scholarly journals Optimization of Quantitative Financial Data Analysis System Based on Deep Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meiyi Liang
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Performance Test ◽  
Learning Algorithm ◽  
User Interaction ◽  
Financial Data ◽  
Similar Time ◽  
Financial Data Analysis ◽  
Analysis System ◽  
Data Analysis System

In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user interaction module obtains and displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results.

scholarly journals MOOCVERSITY - Deep Learning Based Dropout Prediction in MOOCs over Weeks

2020 ◽  
Vol 2 (3) ◽  
pp. 140-152 ◽  
Author(s):  
Vignesh Muthukumar ◽  
Dr. Bhalaji N.
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Prediction Model ◽  
Learning Algorithm ◽  
User Interaction ◽  
Initial Step ◽  
Individual Student ◽  
Student Dropout ◽  
Temporal Prediction ◽  
Deep Learning Algorithm

Massive Open Online Courses (MOOCs) has seen a dramatic increase of participants in the last few years with an exponential growth of internet users all around the world. MOOC allows users to attend lectures of top professors from world class universities. Despite flexible accessibility, the common trend observed in each course is that the number of active participants appears to decrease exponentially as the week’s progress. The structure and nature of the courses affects the number of active participants directly. A comprehensive review of the available literature shows that very little intensive work was done using the pattern of user interaction with courses in the field of MOOC data analysis. In this paper, we take an initial step to use the deep learning algorithm to construct the dropout prediction model and produce the predicted individual student dropout probability. Additional improvements are made to optimize the performance of the dropout prediction model and provide the course providers with appropriate interventions based on a temporal prediction mechanism. Our Exploratory Data Analysis demonstrates that there is a strong correlation between click stream actions and successful learner outcomes. Among other features, the deep learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behaviour over time.

Integrated diagnostic and data analysis system of the C-2W advanced beam-driven field-reversed configuration plasma experiment

2018 ◽  
Vol 89 (10) ◽  
pp. 10K114 ◽  
Author(s):  
M. C. Thompson ◽  
T. M. Schindler ◽  
R. Mendoza ◽  
H. Gota ◽  
S. Putvinski ◽  
...  
Keyword(s):  
Data Analysis ◽  
Field Reversed Configuration ◽  
Plasma Experiment ◽  
Analysis System ◽  
Data Analysis System
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