étection et identification des activités quotidiennes dans les maisons intelligentes
Bibliographic record
Abstract
ans le cadre de ce projet de fin d’études, nous avons proposé d’utiliser deux méthodes de classification pour détecter les activités quotidiennes au sein des maisons intelligentes. Ceci dans le but est de connaitre la quantité d’électricité nécessaire pour répondre aux besoins des occupants de ces maisons intelligentes et éviter ainsi une production d’électricité qui dépasse leurs besoins c’est à dire créer une adéquation entre l’offre et la demande en terme de consommation d’électricité. Il s’agit de deux méthodes qui font parties des méthodes d’apprentissage automatique non supervisé : K-Means et DBSCAN. Les résultats obtenus sur un ensemble de données ont montré que DBSCAN fournit de bonnes performances comparée à K-Means sur une variété de distributions différentes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".