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Showing 20 of 1,217 academic articles
Heechan Han, Boran Kim, Kyunghun Kim, Donghyun Kim et al. • Water Science and Technology
(2023)
Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an ...
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Open Access (DOAJ)
DOI: 10.2166/wst.2023.237
Bhargav Teja Nallapu, Ali Ezzati, Helena M. Blumen, Kellen K. Petersen et al. • Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
(2025)
ABSTRACT INTRODUCTION Understanding the heterogeneity of brain structure in individuals with the Motoric Cognitive Risk Syndrome (MCR) may improve the current risk assessments of dementia. METHODS We used data from six cohorts from the MCR ...
DOAJ
Open Access (DOAJ)
DOI: 10.1002/dad2.70197
Biprateep Dey, David Zhao, Brett H Andrews, Jeffrey A Newman et al. • Machine Learning: Science and Technology
(2025)
Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable Y given complex inputs X . Despite recent advances in machine learning and physic...
DOAJ
Open Access (DOAJ)
DOI: 10.1088/2632-2153/ae1f05
Pierre Baldi, Søren Brunak (2001)
In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who nee...
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Michael I. Jordan, Tom M. Mitchell • Science
(2015)
Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and a...
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DOI: https://doi.org/10.1126/science.aaa8415
Mario D. Molina, Filiz Garip • Annual Review of Sociology
(2019)
Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and soci...
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Open Access
DOI: https://doi.org/10.1146/annurev-soc-073117-041106
Brad Boehmke, Brandon Greenwell • Chapman & Hall/CRC eBooks
(2019)
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process a...
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DOI: https://doi.org/10.1201/9780367816377
Tom M. Mitchell • Communications of the ACM
(1999)
Machine learning algorithms enable discovery of important "regularities" in large data sets....
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Open Access
DOI: https://doi.org/10.1145/319382.319388
Steven L. Brunton, Bernd R. Noack, Petros Koumoutsakos • Annual Review of Fluid Mechanics
(2019)
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniq...
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Open Access
DOI: https://doi.org/10.1146/annurev-fluid-010719-060214
Thomas G. Dietterich • ACM Computing Surveys
(1995)
suggests a reasonable line of research: find algorithms that can search the hypothesis class better. Hence, there is been extensive research in applying second-order methods to fit neural networks and in conducting much more thorough search...
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Open Access
DOI: https://doi.org/10.1145/212094.212114
Tomasz Rokicki, Piotr Bórawski, Aneta Bełdycka-Bórawska, Bogdan Klepacki • Applied Sciences
(2026)
Port activity anomalies are rare but may signal operational constraints relevant to supply chains. The aim of the study was to develop and empirically evaluate a calibrated machine learning framework for forecasting rare, algorithmically de...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app16189017
Ammar Alnmr, Ammar Alnmr, Rashad Alsirawan, Haidar Hosamo et al. • Frontiers in Built Environment
(2026)
Efficient and reliable design of Geosynthetic Reinforced Pile-Supported (GRPS) embankments on soft ground remains challenging due to the complex nonlinear soil–structure interaction and the high computational cost of repeated numerical anal...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fbuil.2026.1829950
Mafalda Reis Pereira, Mafalda Reis Pereira, Filipe Neves dos Santos, Fernando Tavares et al. • Frontiers in Plant Science
(2023)
Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant dis...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fpls.2023.1242201
Doyeon Weon, Kyungchun Lee • IEEE Access
(2020)
In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multiple-input-multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to p...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2020.2987375
Ahmed Medhat Zayed, Arne Janssens, Pavlos Mamouris, Nicolas Delvaux • BMC Medical Informatics and Decision Making
(2024)
Abstract Background The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and acc...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s12911-024-02652-7
Juan Carlos Ramírez-Vázquez, Guadalupe Esmeralda Rivera-García, Marco Antonio Gómez-Guzmán, Marco Antonio Díaz-Martínez et al. • Technologies
(2026)
A substantial number of students with hearing impairments are enrolled in higher education, motivating the development of inclusive assistive technologies that reduce communication barriers. This study developed and evaluated a prototype el...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/technologies14030189
Kevin P. Murphy (2012)
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict fu...
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Jing Wei, Xuan Chu, Xiangyu Sun, Kun Xu et al. • InfoMat
(2019)
Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to the...
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DOI: https://doi.org/10.1002/inf2.12028
Pat Langley B (1995)
Elements of Machine Learning by Pat Langley Preface 1. An overview of machine learning 1.1 The science of machine learning 1.2 Nature of the environment 1.3 Nature of representation and performance 1.4 Nature of the learning component 1.5 F...
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JUDE W. SHAVLIK, Thomas E. Deitterich, Thomas G. Dietterich (1991)
From the Publisher:
The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous ...
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