Forecasting the dynamics of chaotic systems from the analysis of their output signals is a challenging problem with applications in most fields of modern science. In this work, we use a laser model to compare the performance of several machine learning algorithms for forecasting the amplitude of upcoming emitted chaotic pulses. We simulate the dynamics of an optically injected semiconductor laser that presents a rich variety of dynamical regimes when changing the parameters. We focus on a particular regime where the intensity shows a chaotic pulsing dynamics, and occasionally an ultra-high pulse, reminiscent of a rogue wave, is emitted. Our goal is to predict the amplitude (height) of the next pulse, knowing the amplitude of the three preceding pulses. We compare the performance of several machine learning methods, namely neural networks, support vector machine, nearest neighbors and reservoir computing. We analyze how their performance depends on the length of the time-series used for training.
Unprecedented advances in machine learning have led to a variety of algorithms for the remote evaluation of biomedical images, allowing for cost-effective early detection of diseases. In particular, a lot of efforts are focused on the development of reliable image analysis tools for the early diagnosis of eye diseases. Here we present several new methods for ophthalmic image analysis. We propose a machine learning algorithm for ordering images of the anterior chamber (optical coherence tomography, OCT), which extracts features that discriminate between healthy subjects and patients with angle-closure. We also present an algorithm to detect the OCT images that contain artifacts, and we show that removing these images from the data base improves the performance of the ordering algorithm. Finally, we present algorithms for the analysis of retina fundus images, which are able to segment the vessel network in the retina and extract features from the topological tree-like network structure. We show that these features discriminate between healthy subjects and those with glaucoma or diabetic retinopathy.
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