AIMC Topic: Neural Networks, Computer

Clear Filters Showing 8271 to 8280 of 31376 articles

Optimization of the game improvement and data analysis model for the early childhood education major via deep learning.

Scientific reports
An ever-growing portion of the economy is dedicated to the field of education, intensifying the urgency of identifying strategies to secure the sector's enduring prosperity and elevate educational standards universally. This study introduces a model ...

Deep learning-based total suspended solids concentration classification of stream water surface images captured by mobile phone.

Environmental monitoring and assessment
The continuous monitoring of total suspended solids (TSS) in streams plays an important role in the management of hydrological processes, and TSS is also a decisive parameter in the control of pollution in streams. Determination of TSS involves both ...

Transformer based neural network for daily ground settlement prediction of foundation pit considering spatial correlation.

PloS one
Deep foundation pit settlement prediction based on machine learning is widely used for ensuring the safety of construction, but previous studies are limited to not fully considering the spatial correlation between monitoring points. This paper propos...

Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention.

PloS one
This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one brea...

Feature-wise scaling and shifting: Improving the generalization capability of neural networks through capturing independent information of features.

Neural networks : the official journal of the International Neural Network Society
From the perspective of input features, information can be divided into independent information and correlation information. Current neural networks mainly concentrate on the capturing of correlation information through connection weight parameters s...

Multi-Adaptive Optimization for multi-task learning with deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Multi-task learning is a promising paradigm to leverage task interrelations during the training of deep neural networks. A key challenge in the training of multi-task networks is to adequately balance the complementary supervisory signals of multiple...

Learning reduced-order models for cardiovascular simulations with graph neural networks.

Computers in biology and medicine
Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience loss in accuracy when working with anatomies that contain numerous junctions or pathological conditions. We develop...

Sparse convolutional neural network for high-resolution skull shape completion and shape super-resolution.

Scientific reports
Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of high-resolution shapes repr...

An attention 3DUNET and visual geometry group-19 based deep neural network for brain tumor segmentation and classification from MRI.

Journal of biomolecular structure & dynamics
There has been an abrupt increase in brain tumor (BT) related medical cases during the past ten years. The tenth most typical type of tumor affecting millions of people is the BT. The cure rate can, however, rise if it is found early. When evaluating...

Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE.

NPJ systems biology and applications
While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE...