{"title":"Analyzing Youth Attitudes towards Mutual Assistance Support System based on Sensitivity Analysis of Bayesian Networks","authors":"S. Matsumoto, N. Ohhigashi","doi":"10.1109/IIAI-AAI50415.2020.00123","DOIUrl":null,"url":null,"abstract":"We have shown the concept of information sharing system to support vulnerable road users living in the suburban slope residential areas where public transport is scarce. Then we also have constructed a web service to support their daily life named MASS. The role of MASS is to facilitate the encounter between local community people and to provide the opportunity of resource sharing for solving the difficulties in daily life by mutual assistance. In order for MASS to be effective in solving the problems of vulnerable road users, mainly older people, the active participation of young people is essential because most of the resources of skills will be provided by young people. Therefore, in order to discuss the continuity of our system as a business, the previous research has conducted an attitude survey on young people’s awareness of resource sharing at their local community and analyzed it with Bayesian networks. From the analysis, the previous research has shown the relationship between the factors, which could not be clarified so far, and obtained results that support several hypotheses. However, the previous research has analyzed only the results of evaluating MASS from a subjective view and has not dealt with the survey results of evaluating MASS from an objective viewpoint. Furthermore, the strength of each explanatory variable with respect to the objective variable (MASS evaluation) was not sufficiently clear. The purpose of this study is to analyze the sensitivity of each explanatory variable for the objective variable in the constructed model of Bayesian networks and to perform inference using the model.","PeriodicalId":188870,"journal":{"name":"2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIAI-AAI50415.2020.00123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We have shown the concept of information sharing system to support vulnerable road users living in the suburban slope residential areas where public transport is scarce. Then we also have constructed a web service to support their daily life named MASS. The role of MASS is to facilitate the encounter between local community people and to provide the opportunity of resource sharing for solving the difficulties in daily life by mutual assistance. In order for MASS to be effective in solving the problems of vulnerable road users, mainly older people, the active participation of young people is essential because most of the resources of skills will be provided by young people. Therefore, in order to discuss the continuity of our system as a business, the previous research has conducted an attitude survey on young people’s awareness of resource sharing at their local community and analyzed it with Bayesian networks. From the analysis, the previous research has shown the relationship between the factors, which could not be clarified so far, and obtained results that support several hypotheses. However, the previous research has analyzed only the results of evaluating MASS from a subjective view and has not dealt with the survey results of evaluating MASS from an objective viewpoint. Furthermore, the strength of each explanatory variable with respect to the objective variable (MASS evaluation) was not sufficiently clear. The purpose of this study is to analyze the sensitivity of each explanatory variable for the objective variable in the constructed model of Bayesian networks and to perform inference using the model.