Novel model for inhabitants prediction in smart houses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a novel model for inhabitant prediction in smart houses based on daily life activities. It uses data provided by non intrusive sensors and devices to predict the house occupant. The authors' model, named Behavior Classification Model (BCM), applies Support Vector Machines (SVM) classifier to learn the users' habits when they perform activities, and then predicts the user. BCM was tested using real data and compared with a frequency based approach. In this paper the authors present also their approach to improve the accuracy of BCM using SVM feature selection algorithm. Design/methodology/approach The model, named Behavior Classification Model (BCM), applies Support Vector Machines (SVM) classifier to learn the users' habits when they perform activities, and then predicts the user. Findings BCM was tested using real data and compared with a frequency based approach. In this paper the authors' also present their approach to improve the accuracy of BCM using SVM feature selection algorithm. Originality/value The paper is based on blind user recognition in smart homes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it