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In this paper, we utilized Dual Denoising Autoencoder Features (DDAF), which integrates two Denoising Auto-Encoders (DAE) with different activation function to ...
The experimental results on four typical microarray datasets show that the DDAF outperforms the Dual. Autoencoder Features (DAF) and the Cost-sensitive.
In this paper, we utilized Dual Denoising Autoencoder Features (DDAF), which integrates two Denoising Auto-Encoders (DAE) with different activation function.
Jun 27, 2024 · Dual Denoising Autoencoder Feature Learning for Cancer Diagnosis ... Dual Denoising Autoencoder Features for Imbalance Classification Problems.
In this paper, we utilized Dual Denoising Autoencoder Features (DDAF), which integrates two Denoising Auto-Encoders (DAE) with different activation function to ...
Aug 1, 2024 · This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers.
In this study, we propose a dual conditional convolutional auto-encoder framework (DCAE) to tackle this challenge. DCAE framework includes two parts: The first ...
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Dec 18, 2020 · The DA model, a deep learning algorithm, can be used to dissect important features from genome wide-expression datasets of human lung cancers.
The learning process of the dual denoising autoencoder aims to train the model by minimizing the dual objective loss function, which can be defined as follows:.
Jan 16, 2023 · We propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals.