AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Data Visualization

Showing 1 to 10 of 29 articles

Clear Filters

Optimization of Tennis Teaching Resources and Data Visualization Based on Support Vector Machine.

Computational intelligence and neuroscience
In recent years, with the continuous development of machine learning technology, this technology has achieved success in many fields and activities. Therefore, using machine learning technology for fuzzy research has a good research prospect. In the ...

Packaging Big Data Visualization Based on Computational Intelligence Information Design.

Computational intelligence and neuroscience
A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an opt...

A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network.

Biomedical and environmental sciences : BES
OBJECTIVES: Hand, foot and mouth disease (HFMD) is a widespread infectious disease that causes a significant disease burden on society. To achieve early intervention and to prevent outbreaks of disease, we propose a novel warning model that can accur...

Machine Learning Assisted Prediction of Power Conversion Efficiency of All-Small Molecule Organic Solar Cells: A Data Visualization and Statistical Analysis.

Molecules (Basel, Switzerland)
Organic solar cells are famous for their cheap solution processing. Their industrialization needs fast designing of efficient materials. For this purpose, testing of large number of materials is necessary. Machine learning is a better option due to c...

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity.

Sensors (Basel, Switzerland)
Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Ma...

Protocol for automated multivariate quantitative-image-based cytometry analysis by fluorescence microscopy of asynchronous adherent cells.

STAR protocols
Here, we present a protocol for multivariate quantitative-image-based cytometry (QIBC) analysis by fluorescence microscopy of asynchronous adherent cells. We describe steps for the preparation, treatment, and fixation of cells, sample staining, and i...

Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph neural networks.

BMC bioinformatics
With the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approac...

Visualization strategies to aid interpretation of high-dimensional genotoxicity data.

Environmental and molecular mutagenesis
This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seve...

Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG.

IEEE journal of biomedical and health informatics
Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively...

Recovering Permuted Sequential Features for effective Reinforcement Learning.

Neural networks : the official journal of the International Neural Network Society
When applying Reinforcement Learning (RL) to the real-world visual tasks, two major challenges necessitate consideration: sample inefficiency and limited generalization. To address the above two challenges, previous works focus primarily on learning ...