## 2 Materials and Methods
### Analysis of Particular Cases  
Table 7 presents the results of the analysis cases where the developed model has been applied. In the first case, a chatbot based on the NLP model was implemented and integrated into the website of a large online sales company. Their main task was to resolve standard queries regarding products, sales, and delivery issues. The chatbot reduced the average response time by 70% and decreased queries to human agents by 40%. Customers reported 85% satisfaction when interacting with this chatbot, highlighting its accuracy and speed. For another analysis case, it was integrated with IoT systems in a smart city. For this, the model was implemented in its IoT systems to help citizens find real-time information about traffic and parking availability, among others.  
Among other cases, it was implemented in an electronics E-commerce. However, the chatbot based on our model was designed to understand and process natural language; it faced challenges when implemented in an e-commerce site specializing in electronic products. Users often used technical terminology or specific product names not initially in the model's training corpus. This resulted in inaccurate or irrelevant responses in 25% of interactions. In this unsuccessful case, the need for the model's recalibration and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **ROC-AUC** & **MSE** \\ \hline Proposed model & 95.3\% & 94.8\% & 95.1\% & 0.989 & 0.027 \\ Deep Neural Network (DNN) & 93.1\% & 92.5\% & 92.8\% & 0.975 & 0.035 \\ Recurrent Neural Network (RNN) & 91.7\% & 90.9\% & 91.3\% & 0.963 & 0.040 \\ Decision tree & 92.5\% & 91.1\% & 91.8\% & 0.971 & 0.038 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison between machine learning models.  
retraining phase was identified, incorporating specific terminologies and better adapting to the implementation contexts. It is essential to understand that although a model may be robust in one scenario, there will always be a need for specific adaptations and adjustments depending on the application domain.  
In the case above, the 25% inaccuracy in the answers should not be considered simply as a \"failure\" but as a challenge that requires attention and adjustments. It is crucial to recognize that the effectiveness of a chatbot model can vary depending on the application domain and the nature of the interactions. It is essential to clarify that 25% inaccuracy in the answers is not equivalent to 30% correct answers. This difference underscores the need for a more nuanced assessment of implementation. Instead of automatically categorizing a result as a \"failure,\" we should look at specific areas where the model faces challenges and work on precise improvements.  
Furthermore, the need to adapt and adjust the model to address the specific demands of the implementation is highlighted. This may include incorporating technical terminology and retraining the model to improve its performance in a particular context. Understanding that even robust models may require modifications to achieve maximum effectiveness in different applications is essential.