Search Results - "Unsupervised Machine Learning Methods"

Unsupervised machine learning methods and emerging applications in healthcare by Eckhardt, Christina M., Madjarova, Sophia J., Williams, Riley J., Ollivier, Mattheu, Karlsson, Jón, Pareek, Ayoosh, Nwachukwu, Benedict U.

“…Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data…”
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A comparative analysis of unsupervised machine‐learning methods in PSG‐related phenotyping by Ghorvei, Mohammadreza, Karhu, Tuomas, Hietakoste, Salla, Ferreira‐Santos, Daniela, Hrubos‐Strøm, Harald, Islind, Anna Sigridur, Biedebach, Luka, Nikkonen, Sami, Leppänen, Timo, Rusanen, Matias

“…Summary Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover…”
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Using Unsupervised Machine Learning Methods to Cluster Comorbidities in a Population‐Based Cohort of Patients With Rheumatoid Arthritis by Crowson, Cynthia S., Gunderson, Tina M., Davis, John M., Myasoedova, Elena, Kronzer, Vanessa L., Coffey, Caitrin M., Atkinson, Elizabeth J.

“… Unsupervised machine learning methods of interest included hierarchical clustering, factor analysis, K…”
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Application of unsupervised machine learning methods to actor–critic structures in reinforcement learning for training and online implementation by Beahr, Daniel, Bhattacharyya, Debangsu

“…A fundamental obstacle to the implementation of reinforcement learning (RL) to continuous systems is the large amount of data and training that must take place…”
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OpenStreetMap quality assessment using unsupervised machine learning methods by Jacobs, Kent T., Mitchell, Scott W.

“…The reliability and quality of volunteered geographic information (VGI) continue to be pressing concerns. Many VGI projects lack standard geospatial data…”
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A novel framework for groundwater quality evaluation in industrial zone using unsupervised machine learning methods by Panneerselvam, Mukesh, Govindan, Venkatesan, Sakthivel, Lakshmana Prabu

“… The integrated assessment of groundwater using unsupervised machine learning method associated human health risk assessment are identified as research gap in the study region…”
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Precision soil sampling strategy for the delineation of management zones in olive cultivation using unsupervised machine learning methods by Bougiouklis, John-Nick, Barouchas, Pantelis E., Petropoulos, Panagiotis, Tsesmelis, Dimitrios E., Moustakas, Nicholas

“…Climate change and environmental degradation pose a significant threat to the global community. Soil management is one of the critical factors for achieving…”
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Unsupervised Machine Learning Methods for Anomaly Detection in Network Packets by Park, Hyoseong, Shin, Dongil, Park, Chulgyun, Jang, Jisoo, Shin, Dongkyoo

“…Traditional intrusion detection systems (IDS) based on packet signatures are widely used in network security but often fail to detect previously unseen…”
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A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods by Fidan, Huseyin, Erkan Yuksel, Mehmet

“…•Environmental variables should be used for restrictions.•Unsupervised machine learning techniques should be used instead of threshold values.•Fuzzy approaches…”
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Review of unsupervised machine learning methods in active infrared thermography for defect detection and analysis by Liu, Yi, Yao, Yuan, Wang, Fumin, Sfarra, Stefano, Liu, Kaixin

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Mwd-based real-time identification of rock weathering: A comparison of supervised and unsupervised machine learning methods by Li, Yang, Chen, Jiayao, Shen, Yifan, Fang, Qian, Man, Jianhong

“…•A hybrid RS-RF model is proposed for efficient classification of rock weathering degrees.•A database with 264,570 MWD from drill-and-blast tunnel excavation…”
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The relationship between clinical subtypes, prognosis, and treatment in ICU patients with acute cholangitis using unsupervised machine learning methods by Sheng, Yuanhui, Xu, Shan, Zhang, Shu, Zhang, Dan

“…Background Acute cholangitis (AC) presents with significant clinical heterogeneity, and existing severity classifications have limited prognostic value in…”
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Unsupervised Machine Learning Methods for Artifact Removal in Electrodermal Activity by Subramanian, Sandya, Tseng, Bryan, Barbieri, Riccardo, Brown, Emery N

“…Artifact detection and removal is a crucial step in all data preprocessing pipelines for physiological time series data, especially when collected outside of…”
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Fault Detection and Diagnosis Based on Unsupervised Machine Learning Methods: A Kaplan Turbine Case Study by Michalski, Miguel A. C., Melani, Arthur H. A., da Silva, Renan F., de Souza, Gilberto F. M., Hamaji, Fernando H.

“…From the breakdown of the Kaplan rotor of a hydrogenerator unit and the monitored data collected during its operation before such a failure, this work presents…”
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Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods by Hashemzadeh, Mahdi, Adlpour Azar, Baharak

“…•A combination of supervised and unsupervised machine learning methods is used for extracting the retinal blood vessels…”
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Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods by Zimmering, Bernd, Niggemann, Oliver, Hasterok, Constanze, Pfannstiel, Erik, Ramming, Dario, Pfrommer, Julius

“…In the field of Cyber-Physical Systems (CPS), there is a large number of machine learning methods, and their intrinsic hyper-parameters are hugely varied…”
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Clustering honey samples with unsupervised machine learning methods using FTIR data by AVCU, FATIH MEHMET

“…This study utilizes Fourier transform infrared (FTIR) data from honey samples to cluster and categorize them based on their spectral characteristics. The aim…”
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Classification of lidar measurements using supervised and unsupervised machine learning methods by Farhani, Ghazal, Sica, Robert J., Daley, Mark Joseph

“…While it is relatively straightforward to automate the processing of lidar signals, it is more difficult to choose periods of “good” measurements to process…”
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Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer by Gal, Jocelyn, Bailleux, Caroline, Chardin, David, Pourcher, Thierry, Gilhodes, Julia, Jing, Lun, Guigonis, Jean-Marie, Ferrero, Jean-Marc, Milano, Gerard, Mograbi, Baharia, Brest, Patrick, Chateau, Yann, Humbert, Olivier, Chamorey, Emmanuel

“…[Display omitted] Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological…”
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Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity by Xue, Yan, Song, Menghuan, Ung, Carolina Oi Lam, Hu, Hao

“…Background Obesity is a disease with high heterogeneity. Both overall obesity and central obesity are associated with increased risks of having…”
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