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Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.

Human genomics
In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed ...

Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection.

Medical & biological engineering & computing
Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely...

Directions in abusive language training data, a systematic review: Garbage in, garbage out.

PloS one
Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effecti...

Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network.

BioMed research international
Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in ...

Stacked DeBERT: All attention in incomplete data for text classification.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose Stacked DeBERT, short for StackedDenoising Bidirectional Encoder Representations from Transformers. This novel model improves robustness in incomplete data, when compared to existing systems, by designing a novel encoding sc...

Segmentation of Chronic Subdural Hematomas Using 3D Convolutional Neural Networks.

World neurosurgery
OBJECTIVE: Chronic subdural hematomas (cSDHs) are an increasingly prevalent neurologic disease that often requires surgical intervention to alleviate compression of the brain. Management of cSDHs relies heavily on computed tomography (CT) imaging, an...

Validation of the Al-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator in Patients 65 Years and Older.

Annals of surgery
OBJECTIVE: We sought to assess the performance of the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) tool in elderly emergency surgery (ES) patients.

Combining Data with Predictions for Modeling Hepatic Steatosis by Using Stratified Bagging and Conformal Prediction.

Chemical research in toxicology
Hepatic steatosis (fatty liver) is a severe liver disease induced by the excessive accumulation of fatty acids in hepatocytes. In this study, we developed reliable models for predicting hepatic steatosis on the basis of an data set of 1041 compound...

Automatic Identification of Information Quality Metrics in Health News Stories.

Frontiers in public health
Many online and printed media publish health news of questionable trustworthiness and it may be difficult for laypersons to determine the information quality of such articles. The purpose of this work was to propose a methodology for the automatic a...

Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks.

PloS one
Owing the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has...