scholarly journals Questionnaire Investigation on Tourists’ Behavior and its Sensibility Analysis Utilizing Bayesian Network

2018 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Akane Okubo ◽  
Tsuyosi Aburai ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Local Government ◽  
Bayesian Network ◽  
Causal Relationship ◽  
Back Propagation ◽  
Izu Peninsula ◽  
Regional Collaboration ◽  
Cherry Blossom ◽  
Sensibility Analysis ◽  
Tourism Service

Tourists from abroad are increasing rapidly in Japan. Kawazu town in Izu Peninsula is famous for its cherry trees. In the cherry blossom season, many tourists visit this town. In order to get much more visitors, tourists’ behavior should be investigated much further. The Kawazu Cherry Blossom Festival was carried out in February 2015. Our research investigation was performed during that period. In this paper, a questionnaire investigation is executed in order to clarify tourists’ behavior, and to seek the possibility of developing regional collaboration among local government, tourism related industry and visitors. In this research, we construct the model utilizing Bayesian Network and causal relationship is sequentially chained by the characteristics of travelers, an objective to visit Izu Peninsula in Japan and the main occasion to visit them. We analyzed them by sensitivity analysis and some useful results were obtained. Sensitivity analysis is performed by back propagation method. We have presented the paper concerning this. But the volume becomes too large, therefore we have split them and this paper shows the latter half of the investigation result by setting evidence to Bayesian Network items. These are utilized for constructing a much more effective and useful tourism service. We have obtained fruitful results. To confirm the findings by utilizing the new consecutive visiting records would be the future works to be investigated.

scholarly journals Hypothesis Testing for the Questionnaire Investigation on Tourists’ Behavior

2017 ◽  
Vol 10 (12) ◽  
pp. 68
Author(s):  
Akane Okubo ◽  
Yuki Higuchi ◽  
Kazuhiro Takeyasu
Keyword(s):  
Local Government ◽  
Hypothesis Testing ◽  
Izu Peninsula ◽  
Regional Collaboration ◽  
Cherry Blossom ◽  
Cherry Trees

Tourists from abroad are increasing rapidly in Japan. Kawazu town in Izu Peninsula is famous for its cherry trees. In the cherry blossom season, many tourists visit this town. The Kawazu Cherry Blossom Festival was carried out in February 2015. Our research investigation was performed during that period. In this paper, a questionnaire investigation is executed in order to clarify tourists’ behavior, and to seek the possibility of developing regional collaboration among local government, tourism related industry and visitors. Hypothesis testing was executed based on that. We have set 10 Null hypotheses. In the hypothesis testing, 6 cases out of 10 null hypotheses were rejected and the majority of hypotheses were insisted clearly. We have obtained fruitful results.

scholarly journals Bayesian Network Analysis for the Questionnaire Investigation on the Needs at Fuji Shopping Street Town

2018 ◽  
Vol 9 (2) ◽  
pp. 46
Author(s):  
Tsuyoshi Aburai ◽  
Akane Okubo ◽  
Daisuke Suzuki ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Network Analysis ◽  
Bayesian Network ◽  
Causal Relationship ◽  
Back Propagation ◽  
The Future

Shopping streets at local city in Japan became old and are generally declining. In this paper, we handle the area rebirth and/or regional revitalization of shopping street. We focus on Fuji city in Japan. Four big festivals are held at Fuji city (two for Fuji Shopping Street Town and two for Yoshiwara Shopping Street Town). Many people visit these festivals including residents in that area. Therefore a questionnaire investigation to the residents and visitors is conducted during these periods in order to clarify residents and visitors’ needs for the shopping street, and utilize them to the plan building of the area rebirth and/or regional revitalization of shopping street. There is a big difference between Fuji Shopping Street Town and Yoshiwara Shopping Street Town. Therefore we focus Fuji Shopping Street Town in this paper. These are analyzed by using Bayesian Network. The analysis utilizing Bayesian Network enabled us to visualize the causal relationship among items. Furthermore, sensitivity analysis brought us estimating and predicting the prospective visitors. Sensitivity analysis is performed by back propagation method. These are utilized for constructing a much more effective and useful plan building. We have obtained fruitful results. To confirm the findings by utilizing the new consecutive visiting records would be the future works to be investigated.

scholarly journals Bayesian Network Analysis for the Questionnaire Investigation on the Needs at Yoshiwara Shopping Street in Fuji City

2018 ◽  
Vol 9 (1) ◽  
pp. 211
Author(s):  
Kazuhiro Takeyasu ◽  
Tsuyosi Aburai ◽  
Akane Okubo ◽  
Daisuke Suzuki
Keyword(s):  
Sensitivity Analysis ◽  
Network Analysis ◽  
Bayesian Network ◽  
Causal Relationship ◽  
Back Propagation ◽  
The Future

