AIMC Topic: Machine Learning

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General stochastic separation theorems with optimal bounds.

Neural networks : the official journal of the International Neural Network Society
Phenomenon of stochastic separability was revealed and used in machine learning to correct errors of Artificial Intelligence (AI) systems and analyze AI instabilities. In high-dimensional datasets under broad assumptions each point can be separated f...

A survey on modern trainable activation functions.

Neural networks : the official journal of the International Neural Network Society
In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest in the scientific community in investigating ...

Prediction of Phakic Intraocular Lens Vault Using Machine Learning of Anterior Segment Optical Coherence Tomography Metrics.

American journal of ophthalmology
PURPOSE: To compare the achieved vault using the conventional manufacturer's nomogram and the predicted vault using machine learning, in a large cohort of eyes undergoing posterior chamber phakic intraocular lens (EVO implantable collamer lens [ICL];...

Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data.

Sensors (Basel, Switzerland)
In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensit...

Machine learning prediction models for prognosis of critically ill patients after open-heart surgery.

Scientific reports
We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, a...

Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications.

Scientific reports
Early differential diagnosis of several motor neuron diseases (MNDs) is extremely challenging due to the high number of overlapped symptoms. The routine clinical practice is based on clinical history and examination, usually accompanied by electrophy...

Forecasting influenza activity using machine-learned mobility map.

Nature communications
Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials a...

Analysis of the nonperfused volume ratio of adenomyosis from MRI images based on fewshot learning.

Physics in medicine and biology
The nonperfused volume (NPV) ratio is the key to the success of high intensity focused ultrasound (HIFU) ablation treatment of adenomyosis. However, there are no qualitative interpretation standards for predicting the NPV ratio of adenomyosis using m...