This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories analysis: Price, Volume, Turnover. Various error metrics like Mean Absolute Error (MAE), Squared (MSE), Percentage (MAPE), Root-Mean-Squared-Error...

In order to study the application of Cobb-Douglas production function on optimization safety inputs and further reduce accident losses, two input structures a coal mine enterprise were constructed using literature, weight each indicator was determined by entropy method (EWM) analytical hierarchy process (AHP). The used calculate loss structure, obtained multiple regression analysis. optimal con...

XGBoost algorithm and Lasso regression compare r-square, Mean Square Error (MSE), Root MSE, RMSLE values. The should be efficient enough to produce the exact fare amount of trip before starts. sample size for implementing this work was N=10 each groups considered. It iterated 20 times accurate prediction cab price with G power in 80% threshold 0.05%, CI 95% mean standard deviation. calculation ...

A method of training multilayer perceptrons (MLPs) to reach a global or nearly global minimum of the standard mean squared error (MSE) criterion is proposed. It has been found that the region in the weight space that does not have a local minimum of the normalized riskaverting error (NRAE) criterion expands strictly to the entire weight space as the risk-sensitivity index increases to infinity....

We are used Bayes estimators for unknown scale parameter when shape Parameter is known of Erlang distribution. Assuming different informative priors parameter. derived The posterior density with mean and variance using which the inverse exponential distribution, chi-square Gamma standard Levy distribution as prior. And we based on general entropy loss function (GELF) Simulation method to obtain...