AIMC Topic: Neural Networks, Computer

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3-1-3 Weight averaging technique-based performance evaluation of deep neural networks for Alzheimer's disease detection using structural MRI.

Biomedical physics & engineering express
Alzheimer's disease (AD) is a progressive neurological disorder. It is identified by the gradual shrinkage of the brain and the loss of brain cells. This leads to cognitive decline and impaired social functioning, making it a major contributor to dem...

Early diagnosis of Alzheimer's Disease based on multi-attention mechanism.

PloS one
Alzheimer's Disease is a neurodegenerative disorder, and one of its common and prominent early symptoms is language impairment. Therefore, early diagnosis of Alzheimer's Disease through speech and text information is of significant importance. Howeve...

Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.

Journal of imaging informatics in medicine
Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of lesions between the craniocaudal (CC) and mediolateral oblique (MLO) mammographic X-ray image views. M...

Neuro-fuzzy prediction model of occupational injuries in mining.

International journal of occupational safety and ergonomics : JOSE
This study investigates the possibility of developing a unique model for predicting work-related injuries in Serbian underground coal mines using neural networks and fuzzy logic theory. Accidents are common due to the unique nature of underground mi...

Deep learning and feature reconstruction assisted vis-NIR calibration method for on-line monitoring of key growth indicators during kombucha production.

Food chemistry
Artificial intelligence (AI) technology is advancing the digitization and intelligence development of the food industry. A promising application is using deep learning-assisted visible near-infrared (vis-NIR) spectroscopy to monitor residual sugar an...

DCST: Dual Cross-Supervision for Transformer-based Unsupervised Domain Adaptation.

Neural networks : the official journal of the International Neural Network Society
Unsupervised Domain Adaptation aims to leverage a source domain with ample labeled data to tackle tasks on an unlabeled target domain. However, this poses a significant challenge, particularly in scenarios exhibiting significant disparities between t...

Skin Cancer Detection in Diverse Skin Tones by Machine Learning Combining Audio and Visual Convolutional Neural Networks.

Oncology
INTRODUCTION: Skin cancer (SC) is common in fair skin (FS) at a 1:5 lifetime incidence for nonmelanoma skin cancer. In order to assist clinicians' decisions, a risk intervention technology was developed, which combines a dual-mode machine learning of...

Multi-scale convolution enhanced transformer for multivariate long-term time series forecasting.

Neural networks : the official journal of the International Neural Network Society
In data analysis and forecasting, particularly for multivariate long-term time series, challenges persist. The Transformer model in deep learning methods has shown significant potential in time series forecasting. The Transformer model's dot-product ...

SELFNet: Denoising Shear Wave Elastography Using Spatial-temporal Fourier Feature Networks.

Ultrasound in medicine & biology
OBJECTIVE: Ultrasound-based shear wave elastography offers estimation of tissue stiffness through analysis of the propagation of a shear wave induced by a stimulus. Displacement or velocity fields during the process can contain noise as a result of t...

Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear Spiking Neural Membrane Systems.

International journal of neural systems
Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image seg...