Enhancing Machine Learning Innovative Model for Waste Management: A Focus on Data Preprocessing and Labeling
Bibliographic record
Abstract
Efficient waste management is essential for sustainability and urban planning, especially with increasing waste complexity. While traditional methods often rely on manual processes, machine learning offers a promising approach to automate waste classification, improving accuracy and reducing costs. Building on our previous research, which utilized real-time data collection through Internet of Things (IoT) devices, this study focuses on enhancing machine learning models by improving the quality and diversity of the underlying dataset. We developed a large pre-processed dataset, initially analyzed using unsupervised clustering with the K-Means algorithm, and labeled various waste types. The dataset integrates primary data from IoT devices, secondary data from public repositories, and tertiary data from related studies, creating a comprehensive resource for model training. Key preprocessing techniques, including data cleaning, annotation, and normalization, were applied to improve data quality. Experiments with different machine learning models, such as Random Forest and Support Vector Machines, demonstrate that diverse and well-preprocessed data significantly enhances model performance, leading to better classification accuracy. This study contributes to advancing waste management systems by providing a robust dataset and insights into data preprocessing, offering a foundation for further research and practical applications in smart cities.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".