AIMC Topic: Machine Learning

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Coupled convolution layer for convolutional neural network.

Neural networks : the official journal of the International Neural Network Society
We propose a coupled convolution layer comprising multiple parallel convolutions with mutually constrained filters. Inspired by biological human vision mechanism, we constrain the convolution filters such that one set of filter weights should be geom...

Modeling Dengue vector population using remotely sensed data and machine learning.

Acta tropica
Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this v...

Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.

Journal of neuro-oncology
INTRODUCTION: Machine learning methods have been introduced as a computer aided diagnostic tool, with applications to glioma characterisation on MRI. Such an algorithmic approach may provide a useful adjunct for a rapid and accurate diagnosis of a gl...

Statistical and artificial neural network-based analysis to understand complexity and heterogeneity in preeclampsia.

Computational biology and chemistry
Preeclampsia is a pregnancy associated disease. It is characterized by high blood pressure and symptoms that are indicative of damage to other organ systems, most often involving the liver and kidneys. If left untreated, the condition could be fatal ...

Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques.

Environmental science and pollution research international
Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. Th...

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification.

Journal of biomedical informatics
INTRODUCTION: An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for comb...

Reliable and Performant Identification of Low-Energy Conformers in the Gas Phase and Water.

Journal of chemical information and modeling
Prediction of compound properties from structure via quantitative structure-activity relationship and machine-learning approaches is an important computational chemistry task in small-molecule drug research. Though many such properties are dependent ...

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

BMC bioinformatics
BACKGROUND: There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) o...

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

PloS one
This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in...

Emotional hyper-reactivity and cardiometabolic risk in remitted bipolar patients: a machine learning approach.

Acta psychiatrica Scandinavica
OBJECTIVE: Remitted bipolar disorder (BD) patients frequently present with chronic mood instability and emotional hyper-reactivity, associated with poor psychosocial functioning and low-grade inflammation. We investigated emotional hyper-reactivity a...