Julio Barzola-Monteses

Automated vehicular traffic management in Latin American cities requires solutions capable of capturing, processing, and visualizing events through measurable operational update intervals. Conventional automatic number plate recognition (ANPR) pipelines can return plate text while leaving the visual adequacy of the associated crop unassessed and disconnected from downstream cloud persistence, alerts, and monitoring. Because this separation can propagate visually unreliable evidence into operational records, a unified workflow is needed to evaluate plate-crop legibility before the event is exposed to operators. This work presents a three-phase methodology, applied to the SAVH system (Sistema de Aforo Vehicular, Vehicle Counting System), which integrates a Dahua ANPR camera (Zhejiang Dahua Vision Technology Co., Ltd., Hangzhou, China) with a cloud architecture on Amazon Web Services (AWS). The camera captures the vehicle and performs textual reading of the license plate, sending vehicle notifications directly to the FastAPI service deployed on Amazon EC2. This service extracts the visual evidence and runs the YOLOv8n classification variant (YOLOv8n–CLS), which classifies the legibility of the plate crop into two classes: legible plate and non-legible plate. Structured events and visual evidence are persisted in managed storage services, and a serverless function serves the web dashboard queries through an application programming interface (API) managed by Amazon API Gateway. The model was trained on a relabeled dataset derived from the public LPLCv2 collection, split into training, validation, and test subsets. Evaluation on 720 independent images from the test set achieved 97.78% overall accuracy, with 97.75% macro precision, 97.82% macro recall, 97.78% macro F1-score, and an AUC of 0.9981. External validation on 2000 real Guayaquil images achieved 86.10% accuracy, 85.84% macro F1-score, and an AUC of 0.9742. The external results show a performance gap consistent with domain shift and motivate cautious interpretation of deployment results. The contribution is the integration and evaluation methodology rather than a new neural architecture: YOLOv8n–CLS, FastAPI, and the AWS services are existing components assembled into a documented operational workflow.

Shrimp farming is a strategic productive activity for Ecuador; however, pond inspection still substantially depends on manual observation and fragmented visual records. This study describes a prototype mobile/web architecture for image capture, storage, and result visualization and, as a separate experiment, evaluates supervised multiclass instance segmentation on public proxy data. A unified dataset of 4508 images was constructed from three external sources using the classes foam, floater, and shrimp. YOLOv8s-seg was used as the internal reference baseline and YOLOv11s-seg as the comparison candidate; both were trained under the same configuration and evaluated on 445 test images. YOLOv8s-seg achieved mAP50 values of 0.678 for BOX and 0.621 for MASK, whereas YOLOv11s-seg achieved 0.677 and 0.616, respectively. Their isolated GPU inference times were 10.4 and 10.2 ms/img. A weighted global experimental performance index comprising 90% predictive quality and 10% inference efficiency reached 0.596 and 0.593, respectively. Both models performed strongly for floater and shrimp, whereas foam showed low recall and zero MASK mAP50 because of the small number of positive test images and heterogeneous annotations. The evaluated task is supervised segmentation of proxy visual categories rather than anomaly detection in its conventional methodological sense, and no general improvement or practical superiority of one architecture was demonstrated. The study does not calibrate an operational confidence threshold or alert-persistence rule, implement adaptive or online learning, validate images from Ecuadorian production ponds, deploy the unified detector within the API, or demonstrate end-to-end real-time monitoring. Consequently, the results constitute a controlled experimental baseline and must not be interpreted as evidence of operational performance or local-domain generalization.

The sustained growth of urban areas has increased the complexity of managing services, infrastructure, and mobility, creating a need for advanced technological solutions capable of responding dynamically to rapidly changing environments. In this context, adaptive smart urban systems have emerged as an innovative alternative that integrates artificial intelligence (AI) to optimize real-time decision making. This study presents a systematic literature review and bibliometric analysis of 64 scientific articles focused on the frameworks underpinning these systems. The methodology applied is based on the selection and critical analysis of indexed scientific publications, enabling the identification of predominant approaches such as machine learning, deep learning, multi-agent systems, and reinforcement learning. The findings reveal a strong convergence between AI, the Internet of Things (IoT), and Big Data, as well as significant limitations in terms of interoperability, data governance, and scalability. It is concluded that, while the advances are promising, the consolidation of these systems requires a comprehensive approach that combines technological innovation, appropriate regulation, and social sustainability.

