A Novel Transfer Learning with Organic Computing in Deep Learning for Stress Classification
Bibliographic record
Abstract
Nowadays, a stress classification system is essential to classify the psychological stress that impairs a person's socioeconomic life.Several Deep Learning (DL) models have been developed in recent years to classify stress using physiological signals, including electrodermal activity (EDA) and electrocardiography (ECG).However, those models cannot handle concept drift during the training phase, which may struggle to adapt to changing data patterns, leading to unreliable predictions.Concept drift refers to changes in the characteristics or patterns of physiological signals used for stress classification.These changes could be due to various factors, including shifts in the data distribution, environmental conditions, or the subjects' behavior.Therefore, this article develops a novel Deep Transfer Learning with Organic Computing (DTLOC) model by integrating the Deep Convolutional Neural Network (DCNN) with the TL and OC mechanisms to handle concept drift and improve the accuracy of stress classification.The TL brings prior knowledge about EDA and ECG features, which enhances the model's initial capabilities and shortens the learning curve.Additionally, the OC provides a self-management system that oversees the structure and operation of the model.It dynamically adapts the DCNN in response to changing data patterns, ensuring that the model remains accurate and effective in classifying stress, even in the presence of concept drift.The experimental results demonstrate that the DTLOC model, utilizing EDA and ECG data from the WESAD dataset, achieves an accuracy of 93.53%.This is a significant improvement compared to the LIBSVM, LSTM, DNN, and CNN models, with increases of 15.63%, 13.15%, 10.37%, and 5.03% respectively.Thus, this model can enhance individuals' quality of life and safety by detecting stress-related illnesses at an earlier stage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".