AI Medical Compendium Topic

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Gaussian Process Regression Models for the Prediction of Hydrogen Bond Acceptor Strengths.

Molecular informatics
We present two approaches for the computation of hydrogen bond acceptor strengths, one by machine-learning and one by a composite quantum-mechanical protocol, both based on the well-established pK scale and dataset. The QM calculations after a necess...

ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation.

Computers in biology and medicine
Electrocardiogram (ECG) is gaining increased attention as a biometric method in a wide range of applications, such as access control and security/privacy requirements. The majority of reported investigations using the ECG biometric method are usually...

Multiple Mittag-Leffler stability of fractional-order competitive neural networks with Gaussian activation functions.

Neural networks : the official journal of the International Neural Network Society
In this paper, we explore the coexistence and dynamical behaviors of multiple equilibrium points for fractional-order competitive neural networks with Gaussian activation functions. By virtue of the geometrical properties of activation functions, the...

Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks.

Neuron
Large-scale neural recordings have established that the transformation of sensory stimuli into motor outputs relies on low-dimensional dynamics at the population level, while individual neurons exhibit complex selectivity. Understanding how low-dimen...

Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases.

International journal of environmental research and public health
Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and...

On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines.

Neural networks : the official journal of the International Neural Network Society
In this paper, we analyze the role of hidden bias in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines (GBPRBMs), which are similar to the widely used Gaussian-Bernoulli RBMs. Our experiments show that hidden bias play...

Comparison of Models for Predicting Quality of Life After Surgical Resection of Hepatocellular Carcinoma: a Prospective Study.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
BACKGROUND: The essential issue of internal validity has not been adequately addressed in prediction models such as artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and multiple linear regression (MLR)...

Using machine-learning approaches to predict non-participation in a nationwide general health check-up scheme.

Computer methods and programs in biomedicine
BACKGROUND: In the time since the launch of a nationwide general health check-up and instruction program in Japan in 2008, interest in the formulation of an effective and efficient strategy to improve the participation rate has been growing. The aim ...

A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms.

Journal of healthcare engineering
The accurate and efficient segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis. This paper presents a new multiscale Gaussian-matched filter (MGMF) based on artificial neural networks. The p...