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Academic Articles & Research Papers
Showing 20 of 1,629 academic articles
Dimitrios Kleftogiannnis, Sonia Gavasso, Benedicte Sjo Tislevoll, Nisha van der Meer et al. • iScience
(2024)
Summary: Mass cytometry by time-of-flight (CyTOF) is an emerging technology allowing for in-depth characterization of cellular heterogeneity in cancer and other diseases. Unfortunately, high-dimensional analyses of CyTOF data remain quite d...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.isci.2024.110261
Sagar Chokshi, Piyush Gohil, Vijay Parmar, Vijaykumar Chaudhary • Mechanics of Advanced Composite Structures
(2025)
Machine Learning has become prevalent nowadays for predicting data on the mechanical properties of various materials and is widely used in various polymeric applications. In the present study, Artificial Neural Network (ANN), a computationa...
DOAJ
Open Access (DOAJ)
DOI: 10.22075/macs.2024.33874.1664
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...
OPENALEX
Open Access
DOI: https://doi.org/10.1109/access.2019.2917620
Christopher E. Lawson, Jose Manuel Martí, Tijana Radivojević, Sai Vamshi R. Jonnalagadda et al. • Metabolic Engineering
(2020)
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing ...
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DOI: https://doi.org/10.1016/j.ymben.2020.10.005
C. Ramírez, Michael Greenop, Lorna Ashton, Ihtesham Ur Rehman • Applied Spectroscopy Reviews
(2020)
The way to analyze data in spectroscopy has changed substantially. At the same time, data science has evolved to the point where spectroscopy can find space to be housed, adapted and be functional. The integration of the two sciences has in...
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DOI: https://doi.org/10.1080/05704928.2020.1859525
Batta Mahesh • International Journal of Science and Research (IJSR)
(2020)
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without being explicitly programmed. Learning algorithms in many applications that's we make use of dail...
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Open Access
DOI: https://doi.org/10.21275/art20203995
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
Yagang Zhang • InTech eBooks
(2010)
Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large...
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Open Access
DOI: https://doi.org/10.5772/217
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)
Ming Han, Ge Sun, Paul F. Nealey, Juan J. de Pablo • npj Computational Materials
(2025)
Abstract Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41524-025-01836-7
J. W. Njoroge, M. Koeva, M. Farnaghi, D. Petrova-Antonova et al. • ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
(2026)
Digital twins are increasingly used in smart buildings to monitor energy use. This requires reliable sensor data, geospatial information, and semantically rich 3D building geometry. However, building sensor data often has inconsistent sampl...
DOAJ
Open Access (DOAJ)
DOI: 10.5194/isprs-annals-XII-4-W1-2026-251-2026
Maria Schuld, Ilya Sinayskiy, Francesco Petruccione • Contemporary Physics
(2014)
Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT...
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Open Access
DOI: https://doi.org/10.1080/00107514.2014.964942
James L. Melville, Edmund K. Burke, Jonathan D. Hirst • Combinatorial Chemistry & High Throughput Screening
(2009)
In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a partic...
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DOI: https://doi.org/10.2174/138620709788167980
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
William W. Cohen, Andrew McCallum, Sam T. Roweis (2008)
This volume contains the papers accepted to the 25th International Conference on Machine Learning (ICML 2008). ICML is the annual conference of the International Machine Learning Society (IMLS), and provides a venue for the presentation and...
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Michael J. Bianco, Peter Gerstoft, James A. Traer, Emma Reeves Ozanich et al. • The Journal of the Acoustical Society of America
(2019)
Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep lear...
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Open Access
DOI: https://doi.org/10.1121/1.5133944
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
K. W. DeGregory, Patrick K. Kuiper, Thomas DeSilvio, James D. Pleuss et al. • Obesity Reviews
(2018)
Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine lea...
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Open Access
DOI: https://doi.org/10.1111/obr.12667
PMC: 8176949
Xinchen Xu, Xingpeng Liao, Gaoning Shi, Yuyang Chen et al. • Journal of Magnesium and Alloys
(2026)
Though magnesium alloys possess attractive mechanical properties, their applications are severely limited by galvanic corrosion, due to the rapid anodic dissolution and anomalous hydrogen evolution. In this work, well-tempered metadynamics ...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.jma.2026.102217
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