ascitic models and that is comparable to 5-Fluorouracil. The anticancer properties of P. granatum could be due to the presence of the various phytoconstituents in it.
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COMMON FIXED POINT FOR RECIPROCALLY CONTINUOUS AND WEAKLY COMPATIBLE MAPS IN A G-METRIC SPACE
P. Swapna,T. Phaneendra,M. N. Rajashekar 경남대학교 기초과학연구소 2022 Nonlinear Functional Analysis and Applications Vol.27 No.3
A brief comparative survey of some generalizations of a metric space with three dimensional metric structures and different forms of the triangle inequality is done along with their topological properties. Then a common fixed point is obtained for reciprocally continuous and compatible self-maps in a G-metric space. Further, a common fixed point theorem is proved for a pair of weakly compatible self-maps on a G-metric space with the common limit range property.
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GENERALIZED FIXED POINT THEOREMS IN A b-METRIC SPACE
P. Swapna 경남대학교 기초과학연구소 2020 Nonlinear Functional Analysis and Applications Vol.25 No.2
A brief comparison of various contractive conditions in a b-metric space is made,and two generalized fixed point theorems are established. One for a Nesic type contraction,and the other involving a generalized class of auxiliary functions. Also, contractive fixedpoints in a b-metric space are obtained for some contractive conditions.
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Diabetes detection using deep learning algorithms
Swapna G.,Vinayakumar R.,Soman K.P. 한국통신학회 2018 ICT Express Vol.4 No.4
Diabetes is a metabolic disease affecting a multitude of people worldwide. Its incidence rates are increasing alarmingly every year. If untreated, diabetes-related complications in many vital organs of the body may turn fatal. Early detection of diabetes is very important for timely treatment which can stop the disease progressing to such complications. RR-interval signals known as heart rate variability (HRV) signals (derived from electrocardiogram (ECG) signals) can be effectively used for the non-invasive detection of diabetes. This research paper presents a methodology for classification of diabetic and normal HRV signals using deep learning architectures. We employ long short-term memory (LSTM), convolutional neural network (CNN) and its combinations for extracting complex temporal dynamic features of the input HRV data. These features are passed into support vector machine (SVM) for classification. We have obtained the performance improvement of 0.03% and 0.06% in CNN and CNN-LSTM architecture respectively compared to our earlier work without using SVM. The classification system proposed can help the clinicians to diagnose diabetes using ECG signals with a very high accuracy of 95.7%.
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