Machine Learning Based Prediction Models for the Percentage Deaths Due to COVID-19

Worldwide COVID-19 pandemic is currently affecting all countries and led to loss of human life. A lot of scientific research are conducted in different areas to improve the future response. The purpose of the project is to use Machine learning (ML) techniques in predicting COVID-19 deaths which will enhance the hospitals response. This paper contributes by developing models that can predict COVID-19 deaths based on three factors: total number of elderly patients (greater than 65 years), diabetic patients, and smoking patients. Gaussian Process Regression (GPR), Support Vector Regression (SVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Artificial Neural Network-nonlinear autoregressive network with exogenous inputs (ANN-NARX) approaches are used to build the predictive models. All models are trained and tested using trusted data reported by the World Health Organization (WHO) in various countries. The developed models revealed very good results with excellent prediction rate and performance, especially GPR, which has the best performance. Also, it showed that region-based predictive models are more suitable than a single general model. The GPR predictive model showed the best performance compared to other models. © 2021 IEEE.

Applicability of double Channel Technique in AlGaN/GaN HEMT for future biosensing applications

Sensing COVID-19, GOx (glucose oxidase enzyme) in exhaled breath condensate/saliva, bio-molecules like KIM (Kidney Injury Molecule) in human body and pH value in human body fluids have gained huge attention in the present scenario as well as in the past decade. Hence, for the first time, double channel technique in AlGaN/GaN High Electron Mobility Transistor (HEMT) is proposed and its applicability is demonstrated by biosensing application. Simulation using SILVACO Technology Computer Aided Design (TCAD) based on numerical solid state models has been extensively used for investigation and analysis. The sensitivity of double channel device is compared with single channel device and its performance is evaluated in terms of the transconductance. Unlike the single channel device, double channel device exhibited wide range of transconductance with respect to gate bias. The device recorded a sensitivity of 136%, which is 74% higher than the sensitivity of single channel device. Hence, it is inferred that the sensitivity enhances with the use of multiple channels and could be increased by increasing the number of channels. The results of this research show that the proposed sensor stands a promising candidate for future biosensing applications that demand high detection limits. © 2021

GPR and ANN based Prediction Models for COVID-19 Death Cases

COVID-19 pandemic now affects the entire world and has a major effect on the global economy. A number of medical researchers are currently working in various fields to tackle this pandemic and its circumstances. This paper aims of developing a model that can estimate the number of deaths in the affected cases based on the documented number of older (above 65 years of age), diabetic and smoking cases. The Gaussian Process Regression (GPR) approach has been used to build the model and its performance was compared with a corresponding Artificial Neural Network (ANN) model. The model was applied to reliable data published by the World Health Organization (WHO) for different countries in North America, Europe and the Gulf region. The model provided impressive results with an excellent prediction of data from all the countries under investigation. The model may be useful in estimating the number of deaths due to any arbitrary number of inputs. It would also help to prepare effective measures to minimize the number of deaths. © 2020 IEEE.