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This contains data for ISFET based pH sensor drift compensation using machine learning techniques

In order to support the experimental validation presented in the manuscript titled "Emotional Learning Based Controller for Quadruple Tank System - An Improved Stimuli Design for Multiple Set-point Tracking", the algorithms for controller design, optimization and hardware implementation are represented in flowchart form and included in this supporting data.

This dataset was created based on the paper 'Andras Hajdu, Gyorgy Terdik, Attila Tiba, and Henrietta Toman: A stochastic approach to handle knapsack problems in the creation of ensembles'.To summarize our experimental setup for UCI binary classification problems, we have considered base classifiers perceptron, decision tree, Levenberg-Marquardt feedforward neural network, random neural network, and discriminative restricted Boltzmann machine classifier for the 5 UCI datasets MAGIC Gamma Telescope, HIGGS, EEG EyeState, Musk (Version 2), and Spambase; datasets of large cardinalities were sele

The performance testing sample results for RPS = 110 (with the maximum TPS = ~ 350 achieved) for manuscript "Blockchain and IoT for Insurance: A Case Study and Cyberinfrastructure Solution on Fine-grained Transportation Insurance"