AIMC Topic: Machine Learning

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Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

Pacing and clinical electrophysiology : PACE
BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utili...

HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks.

IEEE transactions on visualization and computer graphics
To mitigate the pain of manually tuning hyperparameters of deep neural networks, automated machine learning (AutoML) methods have been developed to search for an optimal set of hyperparameters in large combinatorial search spaces. However, the search...

A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation.

IEEE transactions on visualization and computer graphics
In recent years, deep learning has opened countless research opportunities across many different disciplines. At present, visualization is mainly applied to explore and explain neural networks. Its counterpart-the application of deep learning to visu...

Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach.

Scientific reports
We aimed to delineate the neuropsychological and psychopathological profiles of children with congenital heart disease (CHD) and look for associations with clinical parameters. We conducted a prospective observational study in children with CHD who u...

An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box.

Scientific reports
Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replace...

Dense cellular segmentation for EM using 2D-3D neural network ensembles.

Scientific reports
Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has be...

Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques.

Journal of healthcare engineering
Heart angiography is a test in which the concerned medical specialist identifies the abnormality in heart vessels. This type of diagnosis takes a lot of time by the concerned physician. In our proposed method, we segmented the interested regions of h...

Machine learning for buildings' characterization and power-law recovery of urban metrics.

PloS one
In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us ...

HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets.

PloS one
The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical res...