Browsing by Author "Nair, S."

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A novel approach for classification of normal/abnormal phonocardiogram recordings using temporal signal analysis and machine learning
(IEEE Computer Society help@computer.org, 2016) Vernekar, S.; Nair, S.; Vijayasenan, D.; Ranjan, R.
This paper discusses a novel approach used for classification of phonocardiogram (PCG) excerpts into normal and abnormal classes as a part of Physionet 2016 challenge [10]. The dataset used for the competition comprises of cardiac abnormalities such as mitral valve prolapse (MVP), benign murmurs, aortic diseases, coronary artery disease, miscellaneous pathological conditions etc. [3], We present the approach used for classification from a general machine learning application standpoint, giving details on feature extraction, type of classifiers used comparing their performances individually and in combination. We propose a technique which leverages previous research on feature extraction with a novel approach to modeling temporal dynamics of the signal using Markov chain analysis [7,9]. These newly introduced Markov features along with other statistical and frequency domain features, trained over an ensemble of artificial neural networks and gradient boosting trees, with bagging, gave us an accuracy of 82% on the validation dataset provided in the competition and was consistent with the test data with the best result of 78%. Â© 2016 CCAL.
A novel approach for classification of normal/abnormal phonocardiogram recordings using temporal signal analysis and machine learning
(2016) Vernekar, S.; Nair, S.; Vijaysenan, D.; Ranjan, R.
This paper discusses a novel approach used for classification of phonocardiogram (PCG) excerpts into normal and abnormal classes as a part of Physionet 2016 challenge [10]. The dataset used for the competition comprises of cardiac abnormalities such as mitral valve prolapse (MVP), benign murmurs, aortic diseases, coronary artery disease, miscellaneous pathological conditions etc. [3], We present the approach used for classification from a general machine learning application standpoint, giving details on feature extraction, type of classifiers used comparing their performances individually and in combination. We propose a technique which leverages previous research on feature extraction with a novel approach to modeling temporal dynamics of the signal using Markov chain analysis [7,9]. These newly introduced Markov features along with other statistical and frequency domain features, trained over an ensemble of artificial neural networks and gradient boosting trees, with bagging, gave us an accuracy of 82% on the validation dataset provided in the competition and was consistent with the test data with the best result of 78%. � 2016 CCAL.
Possible Room-Temperature Ferromagnetism in Self-Assembled Ensembles of Paramagnetic and Diamagnetic Molecular Semiconductors
(American Chemical Society service@acs.org, 2016) Dhara, B.; Tarafder, K.; Jha, P.K.; Panja, S.N.; Nair, S.; Oppeneer, P.M.; Ballav, N.
Owing to long spin-relaxation time and chemically customizable physical properties, molecule-based semiconductor materials like metal-phthalocyanines offer promising alternatives to conventional dilute magnetic semiconductors/oxides (DMSs/DMOs) to achieve room-temperature (RT) ferromagnetism. However, air-stable molecule-based materials exhibiting both semiconductivity and magnetic-order at RT have so far remained elusive. We present here the concept of supramolecular arrangement to accomplish possibly RT ferromagnetism. Specifically, we observe a clear hysteresis-loop (Hc ? 120 Oe) at 300 K in the magnetization versus field (M-H) plot of the self-assembled ensembles of diamagnetic Zn-phthalocyanine having peripheral F atoms (ZnFPc; S = 0) and paramagnetic Fe-phthalocyanine having peripehral H atoms (FePc; S = 1). Tauc plot of the self-assembled FePc···ZnFPc ensembles showed an optical band gap of ?1.05 eV and temperature-dependent current-voltage (I-V) studies suggest semiconducting characteristics in the material. Using DFT+U quantum-chemical calculations, we reveal the origin of such unusual ferromagnetic exchange-interaction in the supramolecular FePc···ZnFPc system. © 2016 American Chemical Society.