Staffing & Scheduling

Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction.

Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of...

A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonan...

Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye.

In this study, we propose a method for training convolutional neural networks to make them identify ...

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with ...

Probing the degradation of pharmaceuticals in urine using MFC and studying their removal efficiency by UPLC-MS/MS.

Nutrient recovery from source-separated human urine has attracted interest as it is rich in nitrogen...

Future of Robotic Surgical Education: The (R)evolution from Training the Barber-surgeon to Subspecialist Robot-surgeon.

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.

PURPOSE: To compare performance of independently developed deep learning algorithms for detecting gl...

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT char...

Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables.

Free energy surfaces of chemical and physical systems are often generated using a popular class of e...

A New Design Scheme for Intelligent Upper Limb Rehabilitation Training Robot.

In view of the urgent need for intelligent rehabilitation equipment for some disabled people, an int...

Promises of artificial intelligence in neuroradiology: a systematic technographic review.

PURPOSE: To conduct a systematic review of the possibilities of artificial intelligence (AI) in neur...

Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is underdiagnosed in the community. Thoraci...

Key components of mechanical work predict outcomes in robotic stroke therapy.

BACKGROUND: Clinical practice typically emphasizes active involvement during therapy. However, tradi...

Training of deep neural networks for the generation of dynamic movement primitives.

Dynamic movement primitives (DMPs) have proven to be an effective movement representation for motor ...

Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.

In order to train the neural network for plant phenotyping, a sufficient amount of training data mus...

Neural Message Passing for NMR Chemical Shift Prediction.

Fast and accurate prediction of NMR spectra enables automatic structure validation and elucidation o...

Machine learning modelling of neuropsychological performance could determine vocational training outcome after a brain injury: Case report.

Effectively training neural networks for stock index prediction: Predicting the S&P 500 index without using its index data.

We propose a novel method for training neural networks to predict the future prices of stock indexes...

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation.

The medical imaging literature has witnessed remarkable progress in high-performing segmentation mod...

A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo, Egypt.

The quality of vehicular collision data is crucial for studying the relationship between injury seve...

Improving accuracy and robustness of deep convolutional neural network based thoracic OAR segmentation.

Deep convolutional neural network (DCNN) has shown great success in various medical image segmentati...