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Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!
Engineering, 2023 By combining machine learning with the design of experiments, thereby achieving so-called active machine learning, more efficient and cheaper research can be conducted.
Yannick Ureel +6 more
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Active learning machine learns to create new quantum experiments [PDF]
Proceedings of the National Academy of Sciences, 2017 Significance Quantum experiments push the envelope of our understanding of fundamental concepts in quantum physics. Modern experiments have exhaustively probed the basic notions of quantum theory.
A. Melnikov +6 more
semanticscholar +8 more sources
Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction
Sensors, 2022 The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system.
Ibrahim M. El-Hasnony +3 more
doaj +2 more sources
Visual Informatics, 2019 Supervised machine learning techniques require labelled multivariate training datasets. Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive visualisations. Using appropriate techniques,
Mohammad Chegini +5 more
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Small data machine learning in materials science
npj Computational Materials, 2023 This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced.
Pengcheng Xu +3 more
doaj +2 more sources
ACTIVE LEARNING ON LARGE HYPERSPECTRAL DATASETS: A PREPROCESSING METHOD [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Machine learning algorithms demonstrated promising results for hyperspectral semantic segmentation. However, they strongly rely on the quality of training datasets.
R. Thoreau +5 more
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Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning [PDF]
Nature Communications, 2023 Silicon–oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis.
Linus C. Erhard +3 more
semanticscholar +1 more source
A Classification Method of Agricultural News Text Based on BERT and Deep Active Learning [PDF]
Nongye tushu qingbao xuebao, 2022 [Purpose/Significance] At present, most of the training models used in the research of news classification are non-active learning. There are common problems about these models, including data cannot be labeled immediately and the labeling cost is too ...
SHI Yunlai, CUI Yunpeng, DU Zhigang
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AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden
Sensors, 2023 The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their
Oliver J. Fisher +4 more
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Digital Chemical Engineering, 2022 In this work, we develop a model reduction method using sensitivity analysis and active learning to improve the computational efficiency of machine learning modeling of nonlinear processes.
Tianyi Zhao, Yingzhe Zheng, Zhe Wu
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