Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-secti...
Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and manag...
While previous research studies have focused on either caregivers' or residents' perception and use of social robots, this article offers an empirical...
By 2050, the world's population is predicted to reach over 9 billion, which requires 70% increased production in agriculture and food industries to me...
Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest develop...
OBJECTIVE: Predicting whether the posterior cruciate ligament (PCL) should be preserved during total knee arthroplasty (TKA) procedures is a complex t...
Robotic assistance can improve the learning of complex motor skills. However, the assistance designed and used up to now mainly guides motor commands ...
Dementia is characterized by a progressive loss of cognitive abilities, and diagnosing its early stages Mild Cognitive Impairment (MCI), is difficult ...
OBJECTIVES: Fetal bladder rupture is rare and mainly caused by lower urinary tract obstruction (LUTO). Our case report describes a rupture at a gestat...
This study presents a generalized hybrid model for predicting HS and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbo...
Knee rehabilitation therapy after trauma or neuromotor diseases is fundamental to restore the joint functions as best as possible, exoskeleton robots ...
Artificial intelligence (AI) is expected to transform many scientific disciplines, with the potential to significantly accelerate scientific discovery...
Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding confo...
Biogas production through anaerobic digestion (AD) is one of the complex non-linear biological processes, wherein understanding its dynamics plays a c...
. The incidence of stroke rising, leading to an increased demand for rehabilitation services. Literature has consistently shown that early and intensi...
Variations in color and texture of histopathology images are caused by differences in staining conditions and imaging devices between hospitals. These...
BACKGROUND: Predicting operative time is essential for scheduling surgery and managing the operating room. This study aimed to develop machine learnin...
Increasing robotic surgical utilisation in colorectal surgery internationally has strengthened the need for standardised training. Deconstructed proce...
Due to distribution shift, deep learning based methods for image dehazing suffer from performance degradation when applied to real-world hazy images. ...
The objective of the present study was to employ a green synthesis method to produce a sustainable ZnFeO/BiOI nanocomposite and evaluate its efficacy ...