AIMC Topic: Algorithms

Clear Filters Showing 9281 to 9290 of 28713 articles

A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli.

Sensors (Basel, Switzerland)
In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extrac...

Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.

Scientific reports
Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars a...

Deep learning for tumor margin identification in electromagnetic imaging.

Scientific reports
In this work, a novel method for tumor margin identification in electromagnetic imaging is proposed to optimize the tumor removal surgery. This capability will enable the visualization of the border of the cancerous tissue for the surgeon prior or du...

A new method based on deep learning and image processing for detection of strabismus with the Hirschberg test.

Photodiagnosis and photodynamic therapy
Strabismus is a condition in which one or both eyes do not work in parallel or in harmony. People with strabismus have one eye looking straight ahead while the other eye looks inwards, outwards, upwards or downwards. This condition can affect both ey...

Understanding neural network through neuron level visualization.

Neural networks : the official journal of the International Neural Network Society
Neurons are the fundamental units of neural networks. In this paper, we propose a method for explaining neural networks by visualizing the learning process of neurons. For a trained neural network, the proposed method obtains the features learned by ...

Extracting lung contour deformation features with deep learning for internal target motion tracking: a preliminary study.

Physics in medicine and biology
. To propose lung contour deformation features (LCDFs) as a surrogate to estimate the thoracic internal target motion, and to report their performance by correlating with the changing body using a cascade ensemble model (CEM). LCDFs, correlated to th...

MBT3D: Deep learning based multi-object tracker for bumblebee 3D flight path estimation.

PloS one
This work presents the Multi-Bees-Tracker (MBT3D) algorithm, a Python framework implementing a deep association tracker for Tracking-By-Detection, to address the challenging task of tracking flight paths of bumblebees in a social group. While trackin...

Machine Learning Algorithms Predict Long-Term Postoperative Opioid Misuse: A Systematic Review.

The American surgeon
INTRODUCTION: A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review ai...

Adaptive machine learning method for photoacoustic computed tomography based on sparse array sensor data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Photoacoustic computed tomography (PACT) is a non-invasive biomedical imaging technology that has developed rapidly in recent decades, especially has shown potential for small animal studies and early diagnosis of human dise...

SelANet: decision-assisting selective sleep apnea detection based on confidence score.

BMC medical informatics and decision making
BACKGROUND: One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies have been conducted to devel...