## Bar and Line Chart: Accuracy in Percentage vs Epochs for Different PNN Configurations
### Overview
The image is a bar and line chart comparing the accuracy (in percentage) of three different PNN (Probabilistic Neural Network) configurations (2PNN, 10PNN, and 20PNN) across varying numbers of epochs (100, 200, and 300). The chart displays the accuracy of each PNN configuration as bars, and the average accuracy of CNs (Convolutional Networks) as lines.
### Components/Axes
* **X-axis:** "Epochs" with markers at 100, 200, and 300. The x-axis is repeated for each PNN configuration.
* **Y-axis:** "Accuracy in Percentage" with markers from 97.5 to 98.5, incrementing by 0.1.
* **Legend (bottom):**
* Light gray bars: "2PNN"
* Black line: "Average-CNs"
* Medium gray bars: "10PNN"
* Dark gray bars: "20PNN"
### Detailed Analysis
**2PNN Configuration (Light Gray Bars):**
* At 100 Epochs: Accuracy is approximately 98.08%.
* At 200 Epochs: Accuracy is approximately 98.17%.
* At 300 Epochs: Accuracy is approximately 98.18%.
* Trend: The accuracy increases slightly from 100 to 200 epochs, then plateaus from 200 to 300 epochs.
**10PNN Configuration (Medium Gray Bars):**
* At 100 Epochs: Accuracy is approximately 97.76%.
* At 200 Epochs: Accuracy is approximately 98.00%.
* At 300 Epochs: Accuracy is approximately 98.05%.
* Trend: The accuracy increases from 100 to 200 epochs, then plateaus from 200 to 300 epochs.
**20PNN Configuration (Dark Gray Bars):**
* At 100 Epochs: Accuracy is approximately 97.50%.
* At 200 Epochs: Accuracy is approximately 97.92%.
* At 300 Epochs: Accuracy is approximately 97.97%.
* Trend: The accuracy increases from 100 to 200 epochs, then plateaus from 200 to 300 epochs.
**Average-CNs (Black Lines):**
* **For 2PNN:** The line starts at approximately 98.22% at 100 epochs, dips slightly to approximately 98.20% at 200 epochs, and then increases to approximately 98.24% at 300 epochs.
* **For 10PNN:** The line starts at approximately 97.85% at 100 epochs, increases to approximately 98.10% at 200 epochs, and then increases to approximately 98.14% at 300 epochs.
* **For 20PNN:** The line starts at approximately 97.50% at 100 epochs, increases to approximately 97.85% at 200 epochs, and then increases to approximately 97.95% at 300 epochs.
### Key Observations
* The 2PNN configuration consistently achieves the highest accuracy among the three PNN configurations.
* The accuracy of all PNN configurations generally increases as the number of epochs increases from 100 to 200, but the increase is less pronounced from 200 to 300 epochs.
* The Average-CNs accuracy generally increases with the number of epochs for all PNN configurations.
* The 2PNN configuration has the highest Average-CNs accuracy, while the 20PNN configuration has the lowest.
### Interpretation
The chart suggests that the 2PNN configuration is the most effective among the three PNN configurations tested, as it consistently achieves the highest accuracy. The increasing accuracy with more epochs (up to 200) indicates that the models are learning and improving their performance. The plateauing of accuracy from 200 to 300 epochs suggests that the models may be approaching their maximum potential performance, and further training may not yield significant improvements. The Average-CNs accuracy follows a similar trend, indicating that the convolutional networks are also benefiting from increased training. The differences in Average-CNs accuracy across the PNN configurations may be due to the specific characteristics of each configuration and how they interact with the convolutional networks.
