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Showing 20 of 1,262 academic articles
Niloofar Asefi, Leonard Lupin-Jimenez, Tianning Wu, Ruoying He et al. • Machine Learning. Earth
(2025)
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges fo...
DOAJ
Open Access (DOAJ)
DOI: 10.1088/3049-4753/ae0b70
Quan Quan, Zhongqiang Zhang • Journal of Mathematics
(2022)
With the rapid development of economy and information technology, traditional manufacturing industry is facing severe challenges. Enterprises need to rectify the traditional manufacturing industry and realize the transformation from traditi...
DOAJ
Open Access (DOAJ)
DOI: 10.1155/2022/8572424
Ziru Fu, Yu Cheng Hsu, Christian S Chan, Chaak Ming Lau et al. • Journal of Medical Internet Research
(2024)
BackgroundSentiment analysis is a significant yet difficult task in natural language processing. The linguistic peculiarities of Cantonese, including its high similarity with Standard Chinese, its grammatical and lexical uniqueness, and its...
DOAJ
Open Access (DOAJ)
DOI: 10.2196/51069
Zhiqiang Gong, Ping Zhong, Weidong Hu • IEEE Access
(2019)
Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for special requirements of different tasks. Generally, a good machine...
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Open Access
DOI: https://doi.org/10.1109/access.2019.2917620
Maximilian Pichler, Florian Hartig • Methods in Ecology and Evolution
(2023)
Abstract The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI) has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML and DL algorithms are often perceived as opa...
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DOI: https://doi.org/10.1111/2041-210x.14061
Dane D. Morgan, Ryan Jacobs • Annual Review of Materials Research
(2020)
Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid chang...
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Open Access
DOI: https://doi.org/10.1146/annurev-matsci-070218-010015
Ildar M. Ishkulov, Irik G. Fattakhov • Записки Горного института
(2025)
The problem of timely and accurate evaluation of well integrity is becoming increasingly relevant in the context of mature field development, high wellstream water cut, and a growing number of old wells. For production casing diagnostics, g...
DOAJ
Open Access (DOAJ)
Joost Verbraeken, Matthijs Wolting, Jonathan Katzy, Jeroen Kloppenburg et al. • ACM Computing Surveys
(2020)
The demand for artificial intelligence has grown significantly over the past decade, and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, to increase the qual...
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Open Access
DOI: https://doi.org/10.1145/3377454
Hafsa Habehh, Suril Gohel • Current Genomics
(2021)
Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, among...
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Open Access
DOI: https://doi.org/10.2174/1389202922666210705124359
PMC: 8822225
Xueqian Gong, Zhanyuan Zhu, Li Guo, Yong Zhong et al. • Land
(2025)
Xishu Gardens, an exemplary narrative of classical Chinese gardens, faces challenges in preserving its commemorative spatial structures while accommodating modern visitors’ needs. While trajectory analysis is critical, existing studies stru...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/land14050932
Parliamentary Office of Science and Technology, Lorna Christie (2020)
Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that ...
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Open Access
DOI: https://doi.org/10.58248/pn633
Pavlo O. Dral • The Journal of Physical Chemistry Letters
(2020)
As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and...
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Open Access
DOI: https://doi.org/10.1021/acs.jpclett.9b03664
John C. Snyder, Matthias Rupp, Katja Hansen, Klaus‐Robert Müller et al. • Physical Review Letters
(2012)
Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with ...
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Open Access
DOI: https://doi.org/10.1103/physrevlett.108.253002
Wuxin Sha, Yan Li, Shun Tang, Jie Tian et al. • InfoMat
(2021)
Abstract Polymers have been widely used in energy storage, construction, medicine, aerospace, and so on. However, the complexity of chemical composition and morphology of polymers has brought challenges to their development. Thanks to the i...
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DOI: https://doi.org/10.1002/inf2.12167
Neusa R. Adão Martins, Neusa R. Adão Martins, Simon Annaheim, Christina M. Spengler et al. • Frontiers in Physiology
(2021)
The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fphys.2021.790292
Pengsheng Hui, Yu Jiang, Jie Wang, Congxiao Wang et al. • Frontiers in Aging Neuroscience
(2024)
BackgroundParkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a cri...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fnagi.2024.1354147
Thorsten Wuest, Daniel Weimer, Christopher Irgens, Klaus‐Dieter Thoben • Production & Manufacturing Research
(2016)
The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means av...
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DOI: https://doi.org/10.1080/21693277.2016.1192517
Ji-Hoon Kim, Seunghee Han, Kwanghyun Park, Soo-Young Ji et al. • IEEE Access
(2024)
The ability to perform machine learning (ML) tasks in a database management system (DBMS) is a new paradigm for conventional database systems as it enables advanced data analytics on top of well-established capabilities of DBMSs. However, t...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2023.3347725
S. Mostafa Mousavi, Gregory C. Beroza • Annual Review of Earth and Planetary Sciences
(2022)
Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data m...
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DOI: https://doi.org/10.1146/annurev-earth-071822-100323
Stanislav Mazurenko, Zbyněk Prokop, Jiřı́ Damborský • ACS Catalysis
(2019)
Abstract Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have b...
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other-oa
DOI: https://doi.org/10.1021/acscatal.9b04321