AIMC Topic: Algorithms

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A hybrid EEG classification model using layered cascade deep learning architecture.

Medical & biological engineering & computing
The problem of multi-class classification is always a challenge in the field of EEG (electroencephalogram)-based seizure detection. The traditional studies focus on computing or learning a set of features from EEG to distinguish between different pat...

Distribution-free Bayesian regularized learning framework for semi-supervised learning.

Neural networks : the official journal of the International Neural Network Society
In machine learning it is often necessary to assume or know the distribution of the data, however it is difficult to do so in practical applications. Aiming to this problem, this work, we propose a novel distribution-free Bayesian regularized learnin...

PSE-Net: Channel pruning for Convolutional Neural Networks with parallel-subnets estimator.

Neural networks : the official journal of the International Neural Network Society
Channel Pruning is one of the most widespread techniques used to compress deep neural networks while maintaining their performances. Currently, a typical pruning algorithm leverages neural architecture search to directly find networks with a configur...

Financial impact of incorporating deep learning reconstruction into magnetic resonance imaging routine.

European journal of radiology
PURPOSE: Artificial intelligence and deep learning solutions are increasingly utilized in healthcare and radiology. The number of studies addressing their enhancement of productivity and monetary impact is, however, still limited. Our hospital has fa...

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.

Microbiology spectrum
Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse pr...

Predictive analytics for cardiovascular patient readmission and mortality: An explainable approach.

Computers in biology and medicine
BACKGROUND: Cardiovascular patients experience high rates of adverse outcomes following discharge from hospital, which may be preventable through early identification and targeted action. This study aimed to investigate the effectiveness and explaina...

Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms.

World neurosurgery
OBJECTIVE: This study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injur...

Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements.

NeuroImage
DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, w...

Deus Ex Machina? The Rise of Artificial Intelligence in Toxicology.

Chemical research in toxicology
Artificial intelligence (AI) is rising rapidly, driven by big data, complex algorithms, and computing resources. Current research presented at the American Chemical Society Fall 2023 Meeting demonstrates AI to be a valuable predictive and supporting ...

A deep learning method to identify and localize large-vessel occlusions from cerebral digital subtraction angiography.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: An essential step during endovascular thrombectomy is identifying the occluded arterial vessel on a cerebral digital subtraction angiogram (DSA). We developed an algorithm that can detect and localize the position of occlusion...