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This contains data for ISFET based pH sensor drift compensation using machine learning techniques
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The correlation matrix and the survey items are provided for the design and validation of an instrument to evaluate the implementation of six sigma critical success factors during the realization of improvement projects in higher education institutions.
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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.
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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
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The zipped file includes a description of the combined household model example and a Simscape/MATLAB file for the model.
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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"
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"3_multi-media-of-figure5.zip" includes two short videos. Both of which shows the process of Inertial Generated Timing Sequence (IGTS) works.
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"3_multi-media-of-figure5.zip" includes two short videos. Both of which shows the process of Inertial Generated Timing Sequence (IGTS) works.
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Data and codes for journal paper "MmWave Vehicular Beam Training with Situational Awareness Using Machine Learning" submitted to IEEE Access.
The code assumes Python 3.
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