## 3 Results
### RQ5--What Technological Tools Have Been Used to Implement These Proposals? The combination of software and hardware tools presented in Table 4 reflects a clear trend toward continuous enhancement of deep learning model capabilities. Through the integration of complementary platforms and advanced computing infrastructures, researchers can address increasingly complex challenges, expanding the range of applications across various technological domains. These tools significantly improve the efficiency and adaptability of deep learning models in the analyzed proposals. The tools used in the analyzed proposals highlight a comprehensive integration of software and hardware, enabling enhanced deep learning models across multiple technological applications. Python emerged as the most widely used programming language, adopted in nearly all studies, except S25, which utilized Java, and S22, where Matlab was employed for specialized simulations. TensorFlow and Keras were the most prominent deep learning frameworks, applied in studies such as S1, S3, S5, and S9, demonstrating their robustness in model development. Meanwhile, PyTorch was used in S11 and S44, emphasizing its flexibility in dynamic graph computation. Scikit-learn played a crucial role in machine learning tasks across various studies, including S3, S4, and S5. Regarding hardware, GPUs were the most frequently used systems, facilitating high-performance training for deep learning models, particularly in S1, S3, S5, and S10. Additionally, CPUs and specialized devices, such as Raspberry Pi and Intel Core i7/i5 processors, were employed in studies focusing on IoT applications or resource-constrained environments, as seen in S14, S18, and S26. Complementary tools also played a vital role in model optimization. For instance, OpenCV was extensively used for image and video processing (e.g., S13, S29), while Wireshark and tcpdump were employed for network traffic analysis and security detection (e.g., S10, S6). These tools, along with various APIs--such as REST APIs for system integration and Blockchain APIs for secure transactions--were instrumental in enhancing model efficiency, versatility, and overall performance. RQ6--How Reliable Have These Systems Been Based on Key Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)? The effectiveness of the models trained in the analyzed studies was evaluated using multiple performance metrics, including accuracy, precision, recall, and F1-score. A summary of the results from studies where these metrics were assessed is presented in Table 5. It is important to note that several studies used multiple datasets to validate their proposals, leading to multiple values being reported for each of the aforementioned metrics. In all cases, the analysis considered the set of metrics in which accuracy was the highest. Accuracy (overall model precision; see Figure 6a) measures the percentage of correct predictions relative to the total number of predictions made. In the analyzed studies, the mean accuracy is approximately 97.44%, indicating that most models demonstrate strong overall classification performance. Additionally, the standard deviation is relatively low (4.13), suggesting that variability among models is well controlled. Notably, the best-performing studies in this metric are S32 (99.99%), S9 (99.96%), and S21 (99.74%), highlighting that architectures such as Deep Neural Networks and Gradient Boosting exc