Integrating Multi-Input Data in CNN-LSTM Models for AI-Based Cooking Termination in Smart Ovens
Bibliographic record
Abstract
In this study, an AI-powered smart oven system is proposed, which can automatically terminate the cooking process based on user preferences (rare, regular, or well-done). A camera-integrated oven prototype was developed for five different food types (fresh pizza, frozen pizza, tray pastry, mini pastry, and salmon), and a comprehensive dataset was created using images captured throughout the cooking process. Visual data were subjected to feature extraction using image processing techniques, and the resulting features were combined with numerical inputs such as temperature and cooking time to form the input of an LSTM-based deep learning model. The model was evaluated using both the collected dataset and real-world user scenarios, and was shown to perform with high accuracy. The developed system adapts to individual cooking preferences, prevents overcooking, and contributes to energy efficiency and reduction of food waste.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".