Abstract
In the age of rapidly advancing machine learning capabilities, the pursuit of maximum performance encounters the practical limitations imposed by limited resources in several fields. This work presents a cost-effective proposal for feature selection, which is a crucial part of machine learning processes, and intends to partly solve this problem through computational time reduction. The proposed methodology aims to strike a careful balance between feature exploration and strict computational time concerns, by enhancing the quality and relevance of data. This approach focuses on the use of interim representations of feature combinations to significantly speed up a potentially slow and computationally expensive process. This strategy is evaluated in several datasets against other feature selection methods, and the results indicate a significant reduction in the temporal costs associated with this process, achieving a mean percentage decrease of 85%. Furthermore, this reduction is achieved while maintaining competitive model performance, demonstrating that the selected features remain effective for the learning task. These results emphasize the method's feasibility, confirming its ability to transform machine learning applications in environments with limited resources.

Abstract
Globally Important Agricultural Heritage Systems (GIAHS) are models of sustainability, as they ensure a balance between human activity and ecosystem conservation. The Barroso region in Portugal is part of this network, as it follows traditional natural resource management and resilience practices by local communities. Given the threats posed by environmental degradation, it is urgent to adopt technological solutions for monitoring these conditions. Thus, throughout this article, the main threats to the integrity of these territories will be analyzed, and various methodologies and solutions for environmental monitoring will be presented. Based on the knowledge acquired, we will present an architecture for a digital solution that includes sensors, the Internet of Things (IoT), processing units, and platforms for real-time data visualization and alarm management. © 2025 by SCITEPRESS-Science and Technology Publications, Lda.