A Novel Approach for Association Mining Based on Matrix Factorization and Deep Neural Network

2020 ◽  
Author(s):  
Harvendra Kumar
Keyword(s):  
Neural Network ◽  
Matrix Factorization ◽  
Deep Neural Network ◽  
Association Mining ◽  
Novel Approach

scholarly journals Novel Approach of Neural Collaborative Filter by Pairwise Objective Function with Matrix Factorization

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1213
Author(s):  
Ram Sethuraman ◽  
Akshay Havalgi
Keyword(s):  
Neural Network ◽  
Objective Function ◽  
Matrix Factorization ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Cross Entropy ◽  
Optimization Approach ◽  
Novel Approach ◽  
Latent Features ◽  
The Matrix

The concept of deep learning is used in the various fields like text, speech and vision. The proposed work deep neural network is used for recommender system. In this work pair wise objective function is used for emphasis of non-linearity and latent features. The GMF (Gaussian matrix factorization) and MLP techniques are used in this work. The proposed framework is named as NCF which is basically neural network based collaborative filtering. The NCF gives the latent features by reducing the non-linearity and generalizing the matrix. In the proposed work combination of pair-wise and point wise objective function is used and tune by using the concept of cross entropy with Adam optimization. This optimization approach optimizes the gradient descent function. The work is done on 1K and 1M movies lens dataset and it is compared with deep matrix factorization (DMF).  

scholarly journals A Novel Approach for Efficient Training of Deep Neural Networks

2018 ◽  
Vol 11 (3) ◽  
pp. 954
Author(s):  
D.T.V. Dharmajee Rao ◽  
K.V. Ramana
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Programming Model ◽  
Optimization Technique ◽  
Matrix Multiplication ◽  
Training Algorithms ◽  
Training Time ◽  
Large Matrix ◽  
Novel Approach ◽  
Parallel Programming Model

<p style="text-indent: 1.27cm; margin-bottom: 0.35cm; line-height: 115%;" align="justify"><span style="font-family: Arial,serif;"><span style="font-size: small;"><em>Deep Neural Network training algorithms consumes long training time, especially when the number of hidden layers and nodes is large. Matrix multiplication is the key operation carried out at every node of each layer for several hundreds of thousands of times during the training of Deep Neural Network. Blocking is a well-proven optimization technique to improve the performance of matrix multiplication. Blocked Matrix multiplication algorithms can easily be parallelized to accelerate the performance further. This paper proposes a novel approach of implementing Parallel Blocked Matrix multiplication algorithms to reduce the long training time. The proposed approach was implemented using a parallel programming model OpenMP with collapse() clause for the multiplication of input and weight matrices of Backpropagation and Boltzmann Machine Algorithms for training Deep Neural Network and tested on multi-core processor system. Experimental results showed that the proposed approach achieved approximately two times speedup than classic algorithms.</em></span></span></p>

Deep Learning for Detecting Sleep Apnea from ECG Signals

2020 ◽  
Vol 10 (6) ◽  
pp. 1265-1273
Author(s):  
Lili Chen ◽  
Huoyao Xu
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Deep Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Ecg Signals ◽  
Novel Approach ◽  
Learning Machine ◽  
High Level ◽  
Electrocardiogram Ecg

Sleep apnea (SA) is a common sleep disorders affecting the sleep quality. Therefore the automatic SA detection has far-reaching implications for patients and physicians. In this paper, a novel approach is developed based on deep neural network (DNN) for automatic diagnosis SA. To this end, five features are extracted from electrocardiogram (ECG) signals through wavelet decomposition and sample entropy. The deep neural network is constructed by two-layer stacked sparse autoencoder (SSAE) network and one softmax layer. The softmax layer is added at the top of the SSAE network for diagnosing SA. Afterwards, the SSAE network can get more effective high-level features from raw features. The experimental results reveal that the performance of deep neural network can accomplish an accuracy of 96.66%, a sensitivity of 96.25%, and a specificity of 97%. In addition, the performance of deep neural network outperforms the comparison models including support vector machine (SVM), random forest (RF), and extreme learning machine (ELM). Finally, the experimental results reveal that the proposed method can be valid applied to automatic SA event detection.

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