Sediment load in fluvial systems is one of the critical factors shaping river geomorphological and hydraulic characteristics. A detailed understanding total sediment (TSL) required for protection physical, environmental, ecological functions rivers. This study develops a robust methodological approach based on multiple linear regression (MLR) support vector (SVR) models modified by principal co...

An important problem to be solved in modeling head-related impulse responses (HRIRs) is how to individualize HRIRs so that they are suitable for a listener. We modeled the entire magnitude head-related transfer functions (HRTFs), in frequency domain, for sound sources on horizontal plane of 37 subjects using principal components analysis (PCA). The individual magnitude HRTFs could be modeled ad...

This research aimed to propose an online system based on multispectral images for the real-time estimation of moisture content (MC) sugarcane bagasse. The consisted a conveyor belt, four halogen bulbs, and camera. MC models were developed using machine learning algorithms, i.e., multiple linear regression (MLR), principal component (PCR), artificial neural network (ANN), PCA-ANN, Gaussian proce...

Evaluating the impact of different meteorological conditions on apple yield and predicting future in Yantai City is essential for production. Furthermore, it provides a scientific basis increase yield. In this study, first, grey relational analysis (GRA) was used to determine quantitative relationship between factors which defined as affected only by conditions. Then, comprehensive extracted pr...

In this paper Principal Components (PC) and Multiple Linear Regression (MLR) Technique were used for development of pre-harvest model rice yield in the Navsari district south Gujarat. The weather indices developed utilized forecast models. data parameters from 1990 to 2012 utilized. cross validation confirmed using years 2013 2016. It was observed that value Adj. R2 varied 89 96. appropriate se...

We applied cluster analysis and principal component analysis (PCA) with multivariate linear regression (MLR) to apportion sources of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Huangpu River in Shanghai, China, based on the measured PAH concentrations of 32 samples collected at eight sites in four seasons in 2006. The results indicate that petrogenic and pyrogenic source...

Water quality degradation in river systems has caused great concerns all over the world. Identifying the spatial distribution and sources of water pollutants is the very first step for efficient water quality management. A set of water samples collected bimonthly at 12 monitoring sites in 2009 and 2010 were analyzed to determine the spatial distribution of critical parameters and to apportion t...

Individualized head-related impulse responses (HRIRs) to perfectly suit a particular listener remains an open problem in the area of HRIRs modeling. We have modeled the whole range of magnitude of head-related transfer functions (HRTFs) in frequency domain via principal components analysis (PCA), where 37 persons were subjected to sound sources on median plane. We found that a linear combinatio...

Quantitative Structure-Activity Relationship (QSAR) study on 22 1H-indole-3-acetic acid derivatives with auxin activity was performed by means of Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Partial Least Squares Regression (PLS) and Multiple Linear Regression (MLR). Molecular geometry of the auxins was optimized at MMFF94 and ab initio B3LYP 6-31G** levels. Modeling...

In this paper, we report the relationship between anti-MERS-CoV activities of HKU4 derived peptides for some peptidomimetic compounds and various descriptors using quantitative structure activity relationships (QSAR) methods. The used were computed ChemSketch, Marvin Sketch ChemOffice software. principal components analysis (PCA) multiple linear regression (MLR) methods to propose a model with ...