Automated vehicular traffic management in Latin American cities requires solutions capable of capturing, processing, and visualizing events in near real time. Automatic number plate recognition (ANPR) systems continuously generate vehicle records; however, the visual quality of the evidence associated with each capture can vary considerably under real field conditions, affecting the reliability of subsequent monitoring. This work presents a three-phase methodology, applied to the SAVH system (Sistema de Aforo Vehicular, Vehicle Counting System), which integrates a Dahua ANPR camera with a cloud architecture on Amazon Web Services. The camera captures the vehicle and performs textual reading of the license plate, sending vehicle notifications directly to the FastAPI service deployed on Amazon EC2. This service extracts the visual evidence and runs a YOLOv8n–CLS model that classifies the legibility of the plate crop into two classes: placa_legible (legible plate) and placa_no_legible (non-legible plate). Structured events and visual evidence are persisted in managed storage services, and a serverless function serves the web dashboard queries through Amazon API Gateway. The model was trained on a relabeled dataset derived from the public LPLCv2 collection, split into training, validation, and test subsets. Evaluation on 720 independent images from the test set achieved 97.36% overall accuracy, with 97.33% macro precision, 97.42% macro recall, and a 97.36% macro F1-score. Additionally, external validation on 2,000 images captured with the Dahua camera in locations across Guayaquil achieved 95.05% accuracy, showing the moderate degradation expected from domain shift. The results suggest the methodological viability of integrating lightweight deep learning models with managed cloud services for automated plate quality assessment in real-world application scenarios.

Shrimp farming constitutes a strategic productive activity for Ecuador; however, monitoring of shrimp ponds still relies on manual observations, fragmented records, and qualitative criteria that hinder the early detection of visual conditions of operational interest. In this context, the present study proposes a prototype adaptive intelligent system to support the visual monitoring of shrimp ponds through computer vision. The proposal integrates image capture, visual information processing, organized record storage, and alert visualization in an administrative dashboard. The evaluation was conducted using public datasets employed as experimental approximations to three visual signals of interest: feed accumulation, surface changes, and foam presence. In this work, anomaly detection is understood as the preliminary identification of visual signals associated with possible anomalous conditions or conditions of operational interest. The results show high performance in feed accumulation, with mAP50 values above 0.98 for BOX and MASK; moreover, the bootstrap analysis revealed a statistically significant advantage of YOLOv11m-seg over YOLOv8m-seg for the shrimp class. In surface changes, YOLOv8n and YOLOv11n achieved mAP50 values of 0.884 and 0.885, respectively, with no statistically significant differences in F1-score. In foam presence, the models achieved mAP50 values of 0.767 and 0.758, respectively, and the bootstrap analysis confirmed non-significant differences between architectures, reflecting the visual complexity of this signal. Inference times of 3.9–21.2 ms/img support the use of the prototype for frequent monitoring. It is concluded that the proposed system effectively integrates computer vision, inference services, and alert visualization to support visual inspection, event prioritization, and operational management of shrimp ponds.

The sustained growth of urban areas has increased the complexity of managing services, infrastructure, and mobility, creating a need for advanced technological solutions capable of responding dynamically to rapidly changing environments. In this context, adaptive smart urban systems have emerged as an innovative alternative that integrates artificial intelligence (AI) to optimize real-time decision-making. This study presents a systematic literature review focused on the frameworks underpinning these systems, examining the main adaptive AI techniques, current technological trends, and the structural challenges that limit their implementation. The methodology applied is based on the selection and critical analysis of indexed scientific publications, enabling the identification of predominant approaches such as machine learning, deep learning, multi-agent systems, and reinforcement learning. The findings reveal a strong convergence between AI, the Internet of Things (IoT), and Big Data, as well as significant limitations in terms of interoperability, data governance, and scalability. It is concluded that, while the advances are promising, the consolidation of these systems requires a comprehensive approach that combines technological innovation, appropriate regulation, and social sustainability.

