Predicting CO2 Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm

Mathematics, Journal Year: 2025, Volume and Issue: 13(9), P. 1481 - 1481

Published: April 30, 2025

Global carbon dioxide (CO2) emissions are increasing and present substantial environmental sustainability challenges, requiring the development of accurate predictive models. Due to non-linear temporal nature data, traditional machine learning methods—which work well when data structured—struggle provide effective predictions. In this paper, we propose a general framework that combines advanced deep models (such as GRU, Bidirectional GRU (BIGRU), Stacked Attention-based BIGRU) with novel hybridized optimization algorithm, GGBERO, which is combination Greylag Goose Optimization (GGO) Al-Biruni Earth Radius (BER). First, experiments showed ensemble such CatBoost Gradient Boosting addressed static features effectively, while time-dependent patterns proved more challenging predict. Transitioning recurrent neural network architectures, mainly BIGRU, enabled modeling sequential dependence on data. The empirical results show GGBERO-optimized BIGRU model produced Mean Squared Error (MSE) 1.0 × 10−5, best tested approach. Statistical methods like Wilcoxon Signed Rank Test ANOVA were employed validate framework’s effectiveness in improving evaluation, confirming significance robustness improvements due framework. addition accuracy CO2 forecasting, integrated approach delivers interpretable explanations significant factors emissions, aiding policymakers researchers focused climate change mitigation data-driven decision-making.

Language: Английский

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Monkeypox diagnosis based on probabilistic K-nearest neighbors (PKNN) algorithm

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 186, P. 109676 - 109676

Published: Jan. 23, 2025

Language: Английский

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Multi-source physical information driven deep learning in intelligent education: Unleashing the potential of deep neural networks in complex educational evaluation

AIP Advances, Journal Year: 2025, Volume and Issue: 15(2)

Published: Feb. 1, 2025

With the urgent global demand for sustainable development, intelligent education driven by multi-source physical information has attracted widespread attention as an innovative educational model. However, in context of dual carbon, achieving and efficient development faces many difficulties, one important challenges is how to effectively evaluate students. The application deep neural networks evaluation direction digitization. Currently, there need conduct research on value empowering with networks. We first studied principles characteristics network technology evaluation; second, three major advantages were pointed out: objectivity evaluating diversified data, accuracy perception information, mining data finally, key faced clarified from perspectives environment, theoretical knowledge, interpretability. This provides new ideas methods lays foundation breaking through traditional era carbon development.

Language: Английский

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Enhancing Monkeypox Detection With Efficientnet-B5 And Image Augmentation Fusion Technique

International Journal of Scientific Research in Science and Technology, Journal Year: 2024, Volume and Issue: 11(6), P. 646 - 661

Published: Dec. 12, 2024

The recent surge of monkeypox infections worldwide has underscored the need for rapid, accurate diagnostic tools, particularly in regions with limited access to laboratory-based tests. This study employs deep learning, utilizing a pre-trained efficientNet-B5 model through transfer classify from digital skin lesion images. Data was compiled Kaggle, web scraping, and hospital records, covering both similar conditions such as chickenpox, measles smallpox. dataset preprocessed using advanced augmentation fusion techniques, enhancing image diversity maintaining features critical model's efficacy. achieved impressive results, demonstrating 99.47% accuracy, 99.19% precision recall 99.72 monkeypox. These findings suggest that model, supported by fusion, can serve reliable tool detecting monkeypox, providing scalable solution early identification public health intervention resource-constrained settings.

Language: Английский

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