AIMC Topic: Attention

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Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI.

NeuroImage. Clinical Mar 15, 2023
The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy when predicting the course of neurological conditions, including multiple sclerosis, by means of ext...
Blindness Magnetic Resonance Imaging Multiple Sclerosis Humans Attention Brain Deep Learning
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Accurate detection of arrhythmias on raw electrocardiogram images: An aggregation attention multi-label model for diagnostic assistance.

Medical engineering & physics Mar 2, 2023
BACKGROUND: The low rate of detection of abnormalities has been a major problem with current artificial intelligence-based electrocardiogram diagnostic algorithms, particularly when applied under real-world clinical scenarios.
Artificial Intelligence Algorithms Humans Arrhythmias, Cardiac Attention Electrocardiography Prospective Studies
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IPC prediction of patent documents using neural network with attention for hierarchical structure.

PloS one Mar 2, 2023
International patent classifications (IPCs) are assigned to patent documents; however, since the procedure for assigning classifications is manually done by the patent examiner, it takes a lot of time and effort to select some IPCs from about 70,000 ...
Neural Networks, Computer Machine Learning Attention
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A U-Shaped Network Based on Multi-level Feature and Dual-Attention Coordination Mechanism for Coronary Artery Segmentation of CCTA Images.

Cardiovascular engineering and technology Feb 27, 2023
PURPOSE: Computed tomography coronary angiography (CCTA) images provide optimal visualization of coronary arteries to aid in diagnosing coronary heart disease (CHD). With the deep convolutional neural network, this work aims to develop an intelligent...
Algorithms Image Processing, Computer-Assisted Neural Networks, Computer Coronary Vessels Attention Heart
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A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition.

Sensors (Basel, Switzerland) Feb 17, 2023
With the development of wearable devices such as smartwatches, several studies have been conducted on the recognition of various human activities. Various types of data are used, e.g., acceleration data collected using an inertial measurement unit se...
Acceleration Human Activities Semantics Deep Learning Attention Humans
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Being ostensive (reply to commentaries on "Expression unleashed").

The Behavioral and brain sciences Feb 17, 2023
One of our main goals with "Expression unleashed" was to highlight the distinctive, ostensive nature of human communication, and the many roles that ostension can play in human behavior and society. The commentaries we received forced us to be more p...
Humans Animals Artificial Intelligence Cultural Evolution Language Communication Attention
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Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet.

PloS one Feb 9, 2023
In power fingerprint identification, feature information is insufficient when using a single feature to identify equipment, and small load data of specific customers, difficult to meet the refined equipment classification needs. A power fingerprint i...
Neural Networks, Computer Attention
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Human-Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention.

Sensors (Basel, Switzerland) Jan 26, 2023
Emotions have a crucial function in the mental existence of humans. They are vital for identifying a person's behaviour and mental condition. Speech Emotion Recognition (SER) is extracting a speaker's emotional state from their speech signal. SER is ...
Attention Speech Humans Emotions Computers Neural Networks, Computer
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ResSKNet-SSDP: Effective and Light End-To-End Architecture for Speaker Recognition.

Sensors (Basel, Switzerland) Jan 20, 2023
In speaker recognition tasks, convolutional neural network (CNN)-based approaches have shown significant success. Modeling the long-term contexts and efficiently aggregating the information are two challenges in speaker recognition, and they have a c...
Algorithms Neural Networks, Computer Attention
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Self-attention learning network for face super-resolution.

Neural networks : the official journal of the International Neural Network Society Jan 14, 2023
Existing face super-resolution methods depend on deep convolutional networks (DCN) to recover high-quality reconstructed images. They either acquire information in a single space by designing complex models for direct reconstruction, or employ additi...
Deep Learning Learning Image Processing, Computer-Assisted Attention
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