Belinda Rahman, Alison McEwen, Jane L Phillips, Katherine Tucker, David Goldstein, Chris Jacobs
Genetic and genomic learning needs of oncologists and oncology nurses in the era of precision medicine: a scoping review
Personalized medicine
work presents approach recognition plastics using lowcost spectroscopy sensor module together set machine learning methods sensor multispectral module capable measuring 18 wavelengths visible nearinfrared data processing analysis performed using set ten machine learning methods random forest support vector machines multilayer perceptron convolutional neural networks decision trees logistic regression naive bayes knearest neighbour adaboost linear discriminant analysis experimental setup designed systematic data collection six plastic types including pet hdpe pvc ldpe pp ps household waste set computational methods implemented generalised pipeline validation proposed approach recognition plastics results show convolutional neural networks multilayer perceptron recognise plastics mean accuracy 7250 7025 respectively largest accuracy 835 ps plastic smallest accuracy 66 pet plastic results demonstrate lowcost nearinfrared sensor machine learning methods recognise plastics effectively making affordable portable approach contributes development sustainable systems potential applications fields agriculture ewaste recycling healthcare manufacturing