scholarly journals Type 2 diabetes data classification using stacked autoencoders in deep neural networks

2019 ◽  
Vol 7 (4) ◽  
pp. 530-535 ◽  
Author(s):  
K Kannadasan ◽  
Damodar Reddy Edla ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Neural Networks ◽  
Type 2 Diabetes ◽  
Deep Neural Networks ◽  
Data Classification ◽  
Stacked Autoencoders

An evaluation methodology for 3D deep neural networks using visualization in 3D data classification

2019 ◽  
Vol 33 (3) ◽  
pp. 1333-1339
Author(s):  
Hyun-Tae Hwang ◽  
Soo-Hong Lee ◽  
Hyung Gun Chi ◽  
Nam Kyu Kang ◽  
Hyeon Bae Kong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Classification ◽  
Evaluation Methodology ◽  
3D Data

scholarly journals ARTIFICIAL NEURAL NETWORKS IN MEDICAL DIAGNOSIS

2021 ◽  
Vol 2 (133) ◽  
pp. 33-41
Author(s):  
Nataliya Matveeva
Keyword(s):  
Neural Networks ◽  
Type 2 Diabetes ◽  
Artificial Neural Networks ◽  
Multilayer Perceptron ◽  
Medical Diagnosis ◽  
Healthy Person ◽  
Modeling Process ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks

Artificial neural networks are finding many uses in the medical diagnosis application. The article examines cases of renopathy in type 2 diabetes. Data are symptoms of disease. The multilayer perceptron networks (MLP) is used as a classifier to distinguish between a sick and a healthy person. The results of applying artificial neural networks for diagnose renopathy based on selected symptoms show the network's ability to recognize to recognize diseases corresponding to human symptoms. Various parameters, structures and learning algorithms of neural networks were tested in the modeling process.

scholarly journals Broad Autoencoder Features Learning for Classification Problem

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Activation Function ◽  
Activation Functions ◽  
Classification Problems ◽  
Stacked Autoencoders ◽  
Learned Features ◽  
Sigmoid Functions ◽  
Nonlinear Mappings

Activation functions such as Tanh and Sigmoid functions are widely used in Deep Neural Networks (DNNs) and pattern classification problems. To take advantages of different activation functions, the Broad Autoencoder Features (BAF) is proposed in this work. The BAF consists of four parallel-connected Stacked Autoencoders (SAEs) and each of them uses a different activation function, including Sigmoid, Tanh, ReLU, and Softplus. The final learned features can merge such features by various nonlinear mappings from original input features with such a broad setting. This helps to excavate more information from the original input features. Experimental results show that the BAF yields better-learned features and classification performances.

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