Social media platforms have been established as relevant sources of real-time information for urban traffic analysis. This study proposes an intelligent framework for the classification and spatiotemporal analysis of traffic incidents based on semi-synthetic data streams constructed from historical geolocated seeds for controlled validation, utilizing real reports from platforms such as X and Telegram. The approach integrates adaptive machine learning and incremental density-based clustering. An Adaptive Random Forest (ARF) incremental classifier is used to identify the type of incident, allowing for continuous updating of the model in response to changes in traffic flow and concept drift. The classified events are then processed using DenStream, a clustering algorithm that incorporates a temporal decay mechanism designed to identify dynamic spatial patterns and discard older information. The evaluation is performed in a controlled streaming simulation environment that replicates the dynamics of cities such as Panama and Guayaquil. The proposed framework demonstrated robust quantitative performance, achieving a prequential accuracy of up to 86.4% and a weighted F1-score of 0.864 in the Panama scenario, maintaining high stability against semantic noise. The results suggest that this hybrid architecture is a highly viable approach for urban traffic monitoring, providing useful information for Intelligent Transportation Systems (ITS) by processing authentic social signals.

Social media platforms have established themselves as relevant sources of real-time information for urban traffic analysis. This study proposes an intelligent framework for the classification and spatiotemporal analysis of traffic incidents based on data flows constructed for controlled validation, based on real reports from platforms such as X and Telegram. The approach integrates adaptive machine learning and incremental density-based clustering. An Adaptive Random Forest (ARF) incremental classifier is used to identify the type of incident, allowing for continuous updating of the model in response to changes in traffic flow and concept drift. The classified events are then processed using DenStream, a clustering algorithm that incorporates a temporal decay mechanism designed to identify dynamic spatial patterns and discard older information. The evaluation is performed in a controlled streaming simulation environment that replicates the dynamics of cities such as Panama and Guayaquil, using prequential evaluation metrics. The results suggest that this hybrid architecture is a viable approach for urban traffic monitoring, providing useful information for Intelligent Transportation Systems (ITS) by processing authentic social signals.

Intelligent learning applied to multidimensional data streams has established itself as a rapidly expanding field, driven by the growth of ubiquitous computing and the Internet of Things. The complexity of these streams, characterized by their high dimensionality, variability, and continuous nature, poses significant challenges for traditional approaches to analysis. This study presents a bibliometric analysis of scientific output indexed in Scopus between 2015 and 2025, with the aim of identifying trends, challenges, and opportunities in this field. The results show sustained growth in publications, a marked interdisciplinary orientation, and a diversity of applications including transportation, biomedicine, energy, and information systems. Likewise, there is a geographical concentration in certain leading countries and uneven development in terms of international collaboration. This work contributes to mapping the current state of the field and points to future lines of research aimed at its consolidation.

Intelligent learning applied to multidimensional data streams has established itself as a rapidly expanding field, driven by the growth of ubiquitous computing and the Internet of Things. The complexity of these streams, characterized by their high dimensionality, variability, and continuous nature, poses significant challenges for traditional approaches to analysis. This study presents a bibliometric analysis of scientific output indexed in Scopus between 2015 and 2025, with the aim of identifying trends, challenges, and opportunities in this field. The results show sustained growth in publications, a marked interdisciplinary orientation, and a diversity of applications including transportation, biomedicine, energy, and information systems. Likewise, there is a geographical concentration in certain leading countries and uneven development in terms of international collaboration. This work contributes to mapping the current state of the field and points to future lines of research aimed at its consolidation.