Hybrid user action prediction system for automated home using association rules and ontology
Bibliographic record
Abstract
Nowadays, with the rapid increase of Internet users, the Internet services dominate a primary part of our lifestyle. 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’. 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. 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. 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. For the evaluation of the proposed system, two ontology graphs are introduced with FP‐growth to achieve the best results. Both graphs are tested through multiple weight values with the results of FP‐growth. As a result, the best weight distribution selected in this study is (70, 30) for time and location ontology graphs respectively. Their results showed that the proposed prediction system achieved an accuracy of 79% for all weekdays and 81% excluding weekend days.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".