A Comparative Analysis of Early Fusion Architectures for Multimodal Gas Detection Using Machine Learning Models
Bibliographic record
Abstract
Single-sensor gas detection models often lack robustness and accuracy, hindering safety and security.To enhance the accurate classification performance data from seven sensors along with thermal camera images has been used in this study, to train the model.The dataset focuses on four classes: Smoke, Perfume, No Gas and Mixture of Smoke and Perfume.Data from various sources capture different perspectives that enhance classification of the trained model, hence, early fusion technique was adopted to combine the extracted features, for an improved feature space.The sensor data undergoes preprocessing to normalize and remove noise.VGG16 model was used to extract image features.The fused data then acted as an input for the machine learning models for classification Among the tested models (SVM, Random Forest Classifier, and KNN), the Random Forest model achieved the best validation accuracy of 96.41%, outperforming SVM (94.22%) and KNN (94.53%).This approach demonstrates the effectiveness of multi-sensor data fusion for enhanced gas detection with high accuracy, potentially improving response times and reducing false alarms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".