In this study, various machine learning algorithms, including the minimax probability regression (MPMR), functional network (FN), convolutional neural (CNN), recurrent (RNN), and group method of data handling (GMDH) models, are proposed for estimation seismic bearing capacity factor (Nc) strip footings on sloping ground under events. To train test model, a total 1296 samples were numerically ob...

In the oil and gas industry, energy prediction can help the distributor and customer to forecast the outgoing and incoming gas through the pipeline. It will also help to eliminate any uncertainties in gas metering for billing purposes. The objective of this paper is to develop Neural Network Model for energy consumption and analyze the performance model. This paper provides a comprehensive revi...

Long-term measurements of CO(2) flux can be obtained using the eddy covariance technique, but these datasets are affected by gaps which hinder the estimation of robust long-term means and annual ecosystem exchanges. We compare results obtained using three gap-fill techniques: multiple regression (MR), multiple imputation (MI), and artificial neural networks (ANNs), applied to a one-year dataset...

Purpose The purpose of this paper is to propose a semiparametric estimator for the tail index Pareto-type random truncated data that improves existing ones in terms mean square error. Moreover, we establish its consistency and asymptotic normality. Design/methodology/approach To construct root squared error (RMSE)-reduced index, authors used underlying distribution function given by Wang (1989)...

Appendicular skeletal muscle mass (ASM) is a diagnostic criterion for sarcopenia. Bioelectrical impedance analysis (BIA) offers a bedside approach to measure ASM but the performance of BIA prediction equations (PE) varies with ethnicities and body composition. We aim to validate the performance of five PEs in estimating ASM against estimation by dual-energy X-ray absorptiometry (DXA). We recrui...

accurate prediction of municipal solid waste’s quality and quantity is crucial for designing and programming municipal solid waste management system. but predicting the amount of generated waste is difficult task because various parameters affect it and its fluctuation is high. in this research with application of feed forward artificial neural network, an appropriate model for predicting the...

In other words, find the probability density functions f(·) and g(·) in (2) corresponding to the model (1). (b) Simulate the model (1) to produce T = 100 measurements y1:T . Based on these measurements compute the optimal (in the sense that it minimizes the mean square error) estimate of xt | y1:t for t = 1, . . . , T . Implement a bootstrap particle filter and compare to the optimal estimates....

In other words, find the probability density functions f(·) and g(·) in (2) corresponding to the model (1). (b) Simulate the model (1) to produce T = 100 measurements y1:T . Based on these measurements compute the optimal (in the sense that it minimizes the mean square error) estimate of xt | y1:t for t = 1, . . . , T . Implement a bootstrap particle filter and compare to the optimal estimates....

In this paper we perform a study using the Maximum Likelihood Ensemble Filter, MLEF, developed at the Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University (CSU), and Florida State University (FSU), with CSU’s 2-dimensional shallow water equations model on the sphere. The aim of this study is to find the optimal number of ensemble members, with respect to the ro...