AI-Driven Approaches to Climate Modeling
Keywords:
artificial intelligence, climate, modeling, machine learning, patternsAbstract
This article investigates the role of artificial intelligence in enhancing climate models. By integrating AI-driven algorithms with traditional climate simulations, the study aims to improve accuracy in predicting climate changes. The research focuses on the use of machine learning techniques to analyze vast amounts of climate data, enabling more precise models. The potential of AI to identify patterns in climate dynamics that were previously unrecognized is also examined, showcasing the transformative impact of technology on environmental science.
References
Sakil, M. B. H., Hasan, M. A., Mozumder, M. S. A., Hasan, M. R., Opee, S. A., Mridha, M. F., & Aung, Z. (2025). Enhancing Medicare Fraud Detection with a CNN-Transformer-XGBoost Framework and Explainable AI. IEEE Access.
Mozumder, M. S. A., Hasan, M. R., Sakil, M. B. H., Hasan, M. A., Eva, A. A., & Maua, J. (2025, February). AI-Driven Financial Knowledge Graphs: Bridging Traditional Finance and Blockchain Ecosystems with Graph Neural Networks. In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6). IEEE.
Arkabaev, N., Rahimov, E., Abdullaev, A., Padmanaban, H., & Salmanov, V. (2025). Modelling and analysis of optimization algorithms. Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 9(1), 161-177.
Kumar, N., & Kataria, V. (2025). Unpacking the Emotional Landscape of Reviews: Sentiment-Augmented Topic Modeling with Transformer Embeddings.
Kumar, N., & Kataria, V. (2025). Enhancing Skin Cancer Detection Using Hybrid Deep Neural Network (HDNN) Approach. Journal of Computational Analysis and Applications, 34(6).
