COVID-19

Forecasting actual evapotranspiration without climate data based on stacked integration of DNN and meta-heuristic models across China from 1958 to 2021.

As a non-linear phenomenon that varies along with agro-climatic conditions alongside many other factors, Evapotranspiration (ET) process represents a complexity when be assessed especially if there is a data scarcity in the weather data. However, even under such a data scarcity, the accurate estimates of ET values remain necessary for precise irrigation. So, the present study aims to: i) evaluate ...

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques.

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automa...

The influence of EEG channels and features significance on automatic detection of epileptic waves in MECT.

Modified Electric Convulsive Therapy (MECT) is an efficacious physical therapy in treating mental disorders. The occurrence of epilepsy is a crucial b...

Coot-Lion optimized deep learning algorithm for COVID-19 point mutation rate prediction using genome sequences.

In this study, a deep quantum neural network (DQNN) based on the Lion-based Coot algorithm (LBCA-based Deep QNN) is employed to predict COVID-19. Here...

Fully Automatic Dual-Probe Lung Ultrasound Scanning Robot for Screening Triage.

Two-dimensional lung ultrasound (LUS) has widely emerged as a rapid and noninvasive imaging tool for the detection and diagnosis of coronavirus diseas...

Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy.

Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk (OAR) contouring, enhancing quality and improving efficiency in radiot...

Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex.

Opioid use disorder (OUD) has emerged as a significant global public health issue, necessitating the discovery of new medications. In this study, we p...

Investigating the effectiveness of socially assistive robot on depression and cognitive functions of community dwelling older adults with cognitive impairments.

We evaluated a socially assistive robot (SAR) named Hyodol during a six-week intervention. This study enrolled 69 older adults with cognitive decline....

CoTrFuse: a novel framework by fusing CNN and transformer for medical image segmentation.

Medical image segmentation is a crucial and intricate process in medical image processing and analysis. With the advancements in artificial intelligen...

Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these su...

Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning.

Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address...

FDA Modernization Act 2.0: An insight from nondeveloping country.

Animal testing is required in drug development research and is crucial for assessing the efficacy and safety of medications before they are commercial...

Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias.

The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related t...

Predicting congenital renal tract malformation genes using machine learning.

Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic ...

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis.

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate anti...

Identification of antigen-presentation related B cells as a key player in Crohn's disease using single-cell dissecting, hdWGCNA, and deep learning.

Crohn's disease (CD) arises from intricate intercellular interactions within the intestinal lamina propria. Our objective was to use single-cell RNA s...

Decoding movement kinematics from EEG using an interpretable convolutional neural network.

Continuous decoding of hand kinematics has been recently explored for the intuitive control of electroencephalography (EEG)-based Brain-Computer Inter...

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework.

Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unif...

Domain Agnostic Post-Processing for QRS Detection Using Recurrent Neural Network.

Deep-learning-based QRS-detection algorithms often require essential post-processing to refine the output prediction-stream for R-peak localisation. T...

Development of webcam-collected and artificial-intelligence-derived social and cognitive performance measures for neurodevelopmental genetic syndromes.

This study focused on the development and initial psychometric evaluation of a set of online, webcam-collected, and artificial intelligence-derived pa...