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A systematic review of compressive sensing: Concepts, implementations and applications
Compressive Sensing (CS) is a new sensing modality, which compresses the signal being
acquired at the time of sensing. Signals can have sparse or compressible representation …
acquired at the time of sensing. Signals can have sparse or compressible representation …
Systematic map** of global research on climate and health: a machine learning review
Background The global literature on the links between climate change and human health is
large, increasing exponentially, and it is no longer feasible to collate and synthesise using …
large, increasing exponentially, and it is no longer feasible to collate and synthesise using …
Craft: Concept recursive activation factorization for explainability
Attribution methods are a popular class of explainability methods that use heatmaps to
depict the most important areas of an image that drive a model decision. Nevertheless …
depict the most important areas of an image that drive a model decision. Nevertheless …
Padchest: A large chest x-ray image dataset with multi-label annotated reports
We present a labeled large-scale, high resolution chest x-ray dataset for the automated
exploration of medical images along with their associated reports. This dataset includes …
exploration of medical images along with their associated reports. This dataset includes …
[SÁCH][B] Neural networks and statistical learning
Providing a broad but in-depth introduction to neural network and machine learning in a
statistical framework, this book provides a single, comprehensive resource for study and …
statistical framework, this book provides a single, comprehensive resource for study and …