IoT-Powered Waste Segregation System for Environmental Sustainability
Bibliographic record
Abstract
Waste segregation in India is essential to reduce landfill waste, prevent pollution, and enable efficient recycling of materials like plastic, paper, metal, glass, and cardboard. It supports environmental sustainability, boosts the economy, and protects public health. A solution using CNN and IoT devices involves deploying smart waste bins equipped with cameras and sensors that classify waste (plastic, paper, metal, glass, cardboard) using CNN algorithms. The system employs a CNN-based object detection model to accurately identify and categorize waste types in real time. Trained on a comprehensive dataset, it efficiently classifies waste materials enabling precise sorting. The IoT devices then send real-time data to waste management systems for efficient collection and recycling. The system achieved 95% accuracy in classifying cardboard, glass, metal, paper, and plastic using a CNN trained on 5587 samples, with a validation accuracy of 95%. Integrating IoT and computer vision enabled real-time waste classification and segregation effectively. Finally, the incorporation of CNN and IoT in municipal trash segregation is a possible answer to the challenges of modern garbage management.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".