Jorge Gómez Gómez

Artisanal farmers in developing regions capture limited commercial value due to information asymmetries, market fragmentation, and informal intermediation, where commercialization, rather than production, is the main bottleneck for rural income. This study formalizes artisanal agricultural commercialization as a buyer-recommendation problem in a sparse bilateral market. We propose a context-aware hybrid system that combines content-based and collaborative filtering with economic and geographic variables. Because no transactional dataset exists for the target region (Cesar, Colombia), a proof-of-concept evaluation (Phase A) uses the Brazilian Olist e-commerce dataset as a structural proxy, benchmarking 13 models under a temporal leave-one-out protocol. A Reciprocal Rank Fusion ensemble achieved the best ranking performance (NDCG@10 = 0.4031), an item-based neighborhood model had the lowest rating-prediction error, and content-based filtering proved to be the most informative component under extreme sparsity. Differences were confirmed using Friedman, Nemenyi, bootstrap, and effect size tests. These offline indicators show technical feasibility but do not demonstrate gains in producer income or field adoption rates. Real-world validation, contextual adaptation, and fairness assessment in rural Cesar are proposed for future work (Phase B).

This article presents the design and development of an interactive architecture oriented toward the management of traditional vallenato, a musical genre recognized as an Intangible Cultural Heritage of Humanity by UNESCO. Architecture combines the principles of contextual awareness and the use of massive open online courses (MOOCs) to face the current challenges of preservation, dissemination, and teaching of this cultural expression, threatened by commercialization and the loss of its traditional roots. Through a modular structure, adaptive technological tools are integrated to capture, process, and use contextual information, personalizing learning experiences and strengthening the link between communities and their cultural heritage. The proposal consists of several functional layers, including context management, user profiles, educational resources, and a persistence unit, each designed to ensure the interoperability and sustainability of cultural data. In addition, the capacity of architecture to be used in other cultural contexts is highlighted, expanding its impact on different artistic manifestations and heritages worldwide. This article includes a comparative analysis with other existing models, highlighting the advantages of this solution in terms of customization and adaptability. Finally, opportunities for improvement and expansion are explored, as well as the pending challenges in the implementation of this technological tool in educational and cultural environments.

Confirmatory diagnosis of sickle cell anemia (SCA) traditionally requires specialized tests that are not widely available in low-resource settings. We propose an automated prediction system based exclusively on structured Complete Blood Count (CBC) variables and demographic data. We analyzed the clinical records of 786 patients (425 with normocytic anemia and 361 with SCA) and evaluated seven classifiers using stratified 10-fold cross-validation with fold-wise SMOTE. Random Forest and XGBoost achieved the best performance (mean accuracy = 0.961, mean F1 = 0.957, mean AUC = 0.995). A global Friedman test indicated significant differences among the classifiers (χ² = 29.52, p = 1.16 × 10⁻⁴). Post-hoc Wilcoxon tests adjusted by Holm did not identify pairwise significant contrasts at α = 0.05; however, paired effect sizes (Cohen’s d) revealed large practical differences favoring the ensemble tree methods. SHAP interpretability identified PCV (hematocrit), HGB, RDW and Age as the main contributors. The system offers a low-cost, interpretable screening tool that prioritizes patients for confirmatory testing. However, external validation and multimodal integration are required before it can be clinically deployed.

In this study, three artificial intelligence techniques were compared for the binary detection (healthy/diseased) of cardiopathies using axial slices from cardiac magnetic resonance imaging. Using a dataset of 150 patients in NIfTI format, the images were preprocessed (normalization, rescaling to 128 × 128, RGB conversion, and data augmentation) and split on a per-patient basis using an 80/20 ratio. A Random Forest model with GLCM radiomic descriptors and first-order statistical features, a convolutional neural network (CNN), and a YOLOv8-based model adapted for binary classification were evaluated. The models were compared using accuracy, precision, recall, F1-score, and AUC, and explainability techniques (SHAP, Grad-CAM, Integrated Gradients, and occlusion sensitivity) were applied to validate the anatomical coherence of the predictions. Overall, the results indicate that pretrained deep learning approaches, such as YOLOv8, offer substantial advantages in terms of accuracy and interpretability, positioning them as a promising alternative for the development of intelligent decision-support systems for the diagnosis of structural cardiopathies.

This study explored the evolution of vehicular ad hoc networks (VANET) and focused on the challenges and opportunities for routing in these dynamic environments. Despite advancements in traditional protocols, a significant gap persists in the ability to adapt to highly mobile environments with variable traffic, which limits routing efficiency and quality of service. Emerging technologies, such as artificial intelligence (AI) and software- defined networks (SDN), are discussed that have the potential to revolutionize the management of VANET. Machine learning can be used to predict traffic, optimize routes, and adapt routing protocols in real-time. Furthermore, SDN can simplify routing management and enable greater flexibility in network configurations. A comprehensive overview of the convergence of AI and SDN is presented, and the potential complementarities between these technologies to address routing challenges in VANET are explored. Finally, the implications of efficient routing in VANET for road safety, traffic management, and the development of new applications are discussed, and future research lines are identified to address challenges such as scalability, data security, and computational efficiency in vehicular environments.

This article presents a detailed review of methodologies for estimating crop yields in the context of growing global concern for food security and agricultural sustainability, with the main objective of analyzing, synthesizing, and comparing recent studies that apply artificial intelligence to yield prediction, identifying their strengths, limitations, and emerging trends. Approaches that integrate climatic variables, soil conditions, and agricultural management practices are examined. Artificial intelligence techniques, such as machine learning and neural networks, are effective at building robust predictive models. In several reviewed studies, these methods have achieved coefficients of determination (R²) greater than 0.85 and error reductions of 15% to 30% compared to traditional statistical approaches, confirming their high predictive potential. These models consider key elements such as temperature, precipitation, soil fertility, and agronomic decisions related to planting, crop choice, and fertilizer use. The article also discusses the challenges associated with model calibration and selection, given the complexity of agricultural systems and the variability of available data. The review covers studies published between 2016 and 2024, a period in which there has been a notable advance in the application of hybrid and deep learning approaches in the agricultural field. The importance of further research into hybrid approaches that integrate various techniques to improve prediction accuracy is highlighted. Finally, the strategic role of artificial intelligence in agricultural decision-making, in promoting sustainable practices, and in strengthening global food security is underlined.

Introduction: generative Artificial Intelligence (GAI) represents a disruptive technological advancement that is profoundly transforming higher education worldwide. In response, several organizations, including UNESCO, have developed guidelines promoting the responsible integration of AI in educational and research contexts, emphasizing its supportive and human-centered role.Objectives: this study aimed to analyze the perception of generative AI in higher education by examining the frequency of use, perceptions of its future impact, and awareness of ethical and privacy risks.Methodology: data were collected through instruments administered to faculty members and undergraduate students at the University of Córdoba, Colombia. This dual perspective allowed comparison between academic and student viewpoints, contributing to the growing discussion on the role of GAI in higher education and research.Results: findings reveal that the adoption of GAI is increasingly widespread among both faculty and students, though notable differences persist in their usage patterns and approaches to risk management. Students show high familiarity and frequent use, while faculty members adopt GAI more cautiously, reflecting deliberate integration into pedagogical practices.Conclusions: despite its growing prevalence, significant gaps remain in understanding the ethical and privacy implications of GAI. These findings underscore the need for targeted training and institutional guidance to promote responsible and effective use of generative AI in higher education.