Deep Learning Approaches for Big Data Analysis

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Naomie Salim
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Learning Approaches

scholarly journals Deep learning neural network seismic big-data analysis of earthquake correlations in distinct seismic regions

2021 ◽  
Vol 8 (84) ◽  
Author(s):  
Antonios Konstantaras ◽  
Nikolaos S. Petrakis ◽  
Theofanis Frantzeskakis ◽  
Emmanouil Markoulakis ◽  
Katerina Kabassi ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Seismic Regions ◽  
Deep Learning Neural Network

Research on O2O Platform and Promotion Algorithm of Sports Venues Based on Deep Learning Technique

2018 ◽  
Vol 13 (3) ◽  
pp. 73-84
Author(s):  
Kaiyan Han ◽  
Qin Wang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Joint Distribution ◽  
Big Data Analysis ◽  
Practical Significance ◽  
Hidden Variable ◽  
Optimal Value ◽  
Learning Technique ◽  
Sports Venues

In the era of big data, intelligent sports venues have a practical significance to provide personalized service for users and build up a platform for stadium management. This article proposes a new parallel big data promotion algorithm based on the latest achievements of big data analysis. The proposed algorithm calculates the optimal value by using the observed variables Y, the hidden variable data Z, the joint distribution P (Y, Z | θ) and distribution conditions P (Z | Y | θ). The experimental results show that the proposed algorithm has higher accuracy of big data analysis, and can serve the intelligent sports venues better.

scholarly journals Prediction of Passenger Flow in Urban Rail Transit Based on Big Data Analysis and Deep Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 142272-142279 ◽  
Author(s):  
Kaer Zhu ◽  
Ping Xun ◽  
Wei Li ◽  
Zhen Li ◽  
Ruochong Zhou
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Passenger Flow ◽  
Urban Rail

Application of Deep Learning in Biological Big Data Analysis

2021 ◽  
pp. 117-148
Author(s):  
Rohit Shukla ◽  
Arvind Kumar Yadav ◽  
Tiratha Raj Singh
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Data Extraction ◽  
Basic Structure ◽  
Big Data Analysis ◽  
Biological Data ◽  
Omics Data ◽  
Hidden Information ◽  
Continuous Development

The meaningful data extraction from the biological big data or omics data is a remaining challenge in bioinformatics. The deep learning methods, which can be used for the prediction of hidden information from the biological data, are widely used in the industry and academia. The authors have discussed the similarity and differences in the widely utilized models in deep learning studies. They first discussed the basic structure of various models followed by their applications in biological perspective. They have also discussed the suggestions and limitations of deep learning. They expect that this chapter can serve as significant perspective for continuous development of its theory, algorithm, and application in the established bioinformatics domain.

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