A Novel Approach Web Services Based Long Tail Web Services Using Deep Neural Network

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.

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ServeNet-LT: A Normalized Multi-head Deep Neural Network for Long-tailed Web Services Classification

10.1109/icws53863.2021.00025 ◽

2021 ◽

Author(s):

Jing Zhang ◽

Yang Chen ◽

Yilong Yang ◽

Changran Lei ◽

Deqiang Wang

Keyword(s):

Neural Network ◽

Web Services ◽

Deep Neural Network

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Advanced Satellite Image Classification of Various Resolution Image Using a Novel Approach of Deep Neural Network Classifier

Wireless Personal Communications ◽

10.1007/s11277-018-6024-7 ◽

2018 ◽

Vol 104 (1) ◽

pp. 357-372

Author(s):

S. Jayanthi ◽

C. Vennila

Keyword(s):

Neural Network ◽

Image Classification ◽

Deep Neural Network ◽

Satellite Image ◽

Neural Network Classifier ◽

Resolution Image ◽

Satellite Image Classification ◽

Novel Approach

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A Novel Approach of Lung Tumor Detection Using Deep Neural Network

2019 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC) ◽

10.1109/iintec48298.2019.9112100 ◽

2019 ◽

Author(s):

Noura Nafti ◽

Mohamed Othmani

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Lung Tumor ◽

Tumor Detection ◽

Novel Approach

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Deep Learning for Detecting Sleep Apnea from ECG Signals

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3054 ◽

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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