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Ensemble deep learning: A review
(
Elsevier Ltd
, 2022 , Other)
Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning architectures are showing better performance compared to the shallow or traditional models. Deep ...
MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network
(
Springer Science and Business Media Deutschland GmbH
, 2022 , Article)
Electroencephalogram (EEG) signals suffer substantially from motion artifacts when recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many neurological diseases is heavily reliant on clean ...
An optimized algorithm for optimal power flow based on deep learning
(
Elsevier
, 2021 , Article)
With the increasing requirements for power system transient stability assessment, the research on power system transient stability assessment theory and methods requires not only qualitative conclusions about system transient ...
Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. ...
Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks
(
MDPI
, 2021 , Article)
Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming ...
PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks
(
BioMed Central Ltd
, 2022 , Article)
Background: Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predictive ...
An active learning method for diabetic retinopathy classification with uncertainty quantification
(
Springer Science and Business Media Deutschland GmbH
, 2022 , Article)
In recent years, deep learning (DL) techniques have provided state-of-the-art performance in medical imaging. However, good quality (annotated) medical data is in general hard to find due to the usually high cost of medical ...
Smartphone-based diabetic retinopathy severity classification using convolution neural networks
(
Springer
, 2021 , Conference Paper)
With diabetes growing at an alarming rate, changes in the retina causes a condition called diabetic retinopathy which eventually leads to blindness. Early detection of diabetic retinopathy is the best way to provide good ...
Edge Detection with multi-scale representation and refined Network
(
Institution of Engineering and Technology
, 2022 , Conference Paper)
Edge detection is a representation of boundaries between objects and regions in an image. Due to the variations of types, scales, intensities as well as background, the detection of these boundaries represents a challenge ...
Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review
(
Elsevier B.V.
, 2022 , Other)
Generative adversarial networks (GANs) are deep generative models (GMs) that have recently attracted attention owing to their impressive performance in generating completely novel images, text, music, and speech. Recently, ...