Infectious Disease

A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images.

A clinically comparable Convolutional Neural Network framework-based technique for performing automated classification of cancer grades and tissue structures in hematoxylin and eosin-stained colon histopathological images is proposed in this paper. It comprised of Enhanced Convolutional Learning Modules (ECLMs), multi-level Attention Learning Module (ALM), and Transitional Modules (TMs). The ECLMs...

Accurate virus identification with interpretable Raman signatures by machine learning.

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus bas...

A Comparative Study of Text Genres in English-Chinese Translation Effects Based on Deep Learning LSTM.

In recent years, neural network-based English-Chinese translation models have gradually supplanted traditional translation methods. The neural transla...

A Novel Approach on Deep Learning-Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs' Effect over COVID-19 Pandemic.

Following the outbreak of the COVID-19 pandemic, the continued emergence of major variant viruses has caused enormous damage worldwide by generating s...

Anti-CRISPR prediction using deep learning reveals an inhibitor of Cas13b nucleases.

As part of the ongoing bacterial-phage arms race, CRISPR-Cas systems in bacteria clear invading phages whereas anti-CRISPR proteins (Acrs) in phages i...

Diagnostic accuracy of fluorescence flow-cytometry technology using Sysmex XN-31 for imported malaria in a non-endemic setting.

Malaria diagnosis based on microscopy is impaired by the gradual disappearance of experienced microscopists in non-endemic areas. Aside from the conve...

Application of Imaging Examination Based on Deep Learning in the Diagnosis of Viral Senile Pneumonia.

Medical image classification technology, preferably which is based on the deep learning, is not only a key auxiliary diagnosis and treatment method in...

COVID-19 Isolation Control Proposal UAV and UGV for Crowded Indoor Environments: Assistive Robots in the Shopping Malls.

Artificial intelligence researchers conducted different studies to reduce the spread of COVID-19. Unlike other studies, this paper isn't for early inf...

ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction.

Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies, sheds lig...

A three-methylation-driven gene-based deep learning model for tuberculosis diagnosis in patients with and without human immunodeficiency virus co-infection.

Improved diagnostic tests for tuberculosis (TB) among people with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methy...

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mut...

Nonparametric estimation of the causal effect of a stochastic threshold-based intervention.

Identifying a biomarker or treatment-dose threshold that marks a specified level of risk is an important problem, especially in clinical trials. In vi...

Using machine learning techniques to predict antimicrobial resistance in stone disease patients.

PURPOSE: Artificial intelligence is part of our daily life and machine learning techniques offer possibilities unknown until now in medicine. This stu...

BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network.

The public health system is extremely dependent on the use of vaccines to immunize the population from a series of infectious and dangerous diseases, ...

An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images.

The lungs are COVID-19's most important focus, as it induces inflammatory changes in the lungs that can lead to respiratory insufficiency. Reducing th...

In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning.

The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which cause...

Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models.

Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and t...

A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires.

The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immun...

AD-CovNet: An exploratory analysis using a hybrid deep learning model to handle data imbalance, predict fatality, and risk factors in Alzheimer's patients with COVID-19.

Alzheimer's disease (AD) is the leading cause of dementia globally, with a growing morbidity burden that may exceed diagnosis and management capabilit...

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.

BACKGROUND: COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This st...