Results 51 to 60 of about 1,171,833 (351)

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

Sensors, 2023
Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades.
Kareem Eltouny, Mohamed Gomaa, Xiao Liang   +2 more
doaj   +1 more source

Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models

Remote Sensing, 2020
Landslide susceptibility prediction (LSP) has been widely and effectively implemented by machine learning (ML) models based on remote sensing (RS) images and Geographic Information System (GIS).
Zhilu Chang, Zhen Du, Fan Zhang, Faming Huang, Jiawu Chen, Wenbin Li, Zizheng Guo   +6 more
semanticscholar   +1 more source

Federated Unsupervised Machine Learning

SDU - University of Southern Denmark, Department of Mathematics and Computer Science, 2021
Federated learning (FL) is an emerging privacy-aware machine learning paradigm motivated by an increasing request for confidential and private data mining. Federated learning operates under the assumption that raw data cannot leave the data owners computer. Instead, only aggregated parameters can be exchanged between the participants.
openaire   +2 more sources

Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease

Scientific Reports, 2022
Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging.
Joonsan Lee, E. Warner, Salma Shaikhouni, M. Bitzer, M. Kretzler, D. Gipson, S. Pennathur, K. Bellovich, Z. Bhat, C. Gadegbeku, S. Massengill, K. Perumal, Jharna Saha, Ying-bao Yang, Jinghui Luo, Xin Zhang, L. Mariani, J. Hodgin, Arvind Rao   +18 more
semanticscholar   +1 more source

Unsupervised machine learning combined with 4D scanning transmission electron microscopy for bimodal nanostructural analysis

Scientific Reports
Unsupervised machine learning techniques have been combined with scanning transmission electron microscopy (STEM) to enable comprehensive crystal structure analysis with nanometer spatial resolution.
Koji Kimoto, J. Kikkawa, Koji Harano, O. Cretu, Yuki Shibazaki, Fumihiko Uesugi   +5 more
semanticscholar   +1 more source

Galaxy morphology — An unsupervised machine learning approach [PDF]

Astronomy and Computing, 2015
Astronomy & Computing ...
Schutter, Andrew, Shamir, Lior
openaire   +2 more sources

Optimization of Unsupervised Learning in Machine Learning

Journal of Physics: Conference Series, 2021
Abstract The Ombudsman of the Republic of Indonesia (hereinafter referred to as the Ombudsman) is a state institution (independent) that has the authority to oversee the administration of public services. The purpose of this study is to analyze the completion of reports/complaints from the public by using unsupervised learning techniques
Hidayatus Sibyan, Wildan Suharso, Edi Suharto, Melda Agnes Manuhutu, Agus Perdana Windarto   +4 more
openaire   +1 more source

An Unsupervised Machine Learning Clustering and Prediction of Differential Clinical Phenotypes of COVID-19 Patients Based on Blood Tests—A Hong Kong Population Study

Frontiers in Medicine, 2022
BackgroundTo better understand the different clinical phenotypes across the disease spectrum in patients with COVID-19 using an unsupervised machine learning clustering approach.Materials and MethodsA population-based retrospective study was conducted ...
Kitty Yu-Yeung Lau, Kei-Shing Ng, Ka-Wai Kwok, Kevin Kin-Man Tsia, Chun-Fung Sin, Ching-Wan Lam, Varut Vardhanabhuti   +6 more
doaj   +1 more source

Unraveling the Molecular Mechanisms of Glioma Recurrence: A Study Integrating Single‐Cell and Spatial Transcriptomics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Glioma recurrence severely impacts patient prognosis, with current treatments showing limited efficacy. Traditional methods struggle to analyze recurrence mechanisms due to challenges in assessing tumor heterogeneity, spatial dynamics, and gene networks.
Lei Qiu, Yinjiao Fei, Jiaxuan Ding, Kexin Shi, Jinyan Luo, Yuchen Zhu, Xingjian Sun, Gefei Jiang, Yuandong Cao, Weilin Xu, Shu Zhou   +10 more
wiley   +1 more source

Ensembling Supervised and Unsupervised Machine Learning Algorithms for Detecting Distributed Denial of Service Attacks

Algorithms
The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly disruption of services across all sectors and critical ...
Saikat Das, Mohammad Ashrafuzzaman, Frederick T. Sheldon, Sajjan Shiva   +3 more
doaj   +1 more source