AIMC Topic: Deep Learning

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Research on the process of small sample non-ferrous metal recognition and separation based on deep learning.

Waste management (New York, N.Y.)
Consumption of copper and aluminum has increased significantly in recent years; therefore, recycling these elements from the end-of-life vehicles (ELVs) will be of great economic value and social benefit. However, the separation of non-ferrous materi...

Low-Cost and Device-Free Human Activity Recognition Based on Hierarchical Learning Model.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) has been a vital human-computer interaction service in smart homes. It is still a challenging task due to the diversity and similarity of human actions. In this paper, a novel hierarchical deep learning-based methodol...

Detecting the Early Infarct Core on Non-Contrast CT Images with a Deep Learning Residual Network.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PURPOSE: To explore a new approach mainly based on deep learning residual network (ResNet) to detect infarct cores on non-contrast CT images and improve the accuracy of acute ischemic stroke diagnosis.

An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning.

Computers in biology and medicine
The new coronavirus disease known as COVID-19 is currently a pandemic that is spread out the whole world. Several methods have been presented to detect COVID-19 disease. Computer vision methods have been widely utilized to detect COVID-19 by using ch...

Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle.

Dento maxillo facial radiology
OBJECTIVE: The present study aimed to verify the classification performance of deep learning (DL) models for diagnosing fractures of the mandibular condyle on panoramic radiographs using data sets from two hospitals and to compare their internal and ...

Deep joint learning for language recognition.

Neural networks : the official journal of the International Neural Network Society
Deep learning methods for language recognition have achieved promising performance. However, most of the studies focus on frameworks for single types of acoustic features and single tasks. In this paper, we propose the deep joint learning strategies ...

Reducing bias to source samples for unsupervised domain adaptation.

Neural networks : the official journal of the International Neural Network Society
Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while labels are only available in the source domain. Lots of works in UDA focus on finding a common representation of the two domains via domain alignment, assuming th...

Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm.

Toxicologic pathology
In preclinical studies that involve animal models for hepatic fibrosis, accurate quantification of the fibrosis is of utmost importance. The use of digital image analysis based on deep learning artificial intelligence (AI) algorithms can facilitate a...