AIMC Topic: Neural Networks, Computer

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Meaning-driven syntactic predictions in a parallel processing architecture: Theory and algorithmic modeling of ERP effects.

Neuropsychologia
Syntactic and semantic information processing can interact selectively during language comprehension. However, the nature and extent of the interactions, in particular of semantic effects on syntax, remain to some extent elusive. We revisit an influe...

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges.

Journal of chemical theory and computation
In recent years, machine learning (ML) methods have become increasingly popular in computational chemistry. After being trained on appropriate ab initio reference data, these methods allow for accurately predicting the properties of chemical systems,...

Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning.

International journal of environmental research and public health
Chronic pharyngitis is a common disease, which has a long duration and a wide range of onset. It is easy to misdiagnose by mistaking it with other diseases, such as chronic tonsillitis, by using common diagnostic methods. In order to reduce costs and...

DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks.

Scientific reports
Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed conventional algorithms in comput...

Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks.

BMC infectious diseases
BACKGROUND: Establishing epidemiological models and conducting predictions seems to be useful for the prevention and control of human brucellosis. Autoregressive integrated moving average (ARIMA) models can capture the long-term trends and the period...

Using a Multi-Task Recurrent Neural Network With Attention Mechanisms to Predict Hospital Mortality of Patients.

IEEE journal of biomedical and health informatics
Estimating hospital mortality of patients is important in assisting clinicians to make decisions and hospital providers to allocate resources. This paper proposed a multi-task recurrent neural network with attention mechanisms to predict patients' ho...

nCREANN: Nonlinear Causal Relationship Estimation by Artificial Neural Network; Applied for Autism Connectivity Study.

IEEE transactions on medical imaging
Quantifying causal (effective) interactions between different brain regions are very important in neuroscience research. Many conventional methods estimate effective connectivity based on linear models. However, using linear connectivity models may o...

Novel bifurcation results for a delayed fractional-order quaternion-valued neural network.

Neural networks : the official journal of the International Neural Network Society
This paper reports the innovative results on the stability and bifurcation for a delayed fractional-order quaternion-valued neural network(FOQVNN). Delay-stimulated bifurcation criteria of the developed FOQVNN are attained. Then, the bifurcation diag...

Surgical skill levels: Classification and analysis using deep neural network model and motion signals.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Currently, the assessment of surgical skills relies primarily on the observations of expert surgeons. This may be time-consuming, non-scalable, inconsistent and subjective. Therefore, an automated system that can objectivel...

DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images.

PLoS computational biology
Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic ...