Machine Learning-based Prediction of pH and Temperature using Macromodel of Si3N4-gated Transistor

Mansi Doshi, R. Datar, S. Deshpande, G. Bacher
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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 Si3N4-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©. 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.
基于机器学习的si3n4门控晶体管的pH和温度预测
机器学习算法在传感应用中用于数据处理和分析,例如提取不同的特征和预测特定的参数。这项工作使用决策树和神经网络算法预测离散的pH值和温度。输入数据集来自基于si3n4门控晶体管的pH传感器的ltspice模拟宏观模型的I-V特性。利用MATLAB©中的分类学习器app对不同类型的决策树和神经网络模型进行了训练和研究。机器学习算法的性能根据其准确性、散点图和混淆矩阵进行评估。宽神经网络预测pH值的准确率为99.1%,而精细决策树算法的准确率为71.9%。
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