Search Results - "Unsupervised Machine Learning Methods"

Review of unsupervised machine learning methods in active infrared thermography for defect detection and analysis

Authors: Yi Liu Yuan Yao Fumin Wang et al.

Source: Quantitative InfraRed Thermography Journal. :1-28

Subject Terms: active infrared thermography, defect detection, Non-destructive testing, review, thermographic data analysis, unsupervised machine learning

Access URL: https://www.tandfonline.com/doi/full/10.1080/17686733.2025.2540662?af=R
https://hdl.handle.net/11697/269960
https://doi.org/10.1080/17686733.2025.2540662

Revealing the hidden faults of the Oklahoma basement through unsupervised machine-learning methods and the integration of earthquake data

Authors: Diana K. Salazar Florez Brett M. Carpenter Heather Bedle

Source: Interpretation. 13:T833-T857

Advanced Condition Monitoring of Metal Oxide Varistors (MOVs) in DC Circuit Breakers Using Unsupervised Machine Learning Methods

Authors: Soroush Naeiji Amirhussein Zia Hamid Jafarabadi Ashtiani et al.

Source: 2025 IEEE 34th International Symposium on Industrial Electronics (ISIE). :1-6

Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity

Authors: Xue, Yan Song, Menghuan Ung, Carolina Oi Lam et al.

Source: BMC Cardiovasc Disord

Subject Terms: Research

Research of unsupervised machine learning methods for data analy-sis and preparation and modern approaches to building machine learning models – automated machine learning systems

Authors: I.A. Popova G.I. Afanasyev V.B. Timofeev et al.

Source: Neurocomputers. :47-55

UNSUPERVISED MACHINE LEARNING METHODS FOR REAL-TIME ANOMALY DETECTION IN ENDPOINTS

Authors: K. N. Asgarov

Source: Journal of Modern Technology and Engineering. 9:141-155

Predicting Cancellations of Hotel Reservations with Unsupervised Machine Learning Methods

Authors: Abhishek Kumar Barkha Mahato AGP Kujur et al.

Source: 2024 International BIT Conference (BITCON). :1-6

Unsupervised Machine Learning Methods as a Powerful Tool for Lithological Pattern Recognition Using Wells Logs Data

Authors: A. R. Narino M. Mergany A. Alaraifi

Source: Middle East Oil, Gas and Geosciences Show (MEOS GEO).

The relationship between clinical subtypes, prognosis, and treatment in ICU patients with acute cholangitis using unsupervised machine learning methods

Authors: Sheng, Yuanhui Xu, Shan Zhang, Shu et al.

Source: BMC Infect Dis

Subject Terms: Research

Access URL: https://pubmed.ncbi.nlm.nih.gov/40760727/
https://pmc.ncbi.nlm.nih.gov/articles/PMC12323092/
https://doi.org/10.1186/s12879-025-11346-y

Application of unsupervised machine learning methods to actor–critic structures in reinforcement learning for training and online implementation

Authors: Daniel Beahr Debangsu Bhattacharyya

Source: Computers & Chemical Engineering. 204:109392

Using Unsupervised Machine Learning Methods and the Nasa Snowex Swesarr Instrument to Study How Snow Water Equivalent is Changing with Climate Change

Authors: Gabriela Himmele Alicia Joseph Evi Ofekeze et al.

Source: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. :1869-1872

A novel framework for groundwater quality evaluation in industrial zone using unsupervised machine learning methods.

Authors: Panneerselvam M Govindan V Sakthivel LP

Source: Environmental geochemistry and health [Environ Geochem Health] 2026 Feb 14; Vol. 48 (4), pp. 161. Date of Electronic Publication: 2026 Feb 14.

Publication Type: Journal Article

Journal Info: Publisher: Kluwer Academic Publishers Country of Publication: Netherlands NLM ID: 8903118 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2983 (Electronic) Linking ISSN: 02694042 NLM ISO Abbreviation: Environ Geochem Health Subsets: MEDLINE

MeSH Terms: Groundwater*/chemistry , Groundwater*/analysis , Environmental Monitoring*/methods , Water Pollutants, Chemical*/analysis , Water Quality* , Unsupervised Machine Learning*, India ; Seasons ; Humans ; Principal Component Analysis ; Industry ; Risk Assessment ; Cluster Analysis

Supervised and unsupervised machine learning methods for modelling current and future habitat of Peruvian anchovy

Authors: Mariana Hill Tianfei Xue Jaard Hauschildt et al.

Uncertainty assessment in unsupervised machine-learning methods for deepwater channel seismic facies using outcrop-derived 3D models and synthetic seismic data

Authors: Karelia La Marca Heather Bedle Lisa Stright et al.

Source: Interpretation. 12:T303-T319

Subject Terms: 01 natural sciences, 0105 earth and related environmental sciences

Combining Supervised and Unsupervised Machine Learning Methods for Improving Credit Card Fraud Detection

Authors: Rabab Cherkaoui El Mokhtar En-Naimi Mohamed Kouissi

Source: Proceedings of the 7th International Conference on Networking, Intelligent Systems and Security. :1-5

Unsupervised Machine Learning Methods for Detecting Anomalies in the Structural Behavior of a University Laboratory

Authors: Marco Claudio De Simone Leidy Esperanza Pamplona Berón Domenico Guida

Source: Lecture Notes in Networks and Systems ISBN: 9783031816840

Precision soil sampling strategy for the delineation of management zones in olive cultivation using unsupervised machine learning methods

Authors: John-Nick Bougiouklis Pantelis E. Barouchas Panagiotis Petropoulos et al.

Source: Sci Rep
Scientific Reports, Vol 15, Iss 1, Pp 1-26 (2025)

Subject Terms: Principal Component Analysis, Greece, Science, Climate Change, Agriculture, Olive orchards, Article, Precision soil sampling, Soil, Soil management zones, Olea, Machine learning, Medicine, Cluster Analysis, Unsupervised Machine Learning

Access URL: https://pubmed.ncbi.nlm.nih.gov/40064954
https://doaj.org/article/5a54acc0189a4d57a9a51130e2704985

Advancements in sepsis detection: A review of supervised and unsupervised machine learning methods versus traditional approaches

Authors: Kanthavel R Dhaya R

Source: Innovation and Emerging Technologies. 12

Unsupervised Machine Learning Methods for Determining Special Points of the Polymerase Chain Reaction Fluorescence Accumulation Curve

Authors: A. V. Orekhov M. A. Potekhina

Source: Moscow University Physics Bulletin. 78:S169-S179

Supervised and unsupervised machine learning methods for modelling current and future habitat of Peruvian anchovy

Authors: Hill-Cruz, Mariana Xue, Tianfei Hauschildt, Jaard et al.

Index Terms: Article, NonPeerReviewed

URL: https://oceanrep.geomar.de/id/eprint/62286/
https://doi.org/10.1101/2025.05.08.652876
https://oceanrep.geomar.de/id/eprint/62286
https://doi.org/10.1101/2025.05.08.652876
doi:10.1101/2025.05.08.652876