The convergence of artificial intelligence (AI) and blockchain is transforming the Fintech sector by offering innovative solutions in security, regulatory compliance, and operational transparency. This systematic mapping study analyzed 667 studies published between 2010 and 2024, selecting 175 relevant articles. The findings show that these technologies optimize critical processes such as Know Your Customer (KYC) and fraud prevention, though they face limitations in scalability, interoperability, and regulatory adaptation. This work provides a comprehensive perspective on their current and future impact in Fintech. Objective: Analyze the impact of AI and blockchain on Fintech platform security, evaluating benefits, limitations, and opportunities to improve critical processes and promote industry adoption. Method: This systematic literature review used five major academic databases: IEEE Xplore, ACM Digital Library, Taylor and Francis Online, Wiley Online Library, and Emerald Insight. From 667 studies, 175 met the inclusion criteria. Results: The integration of AI and blockchain significantly improves the security and efficiency of key processes such as KYC and fraud detection. However, barriers in scalability, interoperability, and regulation persist, limiting widespread implementation.
The sustainable management of Waste Electrical and Electronic Equipment (WEEE) is a critical global challenge, particularly in contexts with limited data. This study proposes a predictive model based on artificial neural networks, developed from surveys and historical records in the city of Guayaquil, with the aim of estimating WEEE generation on annual and monthly scales. The model was structured in phases of data collection, preprocessing, training, and validation, integrating sociodemographic variables and categories of discarded devices. To ensure reliability, a multi-technique validation protocol was applied, including Hold-Out, Stratified K-Fold, and Bootstrap Sampling methods. Results showed strong performance, with a coefficient of determination (R²) of 0.9125 in initial tests, an average of 0.9097 in cross-validation, and up to 0.9789 with bootstrap, significantly outperforming traditional linear regression methods. These findings confirm the model’s ability to capture non-linear relationships and produce accurate forecasts in data-scarce environments. It is concluded that neural networks represent an effective tool to support strategic planning and decision-making in sustainable WEEE management, providing a replicable framework for other regions facing similar challenges.
Activation functions are fundamental components of modern neural networks, including Large Language Models (LLMs). Nonlinear activations regulate the flow of information in most models and determine how data is processed. However, training or even fine-tuning very large and complex models with such activations, like those used in ChatGPT and DeepSeek, remains out of reach for many researchers. For this reason, traditional architectures like LSTM remain relevant for moderate or small-scale applications. In this context, the choice of appropriate functions can significantly influence a network's ability to learn complex patterns under limited computing resources. In this work, we analyze the behavior of a novel family of activation functions, called Zorro, which can serve as gating functions in LSTM architectures. We propose replacing traditional activation and gating functions in LSTMs with Zorro functions to improve model performance and convergence speed. Unlike conventional approaches, our method assigns a different function to each gate or activation, enabling the application of the same methodology to any gated architecture. We evaluate the modified LSTM models on widely used small-scale benchmark datasets, including Japanese Vowels and Human Activity Recognition for classification, as well as Chickenpox and Turbofan Degradation for regression. The results show that our method improves model accuracy by up to 10% and reduces training time by up to 15%.
Technology and communication are fundamental in modern enterprise environments, making network performance evaluation crucial. To analyze their impact on enterprise infrastructure performance, this study compares EoIP and VxLAN, two technologies used for Layer 2 network extension over IP. The PPDIOO methodology was employed to structure and optimize the network analysis and improvement process. The implementation included configuring MikroTik devices running RouterOS, playing a key role in the topologies used. Tests on bandwidth, latency, and transfer rate were conducted in simulated environments using hping, iperf, and Wireshark to assess performance. The results indicate that VXLAN provides better latency performance, whereas EoIP offers lower overhead and greater configuration simplicity. These findings highlight the importance of considering various factors when choosing between these technologies, ensuring that the implementation aligns with the specific requirements of each enterprise environment. This study provides key insights for network administrators and IT architects seeking to optimize their organization's infrastructure through advanced segmentation and network virtualization technologies.
This study explores housing price prediction in six major cities of Andalusia using a combination of traditional and machine learning models. Online property listings were compiled to build a detailed dataset incorporating structural, socioeconomic, and locational variables. A traditional hedonic model was estimated using Ordinary Least Squares (OLS), and its performance was compared with that of Random Forest, K-Nearest Neighbours, and XGBoost. Results show that linear models tend to underperform, highlighting the relevance of non-linear approaches in capturing the complex dynamics of the housing market. To enhance interpretability, SHapley Additive exPlanations (SHAP) values were applied to the XGBoost model, enabling the identification of the most influential predictors in each city. While structural variables remained important across all models, socioeconomic and locational features exhibited varying effects depending on the city. These findings underscore the potential of combining rich data sources with interpretable machine learning techniques for urban housing market analysis.
