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Confusion matrix_
| Predictive Positive | Predictive Negative | |
|---|---|---|
| Actual Positive | True Positive (TP) | False Negative (FN) |
| Actual Negative | False Positive (FP) | True Negative (TN) |
Outcomes from prior studies performances_
| Authors | Collection of Datasets (Samples) | Methods that are applied | Model Performance |
|---|---|---|---|
| Cinar et al., 2020 | Used the system’s camera to snap images of the raisins. | LR, MLP and SVM | 86.44% |
| Dirik et al., 2023 | 900 raising grains and 2 classes | KNN, RT and PSO-ANN | 100% |
| Karimi et al., 2017 | 1400 images of raisins and top 50 features | ANN and SVM | 92.71% |
| Mollazade et al., 2012 | Four different types of raisins’ color pictures were analyzed, yielding 36 colors and 8 form attributes | ANN, SVM, DT and NB | 96.33% |
| Omid et al., 2010 | Obtaining raisins’ size and color characteristics using classification techniques | Image Processing Technique | 96% |
| Tarakci et al., 2021 | Database for machine learning at UCI different | KNN and WKNN | 91.70% |
| Yavuj et al., 2023 | Raisin dataset 2022 from UCI machine Learning repository | RF and DT | 85.44% |
| Yu et al., 2011 | Separated the data into four groups based on appearance, texture, and wrinkling | SVM | 95% |
| Koklu et al. 2021 | Images of pumpkin seeds | LR, MLP, SVM, RF, KNN | 86.64% |
j_ijmce-2024-0001_tab_007
| Start |
| Require: n ≥ 0 data set collection |
| Ensure: Training of machine learning classifier |
| while Evaluation of classifier with dataset? do |
| if Yes then |
| Prediction for raisin grains based on machine learning |
| else if No then |
| Parameter tuning |
| end if |
| end while |
The metric used in evaluating the performance of machine and deep learning classifiers_
| Performance Measure Name | Formula |
|---|---|
| Correct Classification Rate |
|
| Precision |
|
| Recall |
|
| F1-score |
|
| True Positive Rate |
|
| False Positive Rate |
|
| Specificity |
|
| Negative Predictive Value |
|
Names and descriptions of dataset attributes_
| Attribute Name | Attribute Description |
|---|---|
| Area | Determine how many pixels are contained within the raisin and return that value. |
| MajorAxisLength | The maximum length of a line that can be drawn on a raisin. |
| MinorAxisLength | The minimum length of a line that can be drawn on a raisin. |
| Eccentricity | An ellipse, which shares the same moments as a raisin, can be described by its degree of roundness. |
| ConvexArea | Provide the size in pixels of the smallest convex shell that contains the raisin region. |
| Extent | Provide the fraction of the bounding box’s pixels that are within the raisin region. |
| Perimeter | Its circumference can be determined by measuring the distance between the pixels that make up the raisin’s circumference. |
| Class | Kecimen and Besni raisin. |
Model performance with a 10-fold cv_
| Model Name | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) | AUC-ROC score (%) |
|---|---|---|---|---|---|
| Support Vector Machine | 87.78% | 90.37% | 85.92% | 88.00% | 87.88% |
| Decision Tree | 86.3% | 91.85% | 82.67% | 87.02% | 86.75% |
| Logistic Regression | 87.41% | 88.15% | 86.86% | 87.5% | 87.42% |
| Naive Bayes | 84.81% | 90.37% | 81.33% | 85.62% | 85.25% |
| K-nearest Neighbours | 87.04% | 90.37% | 84.72% | 87.46% | 87.20% |
| Random Forest | 86.30% | 88.15% | 85.00% | 86.55% | 86.35% |
| AdaBoost | 90.30% | 87.41% | 85.51% | 86.45% | 86.31% |
| XgBoost | 83.70% | 85.19% | 82.73% | 83.95% | 83.73% |
| LightGBM | 98.40% | 97.41% | 89.19% | 93.10% | 92.57% |
| Convolution Neural Net. | 81.35% | 83.15% | 79.08% | 81.23% | - |
| Radial Basis Function Net. | 83.41% | 84.57% | 81.73% | 83.78% | - |
| Recurrence Neural Net. | 73.94% | 74.52% | 72.50% | 73.11% | - |
| Artificial Neural Net. | 65.00% | 67.01% | 64.86% | 65.35% | - |
| Deep Neural Net. | 69.00% | 70.56% | 67.19% | 68.89% | - |
Model performance with a 5-fold cv_
| Model Name | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) | AUC-ROC score (%) |
|---|---|---|---|---|---|
| Support Vector Machine | 88.52% | 91.85% | 86.11% | 88.88% | 88.69% |
| Decision Tree | 85.93% | 88.89% | 83.92% | 86.33% | 86.05% |
| Logistic Regression | 86.67% | 88.89% | 88.37% | 86.95% | 86.74% |
| Naive Bayes | 85.93% | 91.11% | 82.55% | 86.61% | 86.32% |
| K-nearest Neighbours | 86.30% | 91.11% | 83.11% | 86.92% | 86.64% |
| Random Forest | 85.56% | 89.63% | 82.88% | 86.12% | 85.79% |
| AdaBoost | 89.15% | 92.59% | 85.03% | 82.41% | 88.45% |
| XgBoost | 83.33% | 87.41% | 80.82% | 83.98% | 83.56% |
| LightGBM | 96.31% | 97.11% | 88.21% | 93.35% | 91.83% |
| Convilution Neural Net. | 78.51% | 82.22% | 75.54% | 78.91% | - |
| Radial Basis Function Net. | 81.11% | 83.31% | 79.02% | 82.37% | - |
| Recurrence Neural Net. | 71.29% | 74.10% | 68.53% | 67.40% | - |
| Artificial Neural Net. | 64.81% | 65.34% | 62.13% | 63.92% | - |
| Deep Neural Net. | 68.27% | 70.59% | 66.19% | 67.78% | - |