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Deep gaussian processes and infinite neural networks for the analysis of EEG signals in Alzheimer's diseases
(Centro de Investigaciones en Matemática Pura y Aplicada (CIMPA) y Escuela de Matemática, San José, Costa Rica., 2022)
Deep Gaussian processes and infinite neural networks for the analysis of EEG signals in Alzheimer’s diseasesProcesos Gausianos profundos y redes neuronales infinitas para el análisis de señales EEG en la enfermedad de Alzheimer
(Universidad de Costa Rica, Centro de Investigación en Matemática Pura y Aplicada (CIMPA), 2022)
Emotion classification on eye-tracking and electroencephalograph fused signals employing deep gradient neural networks
Emotion produces complex neural processes and physiological changes under appropriate event stimulation. Physiological signals have the advantage of better reflecting a person's actual emotional state than facial expressions ...
A gaussian process emulator for estimating the volume of tissue activated during deep brain stimulationA gaussian process emulator for estimating the volume of tissue activated during deep brain stimulation
(Pereira : Universidad Tecnológica de PereiraFacultad de Ingenierías Eléctrica, Electrónica y Ciencias de la ComputaciónMaestría en Ingeniería Eléctrica, 2016)
Avanços recentes em caracterização e classificação de imagens de texturas: explorando teoria da informação, aprendizado profundo e de variedades
(Universidade Federal de São CarlosUFSCarPrograma de Pós-Graduação em Ciência da Computação - PPGCCCâmpus São Carlos, 2020-04-30)
The task of extracting features from images is a very important activity for many computer vision and image processing applications. Especially the characterization and identification of textures is a fundamental issue in ...
Assessment of deep learning techniques for prognosis of solar thermal systems
(Elsevier, 2020)
Solar Hot Water (SHW) systems are a sustainable and renewable alternative for domestic and low- temperature industrial applications. As solar energy is a variable resource, performance prediction methods are useful tools ...
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
(Springer Science, 2020)
La retinopatía diabética (RD) es una de las complicaciones microvasculares de la diabetes mellitus, que sigue siendo una de las principales causas de ceguera en todo el mundo. Los modelos computacionales basados en redes ...
A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising
(2019-01-01)
During the image acquisition process, some level of noise is usually added to the data mainly due to physical limitations of the sensor, and also regarding imprecisions during the data transmission and manipulation. ...
Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks
Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition ...
Brain fMRI segmentation under emotion stimuli incorporating attention-based deep convolutional neural networks
Functional magnetic resonance imaging (fMRI) is widely used for clinical examinations, diagnosis, and treatment. By segmenting fMRI images, large-scale medical image data can be processed more efficiently. Most deep learning ...