AIMC Topic: Neural Networks, Computer

Clear Filters Showing 13831 to 13840 of 31376 articles

Machine Learning-Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features.

Journal of digital imaging
Although ultrasound plays an important role in the diagnosis of chronic kidney disease (CKD), image interpretation requires extensive training. High operator variability and limited image quality control of ultrasound images have made the application...

Artificial neural networks to model the enantioresolution of structurally unrelated neutral and basic compounds with cellulose tris(3,5-dimethylphenylcarbamate) chiral stationary phase and aqueous-acetonitrile mobile phases.

Journal of chromatography. A
Artificial neural networks (ANN; feed-forward mode) are used to quantitatively estimate the enantioresolution (Rs) in cellulose tris(3,5-dimethylphenylcarbamate) of chiral molecules from their structural information. To the best of our knowledge, for...

A Photoelectric Spiking Neuron for Visual Depth Perception.

Advanced materials (Deerfield Beach, Fla.)
The biological visual system encodes optical information into spikes and processes them by the neural network, which enables the perception with high throughput of visual processing with ultralow energy budget. This has inspired a wide spectrum of de...

Simple, fast, and flexible framework for matrix completion with infinite width neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications but at considerable computational costs. Rem...

AGs-Unet: Building Extraction Model for High Resolution Remote Sensing Images Based on Attention Gates U Network.

Sensors (Basel, Switzerland)
Building contour extraction from high-resolution remote sensing images is a basic task for the reasonable planning of regional construction. Recently, building segmentation methods based on the U-Net network have become popular as they largely improv...

A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing.

Sensors (Basel, Switzerland)
Accurate fire identification can help to control fires. Traditional fire detection methods are mainly based on temperature or smoke detectors. These detectors are susceptible to damage or interference from the outside environment. Meanwhile, most of ...

Sensor Fault Diagnostics Using Physics-Informed Transfer Learning Framework.

Sensors (Basel, Switzerland)
The field of smart health monitoring, intelligent fault detection and diagnosis is expanding dramatically in order to maintain successful operation in many engineering applications. Considering possible fault scenarios that can occur in a system, ind...

Evolution and Neural Network Prediction of CO Emissions in Weaned Piglet Farms.

Sensors (Basel, Switzerland)
This paper aims to study the evolution of CO concentrations and emissions on a conventional farm with weaned piglets between 6.9 and 17.0 kg live weight based on setpoint temperature, outdoor temperature, and ventilation flow. The experimental trial ...

Prognostics of unsupported railway sleepers and their severity diagnostics using machine learning.

Scientific reports
Railway sleepers are safety-critical components of a railway structure. They support ballasted track superstructure and are a critical factor in track geometry and track components' deterioration. Unsupported sleepers are a common issue incurred afte...

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques.

Journal of healthcare engineering
Continuous noninvasive blood glucose monitoring and estimation management by using photoplethysmography (PPG) technology always have a series of problems, such as substantial time variability, inaccuracy, and complex nonlinearity. This paper proposes...