Nanomaterial-Based Sensor Array Signal Processing and Tuberculosis Classification Using Machine Learning
نویسندگان
چکیده
Tuberculosis (TB) has long been recognized as a significant health concern worldwide. Recent advancements in noninvasive wearable devices and machine learning (ML) techniques have enabled rapid cost-effective testing for the real-time detection of TB. However, small datasets are often encountered biomedical chemical engineering domains, which can hinder success ML models result overfitting issues. To address this challenge, we propose various data preprocessing methods approaches, including short-term memory (LSTM), convolutional neural network (CNN), Gramian angular field-CNN (GAF-CNN), multivariate time series with MinCutPool (MT-MinCutPool), classifying TB dataset consisting (MTS) sensor signals. Our proposed compared state-of-the-art commonly used MTS classification (MTSC) tasks. We find that lightweight more appropriate small-dataset problems. experimental results demonstrate average performance our outperformed baseline all aspects. Specifically, GAF-CNN model achieved highest accuracy 0.639 specificity 0.777, indicating its superior effectiveness MTSC Furthermore, MT-MinCutPool surpassed MTPool evaluation metrics, demonstrating viability
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ژورنال
عنوان ژورنال: Journal of Low Power Electronics and Applications
سال: 2023
ISSN: ['2079-9268']
DOI: https://doi.org/10.3390/jlpea13020039