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- W4377700909 abstract "Machine learning algorithms are employed in sensing applications for data processing and analysis, such as extracting different features and predicting specific parameter. This work predicts discrete pH levels and temperatures using decision tree and neural network algorithms. The input dataset was obtained from the I-V characteristics of the LTspice-simulated macromodel of the Si <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>3</inf> N <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>4</inf> -gated transistor-based pH sensor. Different types of decision tree and neural network models were trained and investigated using the classification learner app in MATLAB <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>©</sup> . The performance of the ML algorithms was evaluated based on their accuracy, scatter plots, and confusion matrices. The wide neural network predicted correct pH levels with an accuracy of 99.1% against 71.9% of the fine decision tree algorithms." @default.
- W4377700909 created "2023-05-24" @default.
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- W4377700909 date "2023-04-07" @default.
- W4377700909 modified "2023-09-27" @default.
- W4377700909 title "Machine Learning-based Prediction of pH and Temperature using Macromodel of Si<sub>3</sub>N<sub>4</sub>-gated Transistor" @default.
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- W4377700909 doi "https://doi.org/10.1109/i2ct57861.2023.10126184" @default.
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