SVM-CNN Hybrid Classification for Waste Image Using Morphology and HSV Color Model Image Processing
Bibliographic record
Abstract
Waste is a significant problem that is around us.The problem occurs because waste volume speed could be faster.This problem can be solved by implementing machine learning in the waste sorting process based on two categories which are organic and inorganic.Knowing the most efficient image processing and classification machine learning model is necessary.This research uses the Support Vector Machine classification model hybridized with the Convolutional Neural Network, image processing morphology, and the HSV color model.The dataset is collected from the images available on the Kaggle website and executed using Python.The data used amounted to 25,077 with a training and test data ratio of 85:15.The data is processed using the proposed method, namely the morphology and HSV color model, to determine the performance between using the image process and those that do not.The data that has been processed is classified using the SVM-CNN Hybrid classification model.The performance results are an accuracy rate of 99.34% and a loss of 1.67% without overfitting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".