AIMC Topic: Machine Learning

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Blended fabric with integrated neural network based on attention mechanism qualitative identification method of near infrared spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Near Infrared spectroscopy (NIRS) qualitative analysis technology has shown excellent development potential in the field of blend fabrics. However, the qualitative detection method based on the convolutional neural network (CNN) is difficult to accur...

Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: Prognostic models play an important clinical role in the clinical management of neck pain disorders. No study has compared the performance of modern machine learning (ML) techniques, against more traditional regression techniques, when devel...

Application and performance of artificial intelligence technology in cytopathology.

Acta histochemica
Deep learning algorithms and artificial intelligence (AI) are making great progress in their capacity to evaluate and interpret image data recent advancements in computer vision and machine learning. The first use of AI in a pathology lab was in cyto...

A Systematic Literature Review on Distributed Machine Learning in Edge Computing.

Sensors (Basel, Switzerland)
Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical clou...

Word Embedding Distribution Propagation Graph Network for Few-Shot Learning.

Sensors (Basel, Switzerland)
Few-shot learning (FSL) is of great significance to the field of machine learning. The ability to learn and generalize using a small number of samples is an obvious distinction between artificial intelligence and humans. In the FSL domain, most graph...

AI-Enabled Advanced Development for Assessing Low Circulating Blood Volume for Emergency Medical Care: Comparison of Compensatory Reserve Machine-Learning Algorithms.

Sensors (Basel, Switzerland)
The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to com...

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification.

Journal of healthcare engineering
Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the r...

Sepsis labels defined by claims-based methods are ill-suited for training machine learning algorithms.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases

Machine Learning guided early drug discovery of small molecules.

Drug discovery today
Machine learning (ML) approaches have been widely adopted within the early stages of the drug discovery process, particularly within the context of small-molecule drug candidates. Despite this, the use of ML is still limited in the pharmacokinetic/ph...