AIMC Topic: Machine Learning

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Deep learning based analysis of microstructured materials for thermal radiation control.

Scientific reports
Microstructured materials that can selectively control the optical properties are crucial for the development of thermal management systems in aerospace and space applications. However, due to the vast design space available for microstructures with ...

Single-Cell Sequencing Analysis and Multiple Machine Learning Methods Identified G0S2 and HPSE as Novel Biomarkers for Abdominal Aortic Aneurysm.

Frontiers in immunology
Identifying biomarkers for abdominal aortic aneurysms (AAA) is key to understanding their pathogenesis, developing novel targeted therapeutics, and possibly improving patients outcomes and risk of rupture. Here, we identified AAA biomarkers from publ...

Identifying molecular functional groups of organic compounds by deep learning of NMR data.

Magnetic resonance in chemistry : MRC
We preprocess the raw nuclear magnetic resonance (NMR) spectrum and extract key features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition. We also provide a strategy t...

Implementation of Aging Mechanism Analysis and Prediction for XILINX 7-Series FPGAs with a 28-nm Process.

Sensors (Basel, Switzerland)
Commercial off-the-shelf (COTS) field-programmable gate arrays (FPGAs) with a 28-nm process have become popular devices for computing systems. Although current generation FPGAs have advantages over previous models, the phenomenon of circuit aging has...

Riemannian gradient methods for stochastic composition problems.

Neural networks : the official journal of the International Neural Network Society
In the paper, we study a class of novel stochastic composition optimization problems over Riemannian manifold, which have been raised by multiple emerging machine learning applications such as distributionally robust learning in Riemannian manifold s...

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

Journal of molecular biology
MOTIVATION: Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to t...

Development of lumbar spine MRI referrals vetting models using machine learning and deep learning algorithms: Comparison models vs healthcare professionals.

Radiography (London, England : 1995)
INTRODUCTION: Referrals vetting is a necessary daily task to ensure the appropriateness of radiology referrals. Vetting requires extensive clinical knowledge and may challenge those responsible. This study aims to develop AI models to automate the ve...

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16.

Sensors (Basel, Switzerland)
The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional...

Few-Shot Text Classification with Global-Local Feature Information.

Sensors (Basel, Switzerland)
Meta-learning frameworks have been proposed to generalize machine learning models for domain adaptation without sufficient label data in computer vision. However, text classification with meta-learning is less investigated. In this paper, we propose ...

A non-destructive methodology for determination of cantaloupe sugar content using machine vision and deep learning.

Journal of the science of food and agriculture
BACKGROUND: To determine the maturity of cantaloupe, measuring the soluble solid content (SSC) as the indicator of sugar content based on the refractometric index is commonly practised. This method, however, is destructive and limited to a small numb...