Support Vector Machine with Tunicate Swarm Optimization Algorithm for Emotion Recognition in Human-Robot Interaction
Bibliographic record
Abstract
The rapid development of computer programs for the automatic classification of human emotions in recent years has drawn the attention of researchers. However, existing techniques have not addressed context-information included in facial expressions properly. In this research, a Tunicate Swarm Optimization Algorithm with Support Vector Machine (TSOA-SVM) to tackle the issue and enhance performance in emotion recognition. Then, ORL dataset was is used to the recommended approach to collect the data, then it was image-scaled and enhanced as part of the preparation process. Moreover, characteristics from previously processed images are extracted using Wavelet Transform and Entropy characteristics. After that, the classification stage is used to these derived traits to ascertain whether or not they include emotions. The proposed technique achieved high accuracy of 99.42%, specificity of 99.54%, and sensitivity of 99.35% in distinguishing the emotions when compared to other existing methods such as Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbour.
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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 | 0.000 | 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 | 0.000 | 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".