Improved Linear Factor based Grasshopper Optimization Algorithm with Ensemble Learning for Covid-19 Forecasting

Recently the COVID’19 is extensively increasing around the world with many challenges for researchers. Rigorous respiratory disease corona virus 2 show aggression to many parts of COVID’19 affected patients, together with brain and lungs. The changeableness of Corona virus with likely to infect Central Nervous System emphasize the necessity for technological development to identify, handle, and take care of brain damages in COVID’19 patients. An exact short-term predicting the quantity of newly infected and cured cases is vital for resource optimization to stop or reduce the growth of infection. The previous system designed a Linear Decreasing Inertia Weight based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network (LDIWCSO-HBDCNN) approach for COVID-19 forecasting. However, the ensemble learning is required to improve the prediction outcome via integrating many approaches. This approach allows the production of better predictive performance compared to a single model. For solving this problem, the proposed system designed an Improved Linear Factor based Grasshopper Optimization Algorithm with Ensemble Learning (ILFGOA with EL) for covid-19 forecasting. Initially, the COVID-19 forecasting dataset is taken as an input. With the help of min-max approach, data normalization is done. Then the optimal features are selected by using Improved Linear Factor based Grasshopper Optimization Algorithm (ILFGOA) algorithm to improve the prediction accuracy. Based on the selected features, Ensemble Learning (EL) which includes Hyperparameter based Convolutional Neural Network (HCNN) is utilized to identify infected and demise cases across india for a period of time. The outcome of analysis shows that the introduced method attains better execution against previous system with regard to error rate, accuracy, precision, recall and f-measure.

Multi-variate factor separation of numerical simulations

<p>Factor separation is widely used in the analysis of numerical simulations.  It allows changes in properties of a system to be attributed to changes in multiple variables associated with that system.  There are many possible factor separation methods; here we discuss three previously-proposed methods that have been applied in the field of climate modelling: the linear factor separation, the Stein and Alpert (1993) factor separation, and the Lunt et al (2012) factor separation.  We show that, when more than two variables are being considered, none of these three methods possess all four properties of 'uniqueness', 'symmetry', 'completeness', and 'purity'.  Here, we extend each of these methods so that they do possess these properties for any number of variables, resulting in three factor separation methods -- the 'linear-sum' , the 'shared-interaction', and the 'scaled-total'.  We show that the linear-sum method and the shared-interaction method reduce to be identical in the case of four or fewer variables, and we conjecture that this holds for any number of variables.  We present the results of the factor separations in the context of studies that used the previously-proposed methods.  This reveals that only the linear-sum/shared-interaction factor separation method possesses a fifth property -- `boundedness', and as such we recommend the use of this method in applications for which these properties are desirable.   The work described here is in review in Geoscientific Model Development - see https://gmd.copernicus.org/preprints/gmd-2020-69 .</p>

Bayesian Selection of Asset Pricing Factors Using Individual Stocks*

We apply Bayesian variable selection to investigate linear factor asset pricing models for a large set of candidate factors identified in the literature. We extract model and factor posterior probabilities from thousands of individual stocks via Markov Chain Monte Carlo estimation together with the exact distribution of pricing statistics. Our results show that only a small number of factors are relevant and, except for the market and size factors, these are not the factors in widely used linear factor models such as Fama and French (2015, Journal of Financial Economics 116, 1–22) or Hou et al. (2015, The Review of Financial Studies 28, 650–705). Moreover, many different linear factor models achieve similar empirical performance, suggesting that the search for a single linear factor model is unlikely to yield a definitive answer.

Generalized Linear Factor Score Regression: A Comparison of Four Methods

Factor score regression has recently received growing interest as an alternative for structural equation modeling. However, many applications are left without guidance because of the focus on normally distributed outcomes in the literature. We perform a simulation study to examine how a selection of factor scoring methods compare when estimating regression coefficients in generalized linear factor score regression. The current study evaluates the regression method and the correlation-preserving method as well as two sum score methods in ordinary, logistic, and Poisson factor score regression. Our results show that scoring method performance can differ notably across the considered regression models. In addition, the results indicate that the choice of scoring method can substantially influence research conclusions. The regression method generally performs the best in terms of coefficient and standard error bias, accuracy, and empirical Type I error rates. Moreover, the regression method and the correlation-preserving method mostly outperform the sum score methods.

Measuring Global Cognition in a Pooled Sample using Modified Non-Linear Factor Analysis

Currently, studies of cognition and cognitive decline in the United States are limited by the use of samples that only provide data for respondents during one stage of the adult life course. By using an Integrative Data Analysis (IDA) framework, it is possible to pool multiple national representative samples together in order to create a unified dataset that includes respondents over the entire adult life course. This study applies an IDA framework to two independent public health datasets to create a commensurate measure of cognition using Modified Non-Linear Factor Analysis (MNLFA). The overall goal is to demonstrate the process of using MNLFA for the study of cognition in a pooled dataset.