scholarly journals Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 181916-181929
Author(s):  
Aanshi Bhardwaj ◽  
Veenu Mangat ◽  
Renu Vig
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Ddos Attacks ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Well Posed

Predicting protein–protein interactions from protein sequences by a stacked sparse autoencoder deep neural network

2017 ◽  
Vol 13 (7) ◽  
pp. 1336-1344 ◽  
Author(s):  
Yan-Bin Wang ◽  
Zhu-Hong You ◽  
Xiao Li ◽  
Tong-Hai Jiang ◽  
Xing Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Deep Neural Network ◽  
Protein Sequences ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
P Protein ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Protein–protein interactions (PPIs) play an important role in most of the biological processes.

scholarly journals Construction of a Sensitive and Speed Invariant Gearbox Fault Diagnosis Model Using an Incorporated Utilizing Adaptive Noise Control and a Stacked Sparse Autoencoder-Based Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 18
Author(s):  
Cong Dai Nguyen ◽  
Alexander E. Prosvirin ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Noise Control ◽  
Vibration Characteristics ◽  
Vibration Signals ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Diagnosis Model ◽  
Adaptive Noise

Gearbox fault diagnosis based on the analysis of vibration signals has been a major research topic for a few decades due to the advantages of vibration characteristics. Such characteristics are used for early fault detection to guarantee the enhanced safety of complex systems and their cost-effective operation. There exist many fault diagnosis models that have been developed for classifying various fault types in gearboxes. However, the classification results of the conventional fault classification models degrade when they are applied to gearbox systems with multi-level tooth cut gear (MTCG) faults operating under variable shaft speeds. These conditions cause difficulty in discriminating the gear fault types. Due to the improved computational capabilities of modern systems, the application of deep neural networks (DNNs) is getting popular in a variety of research fields, such as image and natural language processing. DNNs are capable of improving the classification results even when addressing complex problems such as diagnosing gearbox MTCG faults. In this research, an adaptive noise control (ANC) and a stacked sparse autoencoder–based deep neural network (SSA-DNN) are used to construct a sensitive fault diagnosis model that can diagnose a gearbox system with MTCG fault types under varying shaft rotation speeds, despite its complicatedness. An ANC is applied to gear vibration characteristics to remove a significant level of noise along the frequency spectrum of vibration signals to fix the most fault-informative components of each fault case. Next, the autoencoder learns the gear faults characteristic features from these fault-informative components to separate the fault types considered in this study. Furthermore, the implementation of the SSA-DNN is substituted for feature extraction, feature selection, and the classification processes in traditional fault diagnosis schemes by high-performance unity. The experimental results show that the proposed model outperforms conventional methodologies with higher classification accuracy.

scholarly journals Deep Neural Network Hardware Implementation Based on Stacked Sparse Autoencoder

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 40674-40694 ◽  
Author(s):  
Maria G. F. Coutinho ◽  
Matheus F. Torquato ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Hardware Implementation ◽  
Neural Network Hardware ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

scholarly journals Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Aqeel Aslam ◽  
Cuili Xue ◽  
Yunsheng Chen ◽  
Amin Zhang ◽  
Manhua Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Feature Extraction ◽  
Early Gastric Cancer ◽  
Breath Analysis ◽  
Cancer Classification ◽  
Unlabeled Data ◽  
Softmax Classifier ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

AbstractDeep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from the breath samples. Precisely, we identified fifty peaks in each spectrum to distinguish the EGC, AGC, and healthy persons. This CAD system reduces the distance between the input and output by learning the features and preserve the structure of the input data set of breath samples. The features were extracted from the unlabeled data of the breath samples. After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and repeatable. The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result for recall, precision, and f score value, making it suitable for clinical application.

scholarly journals An Efficient Method for Detection of DDoS Attacks on the Web Using Deep Learning Algorithms

2021 ◽  
Vol 10 (4) ◽  
pp. 2821-2829
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Ddos Attacks ◽  
Problem Statement ◽  
Neural Network Approach ◽  
Learning Techniques ◽  
Attack Data ◽  
Deep Learning Neural Network

Recently, DDoS attacks is the most significant threat in network security. Both industry and academia are currently debating how to detect and protect against DDoS attacks. Many studies are provided to detect these types of attacks. Deep learning techniques are the most suitable and efficient algorithm for categorizing normal and attack data. Hence, a deep neural network approach is proposed in this study to mitigate DDoS attacks effectively. We used a deep learning neural network to identify and classify traffic as benign or one of four different DDoS attacks. We will concentrate on four different DDoS types: Slowloris, Slowhttptest, DDoS Hulk, and GoldenEye. The rest of the paper is organized as follow: Firstly, we introduce the work, Section 2 defines the related works, Section 3 presents the problem statement, Section 4 describes the proposed methodology, Section 5 illustrate the results of the proposed methodology and shows how the proposed methodology outperforms state-of-the-art work and finally Section VI concludes the paper.

PEMFC Residual Life Prediction Using Sparse Autoencoder-Based Deep Neural Network

2019 ◽  
Vol 5 (4) ◽  
pp. 1279-1293 ◽  
Author(s):  
Jiawei Liu ◽  
Qi Li ◽  
Ying Han ◽  
Guorui Zhang ◽  
Xiang Meng ◽  
...  
Keyword(s):  
Neural Network ◽  
Life Prediction ◽  
Deep Neural Network ◽  
Residual Life ◽  
Sparse Autoencoder ◽  
Residual Life Prediction

Open-circuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network

2018 ◽  
Vol 311 ◽  
pp. 1-10 ◽  
Author(s):  
Lin Xu ◽  
Maoyong Cao ◽  
Baoye Song ◽  
Jiansheng Zhang ◽  
Yurong Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Open Circuit ◽  
Sparse Autoencoder ◽  
Circuit Fault Diagnosis ◽  
Power Rectifier
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