Machine Learning Based Toolbox in Foreign Language for Children to Address Climate Change Adaptation


  • Zuleima Nuñez Leguia, Genis Arteaga Requena, Jhon Anaya Herrera, Nini Johana Villamizar Parada, Ligia Rosa Martinez Bula, Mario Alfonso Gándara Molina


Machine learning, k-nearest neighbour, decision tree, climate change, toolbox


Climate change poses a significant threat to our planet, and educating future generations about its implications and solutions is paramount for effective adaptation and mitigation efforts. However, language barriers can hinder the dissemination of crucial information, particularly to children who may not yet be proficient in the predominant language of scientific discourse. This paper proposes a novel approach to addressing this challenge by developing a machine learning-based toolbox in a foreign language tailored for children. Leveraging advances in natural language processing and educational technology, the toolbox aims to facilitate interactive learning experiences in foreign languages, fostering a deeper understanding of climate change and promoting actionable strategies for adaptation. Machine learning algorithms like k-nearest neighbor, decision tree, logistic regression, and deep learning techniques such as natural language processing and artificial neural networks are being utilized to tackle climate change challenges across different sectors, including transportation


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How to Cite

Mario Alfonso Gándara Molina, Z. N. L. G. A. R. J. A. H. N. J. V. P. L. R. M. B. . (2024). Machine Learning Based Toolbox in Foreign Language for Children to Address Climate Change Adaptation. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1581–1586. Retrieved from



Research Article