For the evaluation of the proposed system, two ontology graphs are introduced with FP-growth to achieve the best results. Their proposed system simulates the human prediction actions by adding common sense data by utilizing the advantages of the ontology graph and the FP-growth to find a better solution in predicting home user actions for automated systems. In this study, the authors propose a new hybrid prediction system based on the frequent pattern (FP)-growth and ontology graphs for home automation systems.
The idea of smart homes is not a recent concept it has been in high interest by both academia and industry to make smart homes a more convenient technology for human's comfort. Moreover, the evolution of the internet of things has introduced new insights into smart platforms and devices that leads to the new vision of ‘smart homes’. Nowadays, with the rapid increase of Internet users, the Internet services dominate a primary part of our lifestyle. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.The CRISP-DM model: The new blueprint for data mining. Identifying factors that predictĪttrition among first year physiotherapy students: a retrospective analysis, M.,Potier, T., Sherwin, A., & Cassidy, E. Data Mining: Concepts and Techniques (3rded). Children and Youth Services Review, 96, 346-353. Dropout early warning systems for high school students using Computers & Electrical Engineering, 66, 541-556. (2018).ĭata mining for modeling students’ performance: A tutoring action plan to preventĪcademic dropout. Computers & Education, 133,īurgos, C., Campanario, M. Performance using educational data mining. Procedia-Social and Behavioral Sciences, 152, 1079-1086. Humor, Loneliness and Acceptance: Predictors of University Drop-out Intentions. Introduction to Business Analytics with RapidMiner studio 6.Īlkan, N. VeridianĮ-Journal, Science and Technology Silpakorn University, 5(4), 96-110. The findings can be used as a guideline to reduce drop-out rate, and this information can help board members in planning policy and strategy in the future to improve the quality of teaching and learning at the university. In addition, the drop-out rate was caused from failing in specific requirement courses such as Principles of Private Law subject, and from receiving poor grades in Principles of Public Law subject and Thai Legal History subject. The key factors that affected the drop-out of junior year and above were General Education courses having poor result in Science for Quality of Life subject and having poor or fair result in Global Society and Living subject. Freshman drop-out factor was associated with low annual income of their parents. The students who had the average GPA in Mathematics and English at a fair level (1.00-1.99) from secondary school and GPAX at a medium level (between 2.00-2.99/4.00) showed relatively high drop-out rate. The results showed that the students’ educational background was related to drop-out rate during the first and second year of their study. The data were collected from 893 undergraduate law students who studied in the program from the academic year of 2012 to 2015. In addition, the experiment was examined according to the types of admission, personal factors, educational background, and academic performance in the university. The in-depth analysis was done categorized by years of study (freshman, sophomore, junior and senior year). The experiment was conducted to examine the relationship among factors affecting overall drop-out rate. The objective of this research aimed to extract factors associated with student drop-out using association rule mining. The Faculty of Law, Surat Thani Rajabhat University faced with a number of student drop-out.
Drop Out, Data Mining, Association Rule, Factor Analysis Abstract
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