Shopping streets at local city in Japan became old and are generally declining. In this paper, we handle the area rebirth and/or regional revitalization of shopping street. We focus on Fuji city in Japan. Four big festivals are held at Fuji city (two for Fuji Shopping Street and two for Yoshiwara Shopping Street). Many people visit these festivals including residents in that area. Therefore a questionnaire investigation to the residents and visitors is conducted during these periods in order to clarify residents and visitors’ needs for the shopping street, and utilize them to the plan building of the area rebirth and/or regional revitalization of shopping street. There is a big difference between Fuji Shopping Street and Yoshiwara Shopping Street. Therefore we focus Yoshiwara Shopping Street in this paper. These are analyzed by using Bayesian Network. The analysis utilizing Bayesian Network enabled us to visualize the causal relationship among items. Furthermore, sensitivity analysis brought us estimating and predicting the prospective visitors. Sensitivity analysis is performed by back propagation method. These are utilized for constructing a much more effective and useful plan building. We have obtained fruitful results. To confirm the findings by utilizing the new consecutive visiting records would be the future works to be investigated.

scholarly journals Questionnaire Investigation on the Needs at Fuji City and its Sensibility Analysis Utilizing Bayesian Network

2019 ◽  
Vol 11 (2) ◽  
pp. 125
Author(s):  
Tsuyoshi Aburai ◽  
Akane Okubo ◽  
Daisuke Suzuki ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Network ◽  
Causal Relationship ◽  
Surrounding Area ◽  
Network Sensitivity ◽  
The Future ◽  
Sensibility Analysis

Shopping streets at local city in Japan became old and are generally declining. In this paper, we handle the area rebirth and/or regional revitalization of shopping street. We focus on Fuji city in Japan. Four big festivals are held at Fuji city. Many people visit these festivals including residents in that area. Therefore a questionnaire investigation to the residents and visitors is conducted during these periods in order to clarify residents and visitors’ needs for the shopping street, and utilize them to the plan building of the area rebirth and/or regional revitalization of shopping street. These are analyzed by using Bayesian Network. Sensitivity analysis is also conducted. As there are so many items, we focus on “The image of the surrounding area at this shopping street” and pick up former half and make sensitivity analysis in this paper. The analysis utilizing Bayesian Network enabled us to visualize the causal relationship among items. Furthermore, sensitivity analysis brought us estimating and predicting the prospective visitors. These are utilized for constructing a much more effective and useful plan building. We have obtained fruitful results. To confirm the findings by utilizing the new consecutive visiting records would be the future works to be investigated.

scholarly journals Bayesian Network Analysis for the Questionnaire Investigation on the Impression at Fuji City

2018 ◽  
Vol 9 (6) ◽  
pp. 1
Author(s):  
Daisuke Suzuki ◽  
Akane Okubo ◽  
Tsuyosi Aburai ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Network Analysis ◽  
Bayesian Network ◽  
Causal Relationship ◽  
The Future

Shopping streets at local city in Japan became old and are generally declining. In this paper, we handle the area rebirth and/or regional revitalization of shopping street. We focus on Fuji city in Japan. Four big festivals are held at Fuji city. Many people visit these festivals including residents in that area. Therefore a questionnaire investigation to the residents and visitors is conducted during these periods in order to clarify residents and visitors’ needs for the shopping street, and utilize them to the plan building of the area rebirth and/or regional revitalization of shopping street. In this paper, we mainly focus the impression the visitors feel and analyze them. These are analyzed by using Bayesian Network. The analysis utilizing Bayesian Network enabled us to visualize the causal relationship among items. Furthermore, sensitivity analysis brought us estimating and predicting the prospective visitors. These are utilized for constructing a much more effective and useful plan building. We have obtained fruitful results. To confirm the findings by utilizing the new consecutive visiting records would be the future works to be investigated.

scholarly journals An Analysis of the Questionnaire Investigation on Tourists’ Behavior

2017 ◽  
Vol 6 (4) ◽  
pp. 16
Author(s):  
Akane Okubo ◽  
Kazuhiro Takeyasu
Keyword(s):  
Local Government ◽  
Hypothesis Testing ◽  
Regional Cooperation ◽  
Economic Aspects ◽  
Local Society ◽  
Tourist Behavior ◽  
Regional Collaboration ◽  
The Common ◽  
Common Problems ◽  
And Migration

