scholarly journals Design and Application of a Prediction Model for User Purchase Intention Based on Big Data Analysis

2020 ◽  
Vol 25 (3) ◽  
pp. 311-317
Author(s):  
Ruixue Zhang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Prediction Model ◽  
Purchase Intention ◽  
Big Data Analysis ◽  
Design And Application

scholarly journals Sales Volume Prediction Model for Temperature Change using Big Data Analysis

2019 ◽  
Vol 4 (1) ◽  
pp. 29-38
Author(s):  
Back Seung Hoon ◽  
홍성찬 ◽  
Jun-Ki Hong ◽  
Ji-Yeon Oh ◽  
Ji-Su Lee
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Prediction Model ◽  
Temperature Change ◽  
Big Data Analysis ◽  
Sales Volume

scholarly journals Social Big-Data Analysis of Particulate Matter, Health, and Society

2019 ◽  
Vol 16 (19) ◽  
pp. 3607 ◽  
Author(s):  
Juyoung Song ◽  
Tae Min Song
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Particulate Matter ◽  
Data Analysis ◽  
Prediction Model ◽  
Opinion Mining ◽  
Big Data Analysis ◽  
Online News ◽  
Text Documents ◽  
Social Big Data

The study collected particulate matter (PM)-related documents in Korea and classified main keywords related to particulate matter, health, and social problems using text and opinion mining. The study attempted to present a prediction model for important causes related to particulate matter by using social big-data analysis. Topics related to particulate matter were collected from online (online news sites, blogs, cafés, social network services, and bulletin boards) from 1 January 2015, to 31 May 2016, and 226,977 text documents were included in the analysis. The present study applied machine-learning analysis technique to forecast the risk of particulate matter. Emotions related to particulate matter were found to be 65.4% negative, 7.7% neutral, and 27.0% positive. Intelligent services that can detect early and prevent unknown crisis situations of particulate matter may be possible if risk factors of particulate matter are predicted through the linkage of the machine-learning prediction model.

scholarly journals Injury Risk Prediction of Aerobics Athletes Based on Big Data and Computer Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dongdong Zhu ◽  
Honglei Zhang ◽  
Yulong Sun ◽  
Haijie Qi
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Big Data ◽  
Data Analysis ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Risk Prediction ◽  
Injury Risk ◽  
Big Data Analysis ◽  
Related Factors

In recent years, competitive aerobics has been rapidly popularized and developed, and the level of sports skills has also been greatly improved. The performance of some events has gradually approached and reached the advanced level. Therefore, it is vital to invest in the quantitative analysis and cross-disciplinary comprehensive research of aerobics performance and related factors. This paper adopts big data analysis technology and computer vision technology based on convolutional neural network, according to the related theories of sports biomechanics and computer image recognition, to establish a loss risk prediction model for aerobics athletes. The approach firstly has used technology of big data analysis for analyzing the characteristics of competitive aerobics sports data. Secondly, the approach combines the convolutional neural network to visually recognize the aerobics sports images and establish a two-branch prediction model. Finally, the output can be fused to accurately diagnose and evaluate the level of physical fitness development of aerobics athletes, the focus and goal of training content are clarified, and the scientific degree of aerobics training is improved. The study can help injury risk prediction of aerobic athletes based on applications of big data and computer vision.

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