Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

Sustainable Development, Год журнала: 2024, Номер unknown

Опубликована: Окт. 6, 2024

Abstract Artificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on efforts of reducing carbon emissions more efficiently effectively, this study tries focus AI integration with capture technology. The urgency tackling climate change means we need advanced capture, is an area where can make a huge impact in how these technologies operated managed. It will minimize manufacturing improve both resource efficiency as well our planet's footprint by turning waste into something value again. could be leveraged analyze data sets from plants, searching for optimal system settings efficient ways identifying patterns available information at larger scale than currently possible. In addition, incorporated sensors monitoring mechanisms supply chain identify any operational failure reception itself allowing timely action protect those areas. also helps generative design materials, which allows researchers explore new types carbon‐absorbing material, including metal–organic frameworks polymeric materials that industrial CO 2 , such moisture. it increases accuracy reservoir simulations controls injection systems storage or enhanced oil recovery. Through applying algorithms geology, production performance real‐time would like facilitate optimization processes while assuring maximum efficiency. integrates renewable‐based employed AI‐driven smart grid methods.

Язык: Английский

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FedCCW: a privacy-preserving Byzantine-robust federated learning with local differential privacy for healthcare

Cluster Computing, Год журнала: 2025, Номер 28(3)

Опубликована: Янв. 21, 2025

Язык: Английский

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Integration of cognitive conflict in generative learning model to enhancing students’ creative thinking skills

Eurasia Journal of Mathematics Science and Technology Education, Год журнала: 2024, Номер 20(9), С. em2504 - em2504

Опубликована: Сен. 2, 2024

In the complexity of Fourth Industrial Revolution era, importance creative thinking is increasingly emphasized in context learning computing and algorithms. These skills are instrumental inspiring innovative solutions, addressing complex challenges, fostering development advanced technologies that characterize transformative landscape 4.0. This study aims to determine effectiveness generative model based on cognitive conflict improving (CTS) outcomes students computational physics algorithms & programming courses. research used mixed methods consisting pretest-posttest control group design snowballing technique. The instruments consist tests, psychomotor affective CTS observation questionnaires, interviews. sample consisted 138 taking Quantitative data were analyzed using multivariate analysis variance qualitative narrative analysis. findings indicate this effectively improves students’ outcomes. Furthermore, aspect encourages be analyzing solving problems. has potential optimize facing demands fourth industrial revolution.

Язык: Английский

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Curvature index of image samples used to evaluate the interpretability informativeness

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 137, С. 109044 - 109044

Опубликована: Авг. 8, 2024

Язык: Английский

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Enhancing public safety: a hybrid Conv_Trans-OptBiSVM approach for real-time abnormal behavior detection in crowded environments

Signal Image and Video Processing, Год журнала: 2024, Номер 18(11), С. 7513 - 7525

Опубликована: Сен. 4, 2024

Язык: Английский

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TMPSformer: An Efficient Hybrid Transformer-MLP Network for Polyp Segmentation

Mobile Networks and Applications, Год журнала: 2024, Номер unknown

Опубликована: Сен. 10, 2024

Язык: Английский

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LmGa: Combining label mapping method with graph attention network for agricultural recognition

Knowledge and Information Systems, Год журнала: 2024, Номер 67(1), С. 789 - 810

Опубликована: Окт. 5, 2024

Язык: Английский

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Active learning with human heuristics: an algorithm robust to labeling bias

Frontiers in Artificial Intelligence, Год журнала: 2024, Номер 7

Опубликована: Ноя. 19, 2024

Active learning enables prediction models to achieve better performance faster by adaptively querying an oracle for the labels of data points. Sometimes is a human, example when medical diagnosis provided doctor. According behavioral sciences, people, because they employ heuristics, might sometimes exhibit biases in labeling. How does modeling as human heuristic affect active algorithms? If there drop performance, can one design algorithms robust labeling bias? The present article provides answers. We investigate two established heuristics (fast-and-frugal tree, tallying model) combined with four (entropy sampling, multi-view learning, conventional information density, and, our proposal, inverse density) and three standard classifiers (logistic regression, random forests, support vector machines), apply their combinations 15 datasets where people routinely provide labels, such health other domains like marketing transportation. There are main results. First, we show that if significantly drops, below random. Hence, it key bias. Our second contribution algorithm. proposed density algorithm, which inspired psychology, achieves overall improvement 87% over best algorithms. In conclusion, designing benchmarking benefit from incorporating heuristics.

Язык: Английский

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A cross-database micro-expression recognition framework based on meta-learning

Applied Intelligence, Год журнала: 2024, Номер 55(1)

Опубликована: Ноя. 30, 2024

Язык: Английский

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AL-FEW: An enhanced approach for optimized query examples through feature weighting in active learning

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 126045 - 126045

Опубликована: Дек. 1, 2024

Язык: Английский

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