Nowadays steel balls wear is a major problem in mineral processing industries and forms a significant part of the grinding cost. Different factors are effective on balls wear. It is needed to find models which are capable to estimate wear rate from these factors. In this paper a back propagation neural network (BPNN) and multiple linear regression (MLR) method have been used to predict wear rat...

In the digitalization of industry and 4.0 environment, it is important to master accurate forecasting energy demand in order guarantee continuity production service as well improve reliability electrical system while promoting efficiency strategies industrial sector. This paper proposes machine learning models predict consumption an plant, which takes into account at-tributes that directly cons...

The implementation of corrosion detection in submarine pipelines is difficult, and a combined PCA-MLP prediction model proposed to improve the accuracy pipelines. Firstly, rate multiphase flow pipeline South China Sea simulated by De Waard 95 transient simulation software OLGA compared with actual rate; then, according data OLGA, principal component analysis (PCA) used reduce dimensionality fac...

Lithium-ion batteries are the current most promising device for electric vehicle applications. They have been widely used because of their advantageous features, such as high energy density, many cycles, and low self-discharge. One critical factors correct operation an is estimation battery charge state. In this sense, work presents a comparison state (SoC), tested in four different conduction ...

The IL-1β play a major role in inflammatory disorders and IL-1β production inhibitors can be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 46 pyridazine derivatives (inhibitors of IL-1β production) and their activities were investigated by Multiple Linear Regression (MLR) technique Stepwise Regression Method (ES-S...

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...

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 ...

PURPOSE We planned to optimize the effect of formulation variables on the percent drug entrapment (PDE) of the liposomes encapsulating leuprolide acetate by reverse phase evaporation method using Artificial neural network (ANN) and Multiple linear regression (MLR). METHOD Twenty seven formulations were prepared based on 3x3 factorial design. The volume of aqueous phase (X(1)), HSPC/DSPG [nega...

Introduction: Motion trajectory prediction (MTP) employs a time-series of band-pass filtered EEG potentials for reconstructing the three dimensional (3D) trajectory of limb movements with a multiple linear regression (mLR) block. While traditional multiclass classification methods use power values of mu (8-12Hz) and beta (12-30Hz) bands for limb movement based classification, recent MTP brain-c...