Physical Action Categorization Pertaining to Certain Neurological Disorders Using Machine Learning-Based Signal Analysis

Khan, Asad Mansoor; Khawaja, Sajid Gul; Akram, Muhammad Usman; Khan, Ali Saeed

doi:10.1007/978-3-030-97845-7_2

Cited by 1 publication

(1 citation statement)

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“…In this study, the min-max normalization method is used, where the parameter values are scaled within the range of 0 to 1, and this is a commonly used method for normalizing datasets (Ekenel & Stiefelhagen, 2006;A. Jain et al, 2005;Khan et al, 2020;Wu et al, 2005).…”

Section: Resultsmentioning

confidence: 99%

Sensitivity Study of The Effect Polymer Flooding Parameters to Improve Oil Recovery Using X-Gradient Boosting Algorithm

Erfando,

Khariszma

2023

JAETS

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Implementation of waterflooding sometimes cannot increase oil recovery effectively and requires additional methods to increase oil recovery. Polymer flooding is a common chemical EOR method that has been implemented in the last few decades and provides good effectiveness in increasing oil recovery and can reduce the amount of injection fluid injected into the reservoir. Seeing the success of polymer flooding in increasing oil recovery, it is necessary to know the parameters that influence the success of polymer flooding so that it can be evaluated and taken into consideration in creating a new scheme to increase oil recovery with polymer flooding. The parameters tested in this study include Injection Rate, Injection Time, Injection Pressure, Adsorption, Inaccessible Pore Volume, Residual Resistance Factor. This research uses the X-Gardient Boosting Algorithm to look at the most influential parameters in polymer flooding. The parameters that most influence the performance of polymer flooding on the value of oil recovery with the importance level of each parameter in this study are injection time of 0.452632, injection rate of 0.430075, injection pressure of 0.064662, Adsorption of 0.025564, RRF of 0.021053, IPV of 0.006014 and produce accurate predictive modeling using x-gradient boosting where with 3 variations of the comparison ratio of training and testing data obtained at a ratio of 0.7 : 0.3 obtained an R2 train of 0.9886 and an R2 test of 0.9645, a ratio of 0.8 : 0.2 obtained an R2 train of 0.9891 and an R2 test of 0.9579, and a ratio of 0.9: 0.1 obtained R2 train of 0.9890 and R2 test of 0.9660.

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Section: Resultsmentioning

confidence: 99%