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Visual anomaly detection in video by variational autoencoder
Due to efforts needed to label training data, unsupervised approaches to train anomaly detection models for videos is more practical An autoencoder is a neural network that is trained to recreate its input using latent representation of input also called a bottleneck layer.
Rapid point-of-care Hemoglobin measurement through low-cost optics and Convolutional Neural Network based validation
The developed platform has demonstrated precision to the nearest $0. 18[g/dL]$ of hemoglobin, an R^2 = 0. 945 correlation to hemoglobin absorption curves reported in literature, and a 97% detection accuracy of poorly-prepared samples.
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