Predicting Level Measurements by Supervised Learning Based on Gabor and Smote Filters: An Industrial Non-Interacting Tanks Scenario

Abstract

Fluid - Level measurement is required in recognizing the state variable of a Level processing plant for monitoring the level deviations in most the industrial plants. This issue has mostly been addressed by conventional methods using level sensors and level transmitters. However, the cost associated with these mechanisms can be reduced. It has been identified the chances with the applications machine learning approach in this scenario. Such a solution first of its kind is proposed and tried in this article. Gabor feature selection is established with SMOTE (Synthetic Minority Oversampling Technique) to overcome the imbalanced data set to improve the accuracy performance using Weka software. This paper aims to construct a level predictor based on machine learning algorithms and to demonstrate the training and testing performance validated with more accuracy. At this juncture, we consider the fluid level images for analyzing and predicting the level measurement by building supervised models with selected classifiers like IBk, JRip, J48, and Random Forest machine learning algorithms. The java-implemented tool Weka is subjected to get the maximum accuracy of 76.4286% and weighted average ROC (Receiver Operator Characteristics) values of 0.903. Hence such smart measurements using Machine learning algorithms, part of Artificial intelligence provide us a vital role in measuring the level parameter in less time using image processing filters.