COVID-19

Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method.

Investigating the interaction between influent particles and biomass is basic and important for the biological wastewater treatment. The micro-level methods allow for this, such as the microscope image analysis method with the conventional ImageJ processing software. However, these methods are cost and time-consuming, and require a large amount of work on manual parameter tuning. To deal with this...

Development of an equation to predict delta bilirubin levels using machine learning.

OBJECTIVE: Delta bilirubin (albumin-covalently bound bilirubin) may provide important clinical utility in identifying impaired hepatic excretion of conjugated bilirubin, but it cannot be measured in real-time for diagnostic purposes in clinical laboratories.

DiagSWin: A multi-scale vision transformer with diagonal-shaped windows for object detection and segmentation.

Recently, Vision Transformer and its variants have demonstrated remarkable performance on various computer vision tasks, thanks to its competence in c...

Accelerating drug discovery, development, and clinical trials by artificial intelligence.

Artificial intelligence (AI) has profoundly advanced the field of biomedical research, which also demonstrates transformative capacity for innovation ...

Imitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept.

Artificial neural networks (ANNs) are versatile tools capable of learning without prior knowledge. This study aims to evaluate whether ANN can calcula...

Deciphering the role of HLF in idiopathic orbital inflammation: integrative analysis via bioinformatics and machine learning techniques.

Idiopathic orbital inflammation, formerly known as NSOI (nonspecific orbital inflammation), is characterized as a spectrum disorder distinguished by t...

Indirect reference interval estimation using a convolutional neural network with application to cancer antigen 125.

Indirect methods for reference interval (RI) estimation, which use data acquired from routine pathology testing, have the potential to accelerate the ...

Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.

Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational mo...

Real-time monitoring of activated sludge flocs via enhanced mask region-based Convolutional Neural networks.

The functionality of activated sludge in wastewater treatment processes depends largely on the structural and microbial composition of its flocs, whic...

Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy.

Fluorescence intravital microscopy captures large data sets of dynamic multicellular interactions within various organs such as the lungs, liver, and ...

Insertable Glucose Sensor Using a Compact and Cost-Effective Phosphorescence Lifetime Imager and Machine Learning.

Optical continuous glucose monitoring (CGM) systems are emerging for personalized glucose management owing to their lower cost and prolonged durabilit...

Mask-Shift-Inference: A novel paradigm for domain generalization.

Domain Generalization (DG) focuses on the Out-Of-Distribution (OOD) generalization, which is able to learn a robust model that generalizes the knowled...

A Fluorescent Immunochromatography Test Strip for the Rapid Identification of SVV and FMDV.

Seneca Valley virus (SVV) and foot-and-mouth disease virus (FMDV) belong to the Picornaviridae family, which can cause similar symptoms. After infecti...

Randomized algorithms for large-scale dictionary learning.

Dictionary learning is an important sparse representation algorithm which has been widely used in machine learning and artificial intelligence. Howeve...

CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading.

In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentati...

Integrated CNN and Federated Learning for COVID-19 Detection on Chest X-Ray Images.

Currently, Coronavirus Disease 2019 (COVID-19) is still endangering world health and safety and deep learning (DL) is expected to be the most powerful...

Synthesis of higher-B CEST Z-spectra from lower-B data via deep learning and singular value decomposition.

Chemical exchange saturation transfer (CEST) MRI at 3 T suffers from low specificity due to overlapping CEST effects from multiple metabolites, while ...

Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning.

Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects...

Polygonal Approximation Learning for Convex Object Segmentation in Biomedical Images With Bounding Box Supervision.

As a common and critical medical image analysis task, deep learning based biomedical image segmentation is hindered by the dependence on costly fine-g...

Automated segmentation in pelvic radiotherapy: A comprehensive evaluation of ATLAS-, machine learning-, and deep learning-based models.

Artificial intelligence can standardize and automatize highly demanding procedures, such as manual segmentation, especially in an anatomical site as c...