Comparative Code Structure Analysis using Deep Learning for Performance Prediction

Abstract Internet of things (IoT) has increased an importance for an area of interest in many devices. Then, the applications such as sensitive home sensors, medical devices, wireless sensors,and other devices are related to IoT network. The transmission of big data is subject to a possible attack that could cause network interruptions and problems with security. The security performance prediction is important for IoT networks to address complicated security issues in real-time which one of the attacks can freely threaten its global performance. Initially,investigate the safety performance of security intelligent prediction techniques is linking with deep learning algorithms into the IoT security risks. This contribution provides a CNN model that improves IoT security risk assessment (SRA) performance. Then, the access control techniques are changed with IoT-like dynamic systems with the number of items spread all over the place. Therefore, dynamic access control models are necessary. Thesedesign not individual use strategies of access but incorporate environmental and real-time data to predict the decision on access. The risk-based access control approach is one of those dynamic models. To decide the access decision, this model assesses the security risk value associated with the access request. This assessment of the model proposed results from the performance and accuracy of IoT networks.

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Mining Product Reviews: Market Structure Analysis Using Deep Learning and Evolutionary Clustering

SSRN Electronic Journal ◽

10.2139/ssrn.2724124 ◽

2016 ◽

Cited By ~ 1

Author(s):

Feng Mai ◽

Xin (Shane) Wang ◽

David J. Curry ◽

Roger H.L. Chiang

Keyword(s):

Deep Learning ◽

Market Structure ◽

Structure Analysis ◽

Product Reviews ◽

Evolutionary Clustering ◽

Market Structure Analysis

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Integrating Deep Learning Architecture into Matrix Factorization for Student Performance Prediction

10.1007/978-3-030-91387-8_26 ◽

2021 ◽

pp. 408-423

Author(s):

Thanh-Nhan Huynh-Ly ◽

Huy-Thap Le ◽

Nguyen Thai-Nghe

Keyword(s):

Deep Learning ◽

Student Performance ◽

Performance Prediction ◽

Matrix Factorization

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Aerodynamic Prediction on the Off-Design Performance of a S-CO2 Turbine Based on Deep Learning

10.1115/gt2021-60056 ◽

2021 ◽

Author(s):

Yuqi Wang ◽

Tianyuan Liu ◽

Di Zhang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Performance Prediction ◽

Aerodynamic Performance ◽

Operating Conditions ◽

Training Data ◽

Brayton Cycle ◽

Data Set ◽

Set Size ◽

The Mean

Abstract The research on the supercritical carbon dioxide (S-CO2) Brayton cycle has gradually become a hot spot in recent years. The off-design performance of turbine is an important reference for analyzing the variable operating conditions of the cycle. With the development of deep learning technology, the research of surrogate models based on neural network has received extensive attention. In order to improve the inefficiency in traditional off-design analyses, this research establishes a data-driven deep learning off-design aerodynamic prediction model for a S-CO2 centrifugal turbine, which is based on a deep convolutional neural network. The network can rapidly and adaptively provide dynamic aerodynamic performance prediction results for varying blade profiles and operating conditions. Meanwhile, it can illustrate the mechanism based on the field reconstruction results for the generated aerodynamic performance. The training results show that the off-design aerodynamic prediction convolutional neural network (OAP-CNN) has reduced the mean and maximum error of efficiency prediction compared with the traditional Gaussian Process Regression (GPR) and Artificial Neural Network (ANN). Aiming at the off-design conditions, the pressure and temperature distributions with acceptable error can be obtained without a CFD calculation. Besides, the influence of off-design parameters on the efficiency and power can be conveniently acquired, thus providing the reference for an optimized operation strategy. Analyzing the sensitivity of AOP-CNN to training data set size, the prediction accuracy is acceptable when the percentage of training samples exceeds 50%. The minimum error appears when the training data set size is 0.8. The mean and maximum errors are respectively 1.46% and 6.42%. In summary, this research provides a precise and fast aerodynamic performance prediction model in the analyses of off-design conditions for S-CO2 turbomachinery and Brayton cycle.

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