S. Anand, P. Padmanabham, A. Govardhan, Rajesh Kulkarni
{"title":"A socio-economic status-dependent case study on relevance and frequency-enabled trip planning model","authors":"S. Anand, P. Padmanabham, A. Govardhan, Rajesh Kulkarni","doi":"10.1504/IJSSS.2017.10004346","DOIUrl":"https://doi.org/10.1504/IJSSS.2017.10004346","url":null,"abstract":"Planning a trip not only depends on the travelling cost, time and path, but also on the socio-economic status of the traveller. This paper attempts to introduce a new trip-planning model that is able to work on real time data with multiple socio-economic constraints. The proposed trip planning model processes the real time data and it is followed by the extraction of the relevant socio-economic attributes to mine the most frequent and the feasible attribute to plan the trip. Correlation defines the relevance of the socio-economic constraints, whereas the frequent and the feasible attributes are mined using the sequential pattern mining approach. The real-time travel information of about 38,303 trips is acquired from the Indian city of Hyderabad to subject the model for experimentation. The proposed model maintains a substantial trade-off between the multiple performance metrics than the conventional models.","PeriodicalId":89681,"journal":{"name":"International journal of society systems science","volume":"9 1","pages":"29"},"PeriodicalIF":0.0,"publicationDate":"2017-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45455761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Interpreting interactions of ordinal or continuous variables in moderated regression using the zero slope comparison: tutorial, new extensions, and cancer symptom applications.","authors":"Richard B Francoeur","doi":"10.1504/IJSSS.2011.038937","DOIUrl":"https://doi.org/10.1504/IJSSS.2011.038937","url":null,"abstract":"<p><p>Moderated multiple regression (MMR) can model behaviours as multiple interdependencies within a system. When MMR reveals a statistically significant interaction term composed of ordinal or continuous variables, a follow-up procedure is required to interpret its nature and strength across the primary predictor (x) range. A follow-up procedure should probe when interactions reveal magnifier (or aggravating) effects and/or buffering (or relieving) effects that qualify the x-y relationship, especially when interpreting multiple interactions, or a complex interaction involving curvilinearity or multiple co-moderator variables. After a tutorial on the zero slope comparison (ZSC), a rarely used, quick approach for interpreting linear interactions between two ordinal or continuous variables, I derive novel extensions to interpret curvilinear interactions between two variables and linear interactions among three variables. I apply these extensions to interpret how co-occurring cancer symptoms at different levels influence one another - based on their interaction - to predict feelings of sickness malaise.</p>","PeriodicalId":89681,"journal":{"name":"International journal of society systems science","volume":"3 1-2","pages":"137-158"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJSSS.2011.038937","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30840820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}