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Showing 20 of 2,514 academic articles
Federico Amato, Fabian Guignard, Sylvain Robert, Mikhail Kanevski • Scientific Reports
(2020)
Abstract As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help ...
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Open Access (DOAJ)
DOI: 10.1038/s41598-020-79148-7
William W. Hsieh • Cambridge University Press eBooks
(2009)
Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and th...
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DOI: https://doi.org/10.1017/cbo9780511627217
Jeremy M. Watt, Reza Borhani, Aggelos K. Katsaggelos • Cambridge University Press eBooks
(2020)
With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuiti...
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DOI: https://doi.org/10.1017/9781108690935
Jichao Li, Xiaosong Du, Joaquim R. R. A. Martins • Progress in Aerospace Sciences
(2022)
Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and pr...
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DOI: https://doi.org/10.1016/j.paerosci.2022.100849
Artur M. Schweidtmann, Erik Esche, Asja Fischer, Marius Kloft et al. • Chemie Ingenieur Technik
(2021)
Abstract The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio‐)catalysts, and functional materials. Recent breakthroughs in machine learning (ML)...
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DOI: https://doi.org/10.1002/cite.202100083
José Padarian, Budiman Minasny, Alex B. McBratney • SOIL
(2020)
Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proxi...
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DOI: https://doi.org/10.5194/soil-6-35-2020
Andreas Müller, Sarah Guido (2016)
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teac...
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Saqib Aziz, Michael Dowling • Palgrave studies in digital business & enabling technologies
(2018)
We explore how machine learning and artificial intelligence (AI) solutions are transforming risk management. A non-technical overview is first given of the main machine learning and AI techniques of benefit to risk managementRisk management...
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DOI: https://doi.org/10.1007/978-3-030-02330-0_3
Mathieu Calvat, Chris Bean, Dhruv Anjaria, Hyoungryul Park et al. • npj Computational Materials
(2025)
Abstract To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-b...
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Open Access (DOAJ)
DOI: 10.1038/s41524-025-01770-8
Gengke Lai, Yongguang Zhang • Geophysical Research Letters
(2025)
Abstract Canada experienced an unprecedented wildfire season in 2023. Here, we analyzed the exceptional scale, dominant driving factors, and potential impacts on permafrost of these wildfires using Moderate Resolution Imaging Spectroradiome...
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Open Access (DOAJ)
DOI: 10.1029/2024GL114492
Pengfei Li, Yue Jia • Sensors
(2026)
Diameter at breast height (DBH) is a fundamental structural parameter for forest inventory and ecological analysis. However, field-based measurements (e.g., diameter tape surveys) are labor-intensive and inefficient for large-scale applicat...
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Open Access (DOAJ)
DOI: 10.3390/s26092873
Ioannis Kavakiotis, O. Tsave, Athanasios Salifoglou, Nicos Maglaveras et al. • Computational and Structural Biotechnology Journal
(2017)
The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, ap...
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DOI: https://doi.org/10.1016/j.csbj.2016.12.005
PMC: 5257026
Beatriu Reig, Laura C. Heacock, Krzysztof J. Geras, Linda P. Moy • Journal of Magnetic Resonance Imaging
(2019)
Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmenta...
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DOI: https://doi.org/10.1002/jmri.26852
PMC: 7085409
Liu Yun, Oladapo Christopher Esan, Zhefei Pan, Liang An • Energy and AI
(2021)
The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low su...
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DOI: https://doi.org/10.1016/j.egyai.2021.100049
Hanjie Xu, Jian Xiong, Jinyu Wu, Xianlai Zhou et al. • Scientific Reports
(2025)
Abstract The Ivy Algorithm (IVYA), a swarm intelligence algorithm inspired by plant growth, presents a novel framework for optimization. To unlock its full potential in complex, high-dimensional problems, it is crucial to address the fundam...
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Open Access (DOAJ)
DOI: 10.1038/s41598-025-32510-z
Federico Cabitza, Angela Locoro, Giuseppe Banfi • Frontiers in Bioengineering and Biotechnology
(2018)
In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic probl...
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DOI: https://doi.org/10.3389/fbioe.2018.00075
PMC: 6030383
Qingyang Ling • Applied and Computational Engineering
(2023)
Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machin...
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DOI: https://doi.org/10.54254/2755-2721/4/20230355
Pedro Domingos • Communications of the ACM
(2012)
Tapping into the "folk knowledge" needed to advance machine learning applications....
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DOI: https://doi.org/10.1145/2347736.2347755
Sarkar Siddique, James C. L. Chow • Encyclopedia
(2021)
Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelli...
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DOI: https://doi.org/10.3390/encyclopedia1010021
Yajing Tian, Yuyang Wang, Shasha Yin, Jia Lu et al. • Energies
(2024)
U-tube steam generators (UTSGs) are crucial in nuclear power plants, serving as the interface between the primary and secondary coolant loops. UTSGs ensure efficient heat exchange, operational stability, and safety, directly impacting the p...
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Open Access (DOAJ)
DOI: 10.3390/en17163916