In recent years, significant attention has been paid to fuzzy recommender systems for housing, highlighting their ability to effectively handle the imprecision and uncertainty inherent in the real estate market. With the objective of improving the filtering of recommendations in the real estate sector, the PRISMA 2020 methodology was applied to perform new systematic reviews using its checklist on six academic databases from 1985 to 2024. RawGraph, Orange Data Minig, Jamovi and R software were used for document classification and data visualization. After classification, 1003 articles were obtained, of which 46.36% were in Scopus, and 57.82% were articles. At the end of the type, 50 articles were identified as primary, subjecting them to six research questions. It was found that 65% of the algorithms used fuzzy logic, 60% used spatial data, and 80% evaluated performance. The main difficulties were related to the integration of various sources of information. Although incorporating reclusive methods is anticipated in future systems, the need remains to address challenging areas to improve the overall performance of fuzzy recommender systems. The reviewed articles focus on enhancing fuzzy data-based recommendation systems by proposing flexible and less intrusive techniques. The significance of incorporating contextual information and exploring hybrid approaches is emphasized, along with the evaluation in real world environments, averaging artificial intelligence.

Purpose: This investigation is based on the theoretical analysis of the application of neural networks to the design and manage supply chains, along with an empirical approach, this investigation its developed with the prediction of the level of integration in the supply chain through neural networks.Design/methodology/approach: The methodology designed and used for the processing of data was the instruction of a neural network wich is used to predict the level of integration in a supply chain. This type of predictive application appears in the literature reviewed on supply chains. This analysis was carried out in a comparative way with the heterogeneous and homogeneous weights of the neuron training.Findings: The main results of this research focus on predicting the level of integration in the supply chain from the neoronal network. This provides a coached neuron that can be applied in other studies and, therefore, predict the outcome. On the other hand, it is shown that if the weights of the integration level variables are not homogeneous, the procedure presents different results depending on the context in which it is developed.Research limitations/implications: Among the limitations of the implementation of neural networks it should be noted, the necessary adaptation to the characteristics of the supply chains and the areas of performance of the business organizations under study, in the framework of activities productive or service itself, in addition to analyzing its corporate purpose in relation to the satisfaction of certain needs of the target markets.Originality/value: The literature shows multiple theoretical sources that refer to studies of neural networks in supply chains, observing the opportunity to apply this technique to predict the level of integration due to its benefits for decision making. The originality of this scientific work lies in the possibility of comparing the historical data of the level of integration and those predicted as a result of the coaching of the neuron with the weights of the heterogeneous and homogeneous variables.

According to the United Nations (UN), a “continuous, participatory, and inclusive urban planning process should be the starting point and framework for improving population living conditions,” and this research emerges from examining trends in smart cities. It explores the impact of collective intelligence, geographical information systems, and cognitive systems as a way of supporting the decision-making of local governments (municipalities) to face the challenges announced by the UN in the Urban Agenda 2017. This methodology employs an exploratory approach using the participatory action research method. The model was tested in three municipalities in Ecuador using a technological and collaborative platform called Cognitive Urban Planning (PUC). The results highlighted improvements in the decision-making process in urban planning, since the interactive participation processes through the PUC allowed for the suggestion of a group of strategies for an emerging neighborhood plan developed with the citizens working together in the municipality in real-time. In conclusion, the effects of the decision-making plan could be applied to improve and enhance community participation through city co-creation, supporting the decision-making process of municipalities in a way that promotes inclusive urban planning and stakeholders' awareness, as the recommended plans are supported by the desires and needs raised by citizens.

The objective of this paper is derived from the theoretical analysis of the application of support vector machines to the design and management of agri-food chains. This analysis is conducted with an empirical approach, for the prediction of the level of integration in agri-food chains through support vector machines. The methodology designed and used for the processing of research results, which consists in the training of support vector machines is used to predict the level of integration in an agri-food chain. This type of predictive application appears in the literature consulted on the integration of agri-food chains. The analysis is performed comparing the method proposed with the neural network technique. The results of this research are mainly focused on predicting the level of integration in agri-food chains through vector machines. The study provides a support vector machine model that is applied to other case studies and therefore, allows predicting the outcome. The paper also shows the comparison of two techniques that share the goal of predicting, as applied in different contexts.

Este artículo revisa la inclusión social de la mujer en el fortalecimiento y eficiencia de la cadena de valor del maíz en Ecuador. Para cumplir este propósito se analiza las limitaciones y oportunidades de la mujer en la gestión administrativa, procesos de producción y otras actividades, y como incide en el desempeño de la cadena de valor del maíz. Utilizando los resultados de las encuestas aplicadas a setenta y un mujeres agricultoras, comerciantes agrícolas, procesadoras de alimentos, emprendedoras y/o amas de casa—, se estima un modelo Logit para evaluar la incidencia de las características sociales y económicas de las mujeres de la región en la cadena de valor del maíz. Los resultados permiten evidenciar que la participación de la mujer en actividades relacionadas con el maíz han mejorado la posición social, el empoderamiento y la participación comunitaria, a pesar de la mayor participación de los hombres en todos los nodos de la cadena, a la vez que han generado una mejora en la cadena de valor.

Este estudio investiga la aplicación de técnicas de Machine Learning (ML), específicamente el algoritmo de clustering K-Means y la reducción de dimensionalidad mediante PCA, para identificar patrones en datos de siniestros de tránsito. Analizando un conjunto de datos que incluye 21,352 registros de siniestros, este trabajo clasifica los accidentes en cuatro clusters distintos, revelando diferencias significativas en las características de los siniestros relacionadas con la ubicación, hora del día, causas y condiciones ambientales. Los resultados destacan la complejidad de los factores que contribuyen a los siniestros de tránsito y subrayan la necesidad de intervenciones personalizadas para mejorar la seguridad vial. Este enfoque permite una comprensión más profunda de los patrones de siniestros, facilitando el desarrollo de políticas y estrategias de prevención más efectivas. Este estudio no solo amplía la literatura existente, ofreciendo una metodología analítica robusta para el análisis de datos de siniestros, sino que también proporciona orientación práctica para la implementación de intervenciones de seguridad vial dirigidas y basadas en evidencia. Recomendamos futuras investigaciones para incluir más variables, comparar diferentes modelos de ML y realizar estudios longitudinales que permitan evaluar la evolución de los patrones de siniestros y la efectividad de las políticas de seguridad vial implementadas.