AIMC Topic: Neural Networks, Computer

Clear Filters Showing 12541 to 12550 of 31376 articles

Cultural and Creative Product Design and Image Recognition Based on Deep Learning.

Computational intelligence and neuroscience
In today's technological world, advanced intelligence technologies such as deep learning (DL) techniques are widely applied in various fields. In this study, people are going to research cultural and creative product design and image recognition base...

Deep Learning-Based Football Player Detection in Videos.

Computational intelligence and neuroscience
The main task of football video analysis is to detect and track players. In this work, we propose a deep convolutional neural network-based football video analysis algorithm. This algorithm aims to detect the football player in real time. First, five...

Semantic segmentation of human cell nucleus using deep U-Net and other versions of U-Net models.

Network (Bristol, England)
The deep learning models play an essential role in many areas, including medical image analysis. These models extract important features without human intervention. In this paper, we propose a deep convolution neural network, named as deep U-Net mode...

Dynamic Instance Domain Adaptation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Most existing studies on unsupervised domain adaptation (UDA) assume that each domain's training samples come with domain labels (e.g., painting, photo). Samples from each domain are assumed to follow the same distribution and the domain labels are e...

A Multistage Framework With Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem and propose a novel multi-stage framework to solve real-world situations when the target data are unlabeled and arriving online sequentially in batches. Most of the tr...

Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography.

Journal of digital imaging
Limited availability of medical imaging datasets is a vital limitation when using "data hungry" deep learning to gain performance improvements. Dealing with the issue, transfer learning has become a de facto standard, where a pre-trained convolution ...

BackEISNN: A deep spiking neural network with adaptive self-feedback and balanced excitatory-inhibitory neurons.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) transmit information through discrete spikes that perform well in processing spatial-temporal information. Owing to their nondifferentiable characteristic, difficulties persist in designing SNNs that deliver good perfor...

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data.

Sensors (Basel, Switzerland)
Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for...

On-Device IoT-Based Predictive Maintenance Analytics Model: Comparing TinyLSTM and TinyModel from Edge Impulse.

Sensors (Basel, Switzerland)
A precise prediction of the health status of industrial equipment is of significant importance to determine its reliability and lifespan. This prediction provides users information that is useful in determining when to service, repair, or replace the...

Semantic Analysis Technology of English Translation Based on Deep Neural Network.

Computational intelligence and neuroscience
English translation plays an important role in the development of science and technology and cultural exchanges. With the increase in translation volume, intelligent translation has become inevitable, but there is no effective solution for semantic t...