Roughly 6,800 natural disasters occur worldwide annually, and this alarming number continues to grow due to the effects of climate change. Effective methods to improve natural disaster response include performing change detection, map alignment, and vision-aided navigation to allow for the time-efficient delivery of life-saving aid. Current software functions optimally only on nadir images taken ninety degrees above ground level. The inability to generalize to oblique images increases the need to compute an image's geocentric pose, which is its spatial orientation with respect to gravity. This Deep Learning investigation presents three convolutional models to predict geocentric pose using 5,923 nadir and oblique red, green, and blue (RGB) satellite images of cities worldwide. The first model is an autoencoder that condenses the 256x256x3 images to 32x32x16 latent space representations, demonstrating the ability to learn useful features from the data. The second model is a U-Net Fully Convolutional Network with skip connections used to predict each image's corresponding pixel-level elevation mask. This model achieves a median absolute deviation of 0.335 meters and an R2 of 0.865 on test data. Afterward, the elevation masks are concatenated with the RGB images to form four-channel inputs fed into the third model, which predicts each image's rotation angle and scale, the components of its geocentric pose. This Deep Convolutional Neural Network achieves an R2 of 0.943 on test data, significantly outperforming previous models designed by researchers. The high-accuracy software built in this study contributes to mapping and navigation procedures to accelerate disaster relief and save human lives.
Roughly 6,800 natural disasters occur worldwide annually, and this alarming number continues to grow due to the effects of climate change. Effective methods to improve natural disaster response include performing change detection, map alignment, and vision-aided navigation to allow for the time-efficient delivery of life-saving aid. Current software functions optimally only on nadir images taken ninety degrees above ground level. The inability to generalize to oblique images increases the need to compute an image's geocentric pose, which is its spatial orientation with respect to gravity. This Deep Learning investigation presents three convolutional models to predict geocentric pose using 5,923 nadir and oblique RGB satellite images of cities worldwide. The first model is an autoencoder that condenses the 256 × 256 × 3 images to 32 × 32 × 16 latent space representations, demonstrating the ability to learn useful features from the data. The second model is a U-Net Fully Convolutional Network with skip connections used to predict each image's corresponding pixel-level elevation mask. This model achieves a median absolute deviation of 0.335 meters and an R 2 of 0.865 on test data. Afterward, the elevation masks are concatenated with the RGB images to form 256 × 256 × 4 inputs of the third model, which predicts each image's rotation angle and scale, the components of its geocentric pose. This Deep Convolutional Neural Network achieves an R 2 of 0.904 on test data, significantly outperforming previous models designed by researchers. The highaccuracy software built in this study contributes to crucial procedures that can accelerate disaster relief and save human lives.
The pervasiveness of cardiovascular disease and physician misdiagnosis creates the need for artificial intelligence models to improve diagnosis accuracy. The study trains machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompts the creation of an automated open-source diagnosis tool powered by the MLP. Local Interpretable Model-Agnostic Explanations (LIME) are employed to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering segments patients into ten clusters, after which each example is passed through LIME. The resulting histograms depict a complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows nuances in the MLP's understanding of patients from different clusters. LIME analysis reveals that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and ST segment slope. Future experiments should replicate this study's LIME methodology on data sets containing more specialized features in order to gain practical medical insights about the different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models adaptable to other diseases as well.
The pervasiveness of cardiovascular disease and physician misdiagnosis creates the urgent need for artificial intelligence models to improve diagnosis accuracy. The first objective of this study was to train machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompted the creation of an open-source, automated cardiovascular disease diagnosis tool, powered by the MLP. The second objective of this study was to employ a meta-learning methodology called Local Interpretable Model-Agnostic Explanations (LIME) to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering was employed to segment the data into ten clusters, after which each data example was passed through LIME. The resulting histograms depict the complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows the nuances in the MLP's understanding of patients from different clusters. The results of meta-learning analysis reveal that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and slope of the ST segment on an electrocardiogram. Future experiments should replicate the novel methodology introduced in this study on data sets containing more specialized medical features in order to gain practical medical insights about different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models not only for cardiovascular disease, but adaptable to other diseases as well.
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