AIMC Topic: Neural Networks, Computer

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Interpretable clinical prediction via attention-based neural network.

BMC medical informatics and decision making
BACKGROUND: The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in th...

Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease.

JCI insight
BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to ide...

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network.

Computational intelligence and neuroscience
White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include , , , , and , where each type constitutes a diffe...

Investigation of singular ordinary differential equations by a neuroevolutionary approach.

PloS one
In this research, we have investigated doubly singular ordinary differential equations and a real application problem of studying the temperature profile in a porous fin model. We have suggested a novel soft computing strategy for the training of unk...

qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks.

Magnetic resonance in medicine
PURPOSE: To develop a set of artificial neural networks, collectively termed qMTNet, to accelerate data acquisition and fitting for quantitative magnetization transfer (qMT) imaging.

Heavy metals in submicronic particulate matter (PM) from a Chinese metropolitan city predicted by machine learning models.

Chemosphere
The aim of this study was to establish a method for predicting heavy metal concentrations in PM (aerosol particles with an aerodynamic diameter ≤ 1.0 μm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) me...

Identifying Fatal Head Injuries on Postmortem Computed Tomography Using Convolutional Neural Network/Deep Learning: A Feasibility Study.

Journal of forensic sciences
Postmortem computed tomography (PMCT) is a relatively recent advancement in forensic pathology practice that has been increasingly used as an ancillary investigation and screening tool. One area of clinical CT imaging that has garnered a lot of resea...

Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines.

Neural networks : the official journal of the International Neural Network Society
In this work, a novel data-driven fault diagnostic framework is developed by using hybrid multi-mode machine learning strategies to monitor system health status. The coexistence of multi-mode and concurrent faults and their adverse coupling effects p...

Quantum neural networks model based on swap test and phase estimation.

Neural networks : the official journal of the International Neural Network Society
In this paper, a neural networks model for quantum computer is proposed. The core of this model is quantum neuron. Firstly, the inner product of the input qubits and the weight qubits is mapped to the phase of the control qubits in the neuron by the ...

Deep Learning-Based Imbalanced Data Classification for Drug Discovery.

Journal of chemical information and modeling
Drug discovery studies have become increasingly expensive and time-consuming processes. In the early phase of drug discovery studies, an extensive search has been performed to find drug-like compounds, which then can be optimized over time to become ...