Results 41 to 50 of about 1,171,833 (351)
A Multi-Considered Seed Coat Pattern Classification of Allium L. Using Unsupervised Machine Learning
Plants, 2022 The seed coat sculpture is one of the most important taxonomic distinguishing features. The objective of this study is to classify coat patterns of Allium L. seeds into new groups using scanning electron microscopy unsupervised machine learning. Selected
Gantulga Ariunzaya +4 more
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Applied Sciences, 2022 A submarine landslide is a well-known geohazard that can cause significant damage to offshore engineering facilities. Most standard predicting and mapping methods require expert knowledge, supervision, and fieldwork.
Xing Du +4 more
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Explainable Unsupervised Machine Learning for Cyber-Physical Systems
IEEE Access, 2021 Cyber-Physical Systems (CPSs) play a critical role in our modern infrastructure due to their capability to connect computing resources with physical systems.
Chathurika S. Wickramasinghe +4 more
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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition
Journal of Chemical Information and Modeling, 2018 Inspired by natural language processing techniques, we here introduce Mol2vec, which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Like the Word2vec models, where vectors of closely related words
Sabrina Jaeger, S. Fulle, S. Turk
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Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data [PDF]
Integrating Materials and Manufacturing Innovation, 2020 Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled datasets and for achieving maximum machine learning performance.
R. Cohn, Elizabeth Holm
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Non-Line-of-Sight Identification Based on Unsupervised Machine Learning in Ultra Wideband Systems
IEEE Access, 2019 Identification of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation conditions is very useful in ultra wideband localization systems. In the identification, supervised machine learning is often used, but it requires exorbitant efforts to ...
Jiancun Fan, Ahsan Saleem Awan
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Unsupervised Machine Learning for Anomaly Detection in Multivariate Time Series Data of a Rotating Machine from an Oil and Gas Platform [PDF]
Journal of Systemics, Cybernetics and Informatics, 2021 Deep Learning (DP) models have been successfully applied to detect and predict failures in rotating machines. However, these models are often based on the supervised learning paradigm and require annotated data with operational status labels (e.g. normal
Ilan Sousa Figueirêdo +7 more
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An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery
Ain Shams Engineering Journal, 2021 Sediment deposition causes the reduction of aquatic habitats and increase of water velocities within rivers, which negatively impacts the environment and the surrounding ecology. This makes the prediction of river sediment deposition a key factor for the
Awatif Aziz +4 more
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Unsupervised Machine Learning on Encrypted Data [PDF]
, 2019 In the context of Fully Homomorphic Encryption, which allows computations on encrypted data, Machine Learning has been one of the most popular applications in the recent past. All of these works, however, have focused on supervised learning, where there is a labeled training set that is used to configure the model.
Jäschke, Angela, Armknecht, Frederik
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UniverseThis review investigates the application of unsupervised machine learning algorithms to astronomical data. Unsupervised machine learning enables researchers to analyze large, high-dimensional, and unlabeled datasets and is sometimes considered more ...
Chih-Ting Kuo, Duo Xu, Rachel Friesen
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