This study aimed to evaluate the comparative predictive efficacy of the SARIMA statistical model and the Prophet machine learning model for forecasting monthly traffic accidents across the 24 provinces of Ecuador, addressing a critical research gap in model selection for geographically and socioeconomically heterogeneous regions. By integrating classical time series modeling with algorithmic decomposition techniques, the research sought to determine whether a universally superior model exists or if predictive performance is inherently context-dependent. Monthly accident data from January 2013 to June 2025 were analyzed using a rolling-window evaluation framework. Model accuracy was assessed through Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics to ensure consistency and comparability across provinces. The results revealed a global tie, with 12 provinces favoring SARIMA and 12 favoring Prophet, indicating the absence of a single dominant model. However, regional patterns of superiority emerged: Prophet achieved exceptional precision in coastal and urban provinces with stationary and high-volume time series—such as Guayas, which recorded the lowest MAPE (4.91%)—while SARIMA outperformed Prophet in the Andean highlands, particularly in non-stationary, medium-to-high-volume provinces such as Tungurahua (MAPE 6.07%) and Pichincha (MAPE 13.38%). Computational instability in MAPE was noted for provinces with extremely low accident counts (e.g., Galápagos, Carchi), though RMSE values remained low, indicating a metric rather than model limitation. Overall, the findings invalidate the notion of a universally optimal model and underscore the necessity of adopting adaptive, region-specific modeling frameworks that account for local geographic, demographic, and structural factors in predictive road safety analytics.

The integration of alarm systems in environments with access to a computer network through the Internet of Things (IoT), generates weaknesses related to security; therefore, it is necessary to establish protection measures to mitigate attacks by cyber criminals, who seek to prevent the detection and creation of alerts in security systems. In this context, it is important to reduce risks to which alarm systems based on IoT are exposed, which are continuously exchanging information between sensors, the central and a global server. The objective of this research work is to implement a security protocol in an alarm center based on the Internet of Things. To get this objective, a work methodology is proposed through an analysis of prototypes that facilitate the implementation of a security protocol in the operating platform of an Alarm System, using vulnerability detection techniques and implementation of computer security mechanisms based on a literature review.

This study aimed to evaluate the comparative predictive efficacy of the SARIMA statistical model and the Prophet machine learning model for forecasting monthly traffic accidents across the 24 provinces of Ecuador, addressing a critical research gap in model selection for geographically and socioeconomically heterogeneous regions. By integrating classical time series modeling with algorithmic decomposition techniques, the research sought to determine whether a universally superior model exists or if predictive performance is inherently context-dependent. Monthly accident data from January 2013 to June 2025 were analyzed using a rolling-window evaluation framework. Model accuracy was assessed through Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics to ensure consistency and comparability across provinces. Results revealed a global tie, with 12 provinces favoring SARIMA and 12 favoring Prophet, indicating the absence of a single dominant model. However, regional patterns of superiority emerged: Prophet achieved exceptional precision in coastal and urban provinces with stationary and high-volume time series—such as Guayas, which recorded the lowest MAPE (4.91%)—while SARIMA outperformed Prophet in the Andean highlands, particularly in non-stationary, medium-to-high-volume provinces such as Tungurahua (MAPE 6.07%) and Pichincha (MAPE 13.38%). Computational instability in MAPE was noted for provinces with extremely low accident counts (e.g., Galápagos, Carchi), though RMSE values remained low, indicating a metric rather than model limitation. Overall, the findings invalidate the notion of a universally optimal model and underscore the necessity of adopting adaptive, region-specific modeling frameworks that account for local geographic, demographic, and structural factors in predictive road safety analytics.

This study aims to bridge the gap between the high predictive accuracy of machine learning models and the transparency requirements of public policies for sustainable mobility by analyzing technological adoption within the national vehicle fleet of Ecuador. To this end, a methodological framework grounded in Design Science Research (DSR) and CRISP-DM was applied to a real administrative dataset of 482,754 vehicle registration records from the Ecuadorian Internal Revenue Service (SRI, 2025), comparing the performance and interpretability of two state-of-the-art gradient boosting algorithms, XGBoost and LightGBM, for multiclass classification of internal combustion engine (ICE), hybrid, and electric vehicles (EV) under severe class imbalance. The results demonstrate that both models achieve near-perfect predictive performance, with a consolidated Macro F1-score of 0.987, confirming their robustness and suitability for large-scale, policy-relevant administrative data even when electric vehicles represent only 1.3\% of the observed fleet. Beyond global performance metrics, the integration of explainable artificial intelligence through SHAP values reveals that fiscal appraisal value and engine capacity are the primary determinants of EV adoption, while territorial factors exhibit greater influence in the case of hybrid vehicles, highlighting qualitatively distinct adoption mechanisms across technologies. These findings show that advanced ``black-box'' models can be transformed into auditable and interpretable analytical tools capable of linking predictions to concrete economic, technical, and spatial variables relevant for decision-making. The study concludes that XAI-enabled gradient boosting provides a rigorous and transparent framework to support the design of targeted fiscal incentives, fleet electrification strategies, and evidence-based transport decarbonization policies in emerging economies, where data-driven governance is critical for accelerating the transition toward sustainable mobility.