A Cloud-based Hybrid Learning System for Remote Monitoring and Optimization of E-Waste Recycling Operations
Bibliographic record
Abstract
Due to shorter product lifecycles and rising demand, e-waste, particularly WEEE, is the fastest-growing solid waste category globally, growing 3-5% yearly. Despite international limitations, illegal trading and informal recycling still take place, particularly in low-income neighbourhoods where people dispose of hazardous rubbish for profit. To get over these problems, this research proposes a system that uses the Trans-CNBiGRU paradigm to remotely monitor e-waste recycling activities. Data cleansing, feature extraction, and model training are all part of the process. After data normalisation guarantees quality, dimensionality is reduced using Principal Component Analysis (PCA). This results in a two-dimensional fusion vector that contains information at the level of both sentences and keywords. Instead, then relying on word embedding and position encoding, Trans-CNBiGRU utilises a CNN-BiGRU architecture to capture latent properties and relative positioning. The proposed model achieves a classification accuracy of 95.81%, which is higher than current techniques, according to the experimental results. Ethical e-waste recycling, detection of unauthorised disposal, and short-term remote monitoring are all aided by high accuracy. By using the model, operational control and monitoring can be enhanced in global e-waste recycling systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".