Results 1 to 10 of about 1,171,833 (351)

Enhancing Spectrometer Performance with Unsupervised Machine Learning. [PDF]

J Phys Chem B
Solid-state NMR spectroscopy (SSNMR) is a powerful technique to probe structural and dynamic properties of biomolecules at an atomic level. Modern SSNMR methods employ multidimensional pulse sequences requiring data collection over a period of days to weeks.
Harding BD, Hu Z, Hiett A, Delaglio F, Henzler-Wildman KA, Rienstra CM.   +5 more
europepmc   +5 more sources

IoT Device Identification Using Unsupervised Machine Learning

Information, 2023
Device identification is a fundamental issue in the Internet of Things (IoT). Many critical services, including access control and intrusion prevention, are built on correctly identifying each unique device in a network.
Carson Koball, Bhaskar P. Rimal, Yong Wang, Tyler Salmen, Connor Ford   +4 more
doaj   +2 more sources

Clustering superconductors using unsupervised machine learning [PDF]

Physica C: Superconductivity and its Applications, 2022
In this work we used unsupervised machine learning methods in order to find possible clustering structures in superconducting materials data sets. We used the SuperCon database, as well as our own data sets complied from literature, in order to explore how machine learning algorithms groups superconductors.
Roter, B., Ninkovic, N., Dordevic, S. V.
openaire   +3 more sources

GBS-Assisted Quantum Unsupervised Machine Learning on a Universal Programmable Integrated Quantum Chip [PDF]

Research
Quantum machine learning stands poised as a forefront application for near-term quantum devices, addressing scalability challenges posed by classical computers in handling large datasets.
Huihui Zhu, Wei Luo, Rudai Yan, Chao Ren, Jia Guo, Zichao Zhao, Haoran Ma, Tian Chen, Feng Gao, Leong Chuan Kwek, Hong Cai, Yuehai Wang, Jianyi Yang, Ai-Qun Liu   +13 more
doaj   +2 more sources

Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry

Nature Communications, 2021
Machine learning has the potential to significantly speed-up the discovery of new materials in synthetic materials chemistry. Here the authors combine unsupervised machine learning and crystal structure prediction to predict a novel quaternary lithium ...
Andrij Vasylenko, Jacinthe Gamon, Benjamin B. Duff, Vladimir V. Gusev, Luke M. Daniels, Marco Zanella, J. Felix Shin, Paul M. Sharp, Alexandra Morscher, Ruiyong Chen, Alex R. Neale, Laurence J. Hardwick, John B. Claridge, Frédéric Blanc, Michael W. Gaultois, Matthew S. Dyer, Matthew J. Rosseinsky   +16 more
doaj   +2 more sources

Unsupervised Machine Learning and Band Topology [PDF]

Physical Review Letters, 2020
The study of topological bandstructures is an active area of research in condensed matter physics and beyond. Here, we combine recent progress in this field with developments in machine-learning, another rising topic of interest. Specifically, we introduce an unsupervised machine-learning approach that searches for and retrieves paths of adiabatic ...
Mathias S. Scheurer, Robert-Jan Slager
openaire   +6 more sources

Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection

Frontiers in Medical Technology, 2022
Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring.
Talha Iqbal, Adnan Elahi, William Wijns, Atif Shahzad, Atif Shahzad   +4 more
doaj   +2 more sources

Unsupervised Machine Learning to Identify Depressive Subtypes [PDF]

Healthcare Informatics Research, 2022
Objectives This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data. Methods Data from 18,314 depressed patients were used to create LDA models.
Benson Kung, Maurice Chiang, Gayan Perera, Megan Pritchard, Robert Stewart   +4 more
doaj   +6 more sources

Applying unsupervised machine learning to counterterrorism

Journal of Computational Social Science, 2022
Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM). ; To advance the agenda in counterterrorism, this work demonstrates how analysts can combine unsupervised machine learning, exploratory data analysis, and ...
R. Bridgelall
openaire   +3 more sources

Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems

Algorithms, 2022
Decision support systems with machine learning can help organizations improve operations and lower costs with more precision and efficiency. This work presents a review of state-of-the-art machine learning algorithms for binary classification and makes a
Hugo Silva, Jorge Bernardino
doaj   +1 more source