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10.1007/s13748-020-00229-9 http://scihub22266oqcxt.onion/10.1007/s13748-020-00229-9 C7787132Prescriptiveanalyticsforimpulsivebehaviourpreventionusingreal-timebiometrics.!7787132!C7787132     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       ä-/-ä 2021 ; 10 (2): 99-112 Nephropedia Template TP {{tp|p=C7787132|t=2021. Prescriptive analytics for impulsive behaviour prevention using real-time biometrics |pdf=|usr=}}{{C7787132}} gab.com Text Prescriptive analytics for impulsive behaviour prevention using real-time biometrics JO: ä-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=C7787132 #FOAvip The present biometric market segment has been captured by compact, lightweight sensors which are capable of reading the biometric fluctuations of a user in real-time. This biometric market segment has further facilitated rise of a new ecosystem of wearable devices helpful in tracking the real-time physiological data for Healthcare-related analysis. However, the devices in the smart-wearable ecosystem are limited to capturing and displaying the biometrics without any prescriptive analytics. This paper addresses this gap to analyse the human emotion space based on an individual?s state of mind over the past 60 min and employs Deep Learning and Bayesian prediction techniques to predict the possibility of an impulsive outburst within upcoming few minutes. A lightweight smart processing device mounted with sensors captures the biometrics of the user and calibrate the same to the mental state of the user on a scale of zero to hundred. The results reveal that the deep learning algorithm along with the Bayesian probability module can predict the future mood fluctuations of the user with lower error than the other contemporary models. The predicted mood fluctuations has matched with the actual mood changes of the experimental subject within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \pm 10 $$\end{document}±10 min of the predicted time index in 93% of the cases and within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \pm 5 $$\end{document}±5 min in 82% of the cases. Twit Text FOAVip Prescriptive analytics for impulsive behaviour prevention using real-time biometrics ????ä-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=C7787132 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=C7787132 #FOAvip English Wikipedia <ref>{{Cite pmid|C7787132}}</ref> Prescriptive analytics for impulsive behaviour prevention using real-time biometrics #MMPMIDC7787132 Raychaudhuri SJ; Manjunath S; Srinivasan CP; Swathi N; Sushma S; Nitin Bhushan KN; Narendra Babu C ä-/-ä 2021[]; 10 (2): 99-112 PMIDC7787132show ga The present biometric market segment has been captured by compact, lightweight sensors which are capable of reading the biometric fluctuations of a user in real-time. This biometric market segment has further facilitated rise of a new ecosystem of wearable devices helpful in tracking the real-time physiological data for Healthcare-related analysis. However, the devices in the smart-wearable ecosystem are limited to capturing and displaying the biometrics without any prescriptive analytics. This paper addresses this gap to analyse the human emotion space based on an individual?s state of mind over the past 60 min and employs Deep Learning and Bayesian prediction techniques to predict the possibility of an impulsive outburst within upcoming few minutes. A lightweight smart processing device mounted with sensors captures the biometrics of the user and calibrate the same to the mental state of the user on a scale of zero to hundred. The results reveal that the deep learning algorithm along with the Bayesian probability module can predict the future mood fluctuations of the user with lower error than the other contemporary models. The predicted mood fluctuations has matched with the actual mood changes of the experimental subject within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \pm 10 $$\end{document}±10 min of the predicted time index in 93% of the cases and within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \pm 5 $$\end{document}±5 min in 82% of the cases. äPrescriptive analytics for impulsive behaviour prevention using real-t(epmc).pdf DeepDyve Pubget Overpricing