Machine Learning-Based Evolutionary Neural Network Approach Applied in Breast Cancer Tumor Classification

2020 ◽  
pp. 72-83
Author(s):  
Hoang Duc Quy ◽  
Cao Van Kien ◽  
Ho Pham Huy Anh ◽  
Nguyen Ngoc Son
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Tumor Classification ◽  
Network Approach ◽  
Neural Network Approach ◽  
Evolutionary Neural Network ◽  
Cancer Tumor

scholarly journals Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach

2020 ◽  
Vol 10 (17) ◽  
pp. 5764
Author(s):  
Benjamin Tsui ◽  
William A. P. Smith ◽  
Gavin Kearney
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Spherical Harmonic ◽  
High Frequency ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Network Approach ◽  
Spectral Difference ◽  
Neural Network Approach ◽  
Head Related Transfer Function

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse head related transfer function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions into high frequency representations of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of convolutional auto-encoder (CAE) and denoising auto-encoder (DAE) models is proposed to restore the high frequency distortion in SH-interpolated HRTFs. Results were evaluated using both perceptual spectral difference (PSD) and localisation prediction models, both of which demonstrated significant improvement after the restoration process.

Jitter Decomposition Meets Machine Learning: 1D-Convolutional Neural Network Approach

2021 ◽  
pp. 1-1
Author(s):  
Nan Ren ◽  
Zaiming Fu ◽  
Dandan Zhou ◽  
Dexuan Kong ◽  
Hanglin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Network Approach ◽  
Neural Network Approach
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