The development of an automatic microplastic (MPs) classification system using spectra is crucial due to the time-consuming and error-prone nature of analyzing individual spectra, especially with a large quantity of MPs. This study presents a classification system using a dual-modality dataset from micro-Fourier Transform Infrared Spectroscopy (μFTIR) for five common polymer types: polypropylene, polystyrene, polyethylene terephthalate, polyethylene, and polyamide. A comparison of machine learning models, including Decision Tree (DT), Extremely Randomized Trees (ET), Support Vector Classifier (SVC), and Multiclass Logistic Regression (LR), is conducted using features extracted by AlexNet, ResNet18, and Vision Transformer (ViT).
View Article and Find Full Text PDFThis study presents a novel method for the early detection of strabismus, a common eye misalignment disorder, with an emphasis on its application in telemedicine. The technique leverages synchronized eye movements to estimate the pupil location of one eye based on the other, achieving close alignment in non-strabismic cases. Regression models for each eye are developed using advanced machine learning algorithms, and significant discrepancies between estimated and actual pupil positions indicate the presence of strabismus.
View Article and Find Full Text PDFAs the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers.
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