Neural Network Data Processing Technology Based on Deep Belief Networks

2020 ◽  
pp. 234-244
Author(s):  
Viktor V. Krasnoproshin ◽  
Vadim V. Matskevich
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Processing Technology ◽  
Network Data ◽  
Belief Networks ◽  
Deep Belief Networks

Dual-emission CdTe/AgInS2 photoluminescence probe coupled to neural network data processing for the simultaneous determination of folic acid and iron (II)

2020 ◽  
Vol 1114 ◽  
pp. 29-41 ◽  
Author(s):  
Rafael C. Castro ◽  
David S.M. Ribeiro ◽  
Ricardo N.M.J. Páscoa ◽  
José X. Soares ◽  
Sarmento J. Mazivila ◽  
...  
Keyword(s):  
Neural Network ◽  
Folic Acid ◽  
Data Processing ◽  
Simultaneous Determination ◽  
Network Data ◽  
Dual Emission

scholarly journals 3D Virtual Reality Implementation of Tourist Attractions Based on the Deep Belief Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fuli Song
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Optimal Algorithm ◽  
Target Recognition ◽  
Tourism Industry ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Excellent Performance ◽  
Special Equipment ◽  
Tourist Attractions

In today’s society, information technology is widely used, and virtual reality technology, as one of the emerging frontier technologies, has entered a stage of rapid development. Virtual reality is the use of computer technology to simulate the real-life environment into a virtual simulation environment, with the help of special equipment to realize the natural interaction between users and technical environment, in which the tourism industry is the most widely used. In order to realize 3D virtual reality of tourist attractions and improve users’ immersive experience in the process of interaction, the deep belief neural network is introduced to realize the target recognition and reconstruction in virtual reality. The results show that the algorithm has excellent performance in target recognition and target reconstruction, and deep belief networks improve the accuracy by 0.57% and 0.81% and the accuracy by 0.21% and 2.06%, respectively, compared with the current optimal algorithm in target recognition of 12 and 20 view regular projection images. Compared with the current optimal algorithm, deep belief networks are reduced by 0.2%, 3.7%, and 0.6%, respectively. The accuracy index was increased by 2%, 0.1%, and 0.1%, respectively. The above results show that the proposed algorithm based on the deep belief neural network can realize 3D virtual reality of complex scenes such as tourist attractions according to its excellent performance.

scholarly journals A Novel Hybrid Model for Predicting Traffic Flow via Improved Ensemble Learning Combined with Deep Belief Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yikang Rui ◽  
Wenqi Lu ◽  
Ziwei Yi ◽  
Renfei Wu ◽  
Bin Ran
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Ensemble Learning ◽  
Hybrid Model ◽  
Urban Traffic ◽  
Combination Method ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Short Term ◽  
Traffic State

The intelligent transportation system (ITS) plays an irreplaceable role in alleviating urban traffic congestion and realizing sustainable urban development. Accurate and efficient short-term traffic state forecasting is a significant issue in ITS. This study proposes a novel hybrid model (ELM-IBF) to predict the traffic state on urban expressways by taking advantage of both deep learning models and ensemble learning framework. First, a developed bagging framework is introduced to combine several deep belief networks (DBNs) that are utilized to capture the complicated temporal characteristic of traffic flow. Then, a novel combination method named improved Bayesian fusion (IBF) is proposed to replace the averaging method in the bagging framework since it can better fuse the prediction results of the component DBNs by assigning the reasonable weights to DBNs at each prediction time interval. Finally, the proposed hybrid model is validated with ground-truth traffic flow data captured by the remote traffic microwave sensors installed on the multiple road sections of 2nd Ring Road in Beijing. The experimental results illustrate that the ELM-IBF method can effectively capture sharp fluctuations in the traffic flow. Compared with several benchmark models (e.g., artificial neural network, long short-term memory neural network, and DBN), the ELM-IBF model reveals better performance in forecasting single-step-ahead traffic volume and speed. Additionally, it is proved that the ELM-IBF model is capable of providing stable and high-quality results in multistep-ahead traffic flow prediction.

scholarly journals Prediksi Tingkat Kelancaran Kredit BSU BMT Tunas Harapan Syari’ah Pringgasela Kabupaten Lombok Timur Menggunakan Algoritma Neural Network

2021 ◽  
Vol 4 (2) ◽  
pp. 205-216
Author(s):  
Amri Muliawan Nur ◽  
◽  
Imam Fathurrahman ◽  
Yahya Yahya ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Processing ◽  
Statistical Data ◽  
Network Data ◽  
Mining Method ◽  
The Neural Network ◽  
Using Data ◽  
Data Processing Methods

The role of credit in a cooperative is very important. With the credit can be a source of profit for the cooperative. The cooperative was founded with the aim of prospering its members. One of the advantages is that cooperative members can apply for credit loans. To approve the proposed loan, it is necessary to analyze the credit submitted by the members. This has become one of the difficulties for several cooperatives, one of which is KSU BMT Tunas Harapan Syari'ah which is located in thePringgasela village, Pringgasela District, East Lombok Regency. The problem that often arises is that the analysis conducted is often incorrect, resulting in a prolonged bad credit in installment payments. The reason is that cooperatives always use statistical data which is sometimes inaccurate because there is no processing using data processing methods. Therefore, the neural network data mining method can be used as a tool to analyze which customers are problematic and not problematic. From the results of the research that has been done, it produces an accuracy of 96.19% and an AUC of 0.976

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