COVID-19

Decision tree accelerated CTU partition algorithm for intra prediction in versatile video coding.

Versatile video coding (VVC) achieves enormous improvement over the advanced high efficiency video coding (HEVC) standard due to the adoption of the quadtree with nested multi-type tree (QTMT) partition structure and other coding tools. However, the computational complexity increases dramatically as well. To tackle this problem, we propose a decision tree accelerated coding tree units (CTU) partit...

Evaluating the presence of deregulated tumoral onco-microRNAs in serum-derived exosomes of gastric cancer patients as noninvasive diagnostic biomarkers.

Exosomal microRNAs (miRNAs) are emerging diagnostic biomarkers for different types of cancers. We aim to detect gastric cancer (GC)-specific miRNAs in serum exosomes with diagnostic potential. A pair of 43 tumor and tumor-adjacent tissue biopsies obtained from GC patients, also 5 mL peripheral blood (following 12h fasting) were collected from the same patients and healthy controls (HCs). QIAGEN ...

Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation.

The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. Segmentation of COVID lesions...

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images.

COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause p...

Single B cell technologies for monoclonal antibody discovery.

Monoclonal antibodies (mAbs) are often selected from antigen-specific single B cells derived from different hosts, which are notably short-lived in ex...

A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images.

Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy c...

Accuracy of deep learning-based computed tomography diagnostic system for COVID-19: A consecutive sampling external validation cohort study.

Ali-M3, an artificial intelligence program, analyzes chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) based...

EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images.

The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To ...

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion.

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomogra...

A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19).

COVID-19 has had a detrimental impact on normal activities, public safety, and the global financial system. To identify the presence of this disease w...

Artificial Intelligence of Things (AIoT) Enabled Floor Monitoring System for Smart Home Applications.

To enable smart homes and relative applications, the floor monitoring system with embedded triboelectric sensors has been proven as an effective parad...

Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition.

In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integr...

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification.

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computat...

Neural Network-Oriented Big Data Model for Yoga Movement Recognition.

The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. P...

PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis.

COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system....

A Bilevel Learning Model and Algorithm for Self-Organizing Feed-Forward Neural Networks for Pattern Classification.

Conventional artificial neural network (ANN) learning algorithms for classification tasks, either derivative-based optimization algorithms or derivati...

New Insights Into Drug Repurposing for COVID-19 Using Deep Learning.

The coronavirus disease 2019 (COVID-19) has continued to spread worldwide since late 2019. To expedite the process of providing treatment to those who...

AANet: Adaptive Attention Network for COVID-19 Detection From Chest X-Ray Images.

Accurate and rapid diagnosis of COVID-19 using chest X-ray (CXR) plays an important role in large-scale screening and epidemic prevention. Unfortunate...

Disease variant prediction with deep generative models of evolutionary data.

Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelmin...

Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images.

COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, wides...