## 4 Results and Discussion  
The performance results of the algorithms in Table 2 are evaluated based on different metrics. In this evaluation, metrics such as accuracy, precision, recall, and F1-Score were taken into consideration. These metrics show the performance of the algorithms on different data distributions and classification tasks. When the performances of the algorithms in the block are evaluated as a whole, it is observed that each of them offers certain advantages and disadvantages. Although Naive Bayes successfully detects positive classes with an accuracy rate of 93.33% and a recall rate as high as 95.37%, it shows a precision of 91.96%, which may limit its generalizability. In contrast, SVM performs similarly with an accuracy of 93.81% and a recall of 96.30% but performs slightly better in reducing the impact of false positive classes with a precision of 92.04%. Although the K-NN algorithm provides an adequate result with 88.10% accuracy and 90.74% recall, it may be disadvantageous in larger datasets due to its 86.73% precision \begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithms** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Naive Bayes & 0.9333 & 0.9196 & 0.9537 & 0.9364 \\ \hline SVM & 0.9381 & 0.9204 & 0.9630 & 0.9412 \\ \hline K-NN & 0.8810 & 0.8673 & 0.9074 & 0.8869 \\ \hline Decision Tree & 0.8190 & 0.8500 & 0.7870 & 0.8173 \\ \hline Random Forest & 0.8952 & 0.8909 & 0.9074 & 0.8991 \\ \hline LSTM & 0.8952 & 0.9479 & 0.8426 & 0.8922 \\ \hline DNN & 0.8952 & 0.9388 & 0.8519 & 0.8932 \\ \hline BERT & 0.9571 & 0.9901 & 0.9259 & 0.9569 \\ \hline Word Embeddings with LSTM & 0.8810 & 0.9368 & 0.8241 & 0.8768 \\ \hline TF-IDF Vectorize & 0.9333 & 0.9123 & 0.9630 & 0.9369 \\ \hline CNN & 0.9095 & 0.9320 & 0.8889 & 0.9100 \\ \hline GPT & 0.9619 & 0.9717 & 0.9537 & 0.9626 \\ \hline DistilBERT & 0.9286 & 0.9266 & 0.9352 & 0.9309 \\ \hline DistilBERT + CNN/LSTM & 0.9476 & 0.9802 & 0.9167 & 0.9474 \\ \hline T5 + GNN & 0.5143 & 0.5143 & 1.0000 & 0.6792 \\ \hline RAG & 0.9238 & 0.9510 & 0.8981 & 0.9238 \\ \hline RAG + LSTM & 0.9333 & 0.9608 & 0.9074 & 0.9333 \\ \hline
**RAG + ECE** & **0.9767** & **0.9771** & **0.9767** & **0.9767** \\ \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of the algorithms. and high computational cost. Similarly, Decision Tree performs relatively poorly with an accuracy of 81.90% and a recall rate of 78.70%, indicating that it has difficulties in distinguishing negative classes. On the other hand, Random Forest offers a more balanced performance with 89.52% accuracy and 90.74% recall rate, but it is notable for its increased computational cost due to the combination of multiple decision trees. While LSTM and DNN present similar results with 89.52% accuracy, respectively, LSTM's 94.79% precision value highlights its strength in learning long-term dependencies, while its 84.26% recall value suggests that it may miss some false negatives. In contrast, DNN may be more sensitive to class imbalances with 93.88% accuracy and 85.19% recall. While BERT stands out with 95.71% accuracy and 99.01% precision, its applicability in larger datasets may be limited due to its high computational cost. DistilBERT, which stands out with its lightweight structure, offers a success that is close to BERT, with 92.86% accuracy and 93.52% recall rate, but can produce faster results. However, the combination of DistilBERT + CNN/LSTM shows a superior performance by combining contextual understanding with spatial features with 94.76% accuracy and 98.02% precision. GPT stands out as one of the most powerful models in the text classification and generation tasks with 96.19% accuracy, 97.17% precision, and 95.37% recall rates. The TF-IDF Vectorization method can be considered as an effective tool especially in feature extraction and the representation of text data with 93.33% accuracy and a 96.30% recall rate. CNN, with 90.95% accuracy and balanced precision (93.20%) and recall (88.89%), is particularly successful in the extraction of spatial features in visual data.