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- 저자 : S. Bhansali (검색결과 3 건)
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Dielectric relaxation in nanopillar NiFe-silicon structures in high magnetic fields
R. Vasic,J.S. Brooks,E. Jobiliong,S. Aravamudhan,K. Luongo,S. Bhansali 한국물리학회 2007 Current Applied Physics Vol.7 No.1
We explore the dielectric relaxation properties of NiFe nanowires in a nanoporous silicon template. Dielectric data of the NiFesil-icon structure show a strong relaxation resonance near 30 K. This system shows Arrhenius type of behavior in the temperature depen-in the dielectric spectrum related to multiple relaxation rates. A magnetic eld aects both the exponential prefactor in the Arrheniusformula and the activation energy. From this eld dependence we derive a simple exponential eld dependence for the prefactor andlinear eld approximation for the activation energy which describes the data. We nd a signicant angular dependence of the dielectricrelaxation spectrum for regular silicon and nanostructured silicon vs. magnetic eld direction, and describe a simple sum rule thatNiFesilicon shows a more complex, magnetic eld dependent relaxation spectrum.
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Dysarthric-Speech Detection Using Transfer Learning With Convolutional Neural Networks
S R Mani Sekhar,Gaurav Kashyap,Akshay Bhansali,Andrew Abishek A.,Kushan Singh 한국통신학회 2022 ICT Express Vol.8 No.1
Speech Dysarthria is a disorder in which speech muscles become weak, and it becomes difficult to articulate otherwise linguistically normal speech. This work is based on detection of speech dysarthria and how it can assist physicians, specialists, and doctors in its detection. The proposed work achieves higher accuracies on the TORGO dataset by using a transfer learning based convolutional neural network model (TL-CNN) and by converting the audio samples to Mel-spectrograms. The proposed work TL-CNN achieved better accuracy when compared with other machine learning models.
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Dipika Bansal,Ramya S. R. Nayakallu1,Kapil Gudala,Rajavikram Vyamasuni,Anil Bhansali 대한당뇨병학회 2015 Diabetes and Metabolism Journal Vol.39 No.4
Background: The aim of the study is to evaluate the concurrence between Framingham Risk score (FRS) and United Kingdom Prospective Diabetes Study (UKPDS) risk engine in identifying coronary heart disease (CHD) risk in newly detected diabetes mellitus patients and to explore the characteristics associated with the discrepancy between them. Methods: A cross-sectional study involving 489 subjects newly diagnosed with type 2 diabetes mellitus was conducted. Agreement between FRS and UKPDS in classifying patients as high risk was calculated using kappa statistic. Subjects with discrepant scores between two algorithms were identified and associated variables were determined. Results: The FRS identified 20.9% subjects (range, 17.5 to 24.7) as high-risk while UKPDS identified 21.75% (range, 18.3 to 25.5) as high-risk. Discrepancy was observed in 17.9% (range, 14.7 to 21.7) subjects. About 9.4% had high risk by UKPDS but not FRS, and 8.6% had high risk by FRS but not UKPDS. The best agreement was observed at high-risk threshold of 20% for both (κ=0.463). Analysis showed that subjects having high risk on FRS but not UKPDS were elderly females having raised systolic and diastolic blood pressure. Patients with high risk on UKPDS but not FRS were males and have high glycosylated hemoglobin. Conclusion: The FRS and UKPDS (threshold 20%) identified different populations as being at high risk, though the agreement between them was fairly good. The concurrence of a number of factors (e.g., male sex, low high density lipoprotein cholesterol, and smoking) in both algorithms should be regarded as increasing the CHD risk. However, longitudinal follow-up is required to form firm conclusions.
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