Tourists from abroad are increasing rapidly in Japan. Particular aims of local government are to overcome the common problems of an aging population and declining birthrate through tourism-generated income and to stimulate the local society through regional exchange and migration. In order to analyze economic aspects of tourism, accurate and up-to-date statistics and information regarding tourism are needed. Specifically, this study presents opportunities for inter-regional cooperation in marketing, in light of studies of tourist behavior at events featuring seasonal flowers and held in Kawazu town, which is located on the Izu Peninsula in Shizuoka Prefecture. In this paper, a questionnaire investigation is executed in order to clarify tourists’ behavior, and to seek the possibility of developing regional collaboration among local government, tourism related industry and visitors. Hypothesis testing was executed based on that. Some interesting and instructive results were obtained.

scholarly journals KAUSALITAS PENERIMAAN, PENGELUARAN DAN PDRB PROVINSI DI INDONESIA 2010 – 2019

2021 ◽  
Vol 3 (2) ◽  
pp. 47
Author(s):  
Alno Sardi Putra ◽  
Ali Anis
Keyword(s):  
Local Government ◽  
Hypothesis Testing ◽  
Causal Relationship ◽  
Government Spending ◽  
Government Expenditure ◽  
Secondary Data ◽  
Government Revenue ◽  
The Third ◽  
Testing Stage ◽  
Local Government Expenditure

This study has three main objectives, namely, first to find out how the causal relationship between local government revenue and local government expenditure in provinces in Indonesia, the second objective is to find out how the causal relationship between local government expenditure and GRDP in provinces in Indonesia. Meanwhile, the third objective is to determine the causal relationship between local government revenue and GRDP in provinces in Indonesia. In this study, the objects in this study are 33 provinces throughout Indonesia. The data used are from 2010 to 2019. The data used are secondary data obtained from the Central Statistics Agency (BPS). The analytical method used is the VAR (Vector Auto Regression) time series analysis and the cluasaility granger test. which is processed using the help of Eviews. Based on the results of hypothesis testing, it shows that: (1) There is no causal relationship between local government revenue and local government expenditure in 33 provinces in Indonesia, but what is formed is a one-way relationship between government revenue and local government expenditure in 33 Indonesian provinces. In the hypothesis testing stage (2) there is no causal relationship between local government spending and GRDP in 33 provinces in Indonesia, in the analysis stage there is no one-way or two-way relationship between government spending and GRDP. Thus the hypothesis is rejected, while the results of hypothesis testing (3) There is no causal relationship between local government revenue and GRDP in 33 provinces in Indonesia. In the analysis stage, there is no one-way or two-way relationship between each variable. Thus the third hypothesis is rejected.

scholarly journals Causal Association Between Heart Failure and Alzheimer’s Disease: A Two-Sample Bidirectional Mendelian Randomization Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Chenglin Duan ◽  
Jingjing Shi ◽  
Guozhen Yuan ◽  
Xintian Shou ◽  
Ting Chen ◽  
...  
Keyword(s):  
Heart Failure ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sensitivity Analysis ◽  
Causal Relationship ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Relationships ◽  
Simple Mode

Background: Traditional observational studies have demonstrated an association between heart failure and Alzheimer’s disease. The strengths of observational studies lie in their speed of implementation, cost, and applicability to rare diseases. However, observational studies have several limitations, such as uncontrollable confounders. Therefore, we employed Mendelian randomization of genetic variants to evaluate the causal relationships existing between AD and HF, which can avoid these limitations.Materials and Methods: A two-sample bidirectional MR analysis was employed. All datasets were results from the UK’s Medical Research Council Integrative Epidemiology Unit genome-wide association study database, and we conducted a series of control steps to select the most suitable single-nucleotide polymorphisms for MR analysis, for which five primary methods are offered. We reversed the functions of exposure and outcomes to explore the causal direction of HF and AD. Sensitivity analysis was used to conduct several tests to avoid heterogeneity and pleiotropic bias in the MR results.Results: Our MR studies did not support a meaningful causal relationship between AD on HF (MR-Egger, p = 0.634 > 0.05; weighted median (WM), p = 0.337 > 0.05; inverse variance weighted (IVW), p = 0.471 > 0.05; simple mode, p = 0.454 > 0.05; weighted mode, p = 0.401 > 0.05). At the same time, we did not find a significant causal relationship between HF and AD with four of the methods (MR-Egger, p = 0.195 > 0.05; IVW, p = 0.0879 > 0.05; simple mode, p = 0.170 > 0.05; weighted mode, p = 0.110 > 0.05), but the WM method indicated a significant effect of HF on AD (p = 0.025 < 0.05). Because the statistical powers of IVW and MR-Egger are more than that of WM, we think that there is no causal effect of HF on AD. Sensitivity analysis and horizontal pleiotropy were not detected in the MR analysis.Conclusion: Our results did not provide significant evidence indicating any causal relationships between HF and AD in the European population. Therefore, more large-scale datasets or datasets related to similar factors are expected for further MR analysis.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

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