A Study on Emotion Recognition Model Incorporating Random Forest Algorithm and Its Improvement on Information System Security Performance
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Bibliographic record
Abstract
The article uses the appropriate equipment for research data, designing the face and physiological signal emotion recognition network respectively, and putting its recognition features into the random forest classifier for training in order to realize the construction work of emotion recognition model.In-depth interpretation of the random forest algorithm based emotion recognition model in the application of information systems, combined with the research data, respectively, the emotion recognition model and system safety performance testing assessment.The emotion recognition model of this paper based on the 25% retention method has a recognition rate of 96.16% for the 14dimensional B emotion features, which has the highest recognition efficacy and can well meet the system emotion recognition needs.The experimental group is found to be significantly different from the control group, and it is concluded that by introducing the emotion recognition model into the traditional information system, all three security performance indicators of the system are significantly improved.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it