Staffing & Scheduling

Adversarial training based lattice LSTM for Chinese clinical named entity recognition.

Clinical named entity recognition (CNER), which intends to automatically detect clinical entities in...

Application of artificial intelligence (AI) in Radiotherapy workflow: Paradigm shift in Precision Radiotherapy using Machine Learning.

Rethinking the performance comparison between SNNS and ANNS.

Artificial neural networks (ANNs), a popular path towards artificial intelligence, have experienced ...

Shift in Potential Malaria Transmission Areas in India, Using the Fuzzy-Based Climate Suitability Malaria Transmission (FCSMT) Model under Changing Climatic Conditions.

The future implications of climate change on malaria transmission at the global level have already b...

Application of Deep Learning in Food: A Review.

Deep learning has been proved to be an advanced technology for big data analysis with a large number...

Evaluation of multiple prediction models: A novel view on model selection and performance assessment.

Model selection and performance assessment for prediction models are important tasks in machine lear...

Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review.

PURPOSE: The required training sample size for a particular machine learning (ML) model applied to m...

A segmentation method combining probability map and boundary based on multiple fully convolutional networks and repetitive training.

Cell nuclei image segmentation technology can help researchers observe each cell's stress response t...

An analysis of training and generalization errors in shallow and deep networks.

This paper is motivated by an open problem around deep networks, namely, the apparent absence of ove...

An approach on the implementation of full batch, online and mini-batch learning on a Mamdani based neuro-fuzzy system with center-of-sets defuzzification: Analysis and evaluation about its functionality, performance, and behavior.

Due to the rapid technological evolution and communications accessibility, data generated from diffe...

The effect of using Gait Exercise Assist Robot (GEAR) on gait pattern in stroke patients: a cross-sectional pilot study.

: The Gait Exercise Assist Robot (GEAR) has been developed to support gait training for stroke patie...

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning...

Hierarchical gated recurrent neural network with adversarial and virtual adversarial training on text classification.

Document classification aims to assign one or more classes to a document for ease of management by u...

Generative adversarial network in medical imaging: A review.

Generative adversarial networks have gained a lot of attention in the computer vision community due ...

Robotically Simulated Pivot Shift That Represents the Clinical Exam.

A thorough understanding of anterior cruciate ligament (ACL) function and the effects of surgical in...

Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis.

Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the...

Artificial intelligence: Implications for the future of work.

Artificial intelligence (AI) is a broad transdisciplinary field with roots in logic, statistics, cog...

Peak alignment of gas chromatography-mass spectrometry data with deep learning.

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Sp...

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review.

BACKGROUND: Intensive care units (ICUs) face financial, bed management, and staffing constraints. De...

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.

Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides th...

Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening.

Recently much effort has been invested in using convolutional neural network (CNN) models trained on...