Predicted Oil Recovery Scaling-Law Using Stochastic Gradient Boosting Regression Model

In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essential for determining the behavior of actual reservoirs.The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data.This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity,which dynamics of the process.One of the practical machine learning(ML)techniques for regression/classification problems is gradient boosting(GB)regression.The GB produces a prediction model as an ensemble of weak prediction models that can be done at each iteration by matching a least-squares base-learner with the current pseudoresiduals.Using a randomization process increases the execution speed and accuracy of GB.Hence in this study,we developed a stochastic regression model of gradient boosting(SGB)to forecast oil recovery.Different nondimensional time-scales have been used to generate data to be used with machine learning techniques.The SGB method has been found to be the best machine learning technique for predicting the non-dimensional time-scale,which depends on oil/rock properties.

Stochastic Gradient Boosting Model for Twitter Spam Detection

In today’s world of connectivity there is a huge amount of data than we could imagine.The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time.These social networks give the complete independence to the user to post the data either political,commercial or entertainment value.Some data may be sensitive and have a greater impact on the society as a result.The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter.Due to the large user base and its openness there is a huge possibility to spread spam messages in this network.Spam detection is a technique to identify and mark data as a false data value.There are lot of machine learning approaches proposed to detect spam in social networks.The efficiency of any spam detection algorithm is determined by its cost factor and accuracy.Aiming to improve the detection of spam in the social networks this study proposes using statistical based features that are modelled through the supervised boosting approach called Stochastic gradient boosting to evaluate the twitter data sets in the English language.The performance of the proposed model is evaluated using simulation results.

MODELING OF FREE JUMPS DOWNSTREAM SYMMETRIC AND ASYMMETRIC EXPANSIONS:THEORITICAL ANALYSIS AND METHOD OF STOCHASTIC GRADIENT BOOSTING

The general computational approach of Stochastic Gradient Boosting （SGB） is seen as one of the most powerful methods in predictive data mining. Its applications include regression analysis, classification problems with/without continuous categorical predictors. The present theoretical and experimental study aims to model the free hydraulic jump created through rectangular Channels Downstream （DS） symmetric and asymmetric expansions using SGB. A theoretical model for prediction of the depth ratio of jumps is developed using the governing flow equations. At the same time, statistical models using linear regression are also developed. Three different parameters of the hydraulic jump are investigated experimentally using modified angled-guide walls. The results from the modified SGB model indicate a significant improvement on the original models. The present study shows the possibility of applying the modified SGB method in engineering designs and other practical applications.