Search Results - Unsupervised Machine Learning Algorithm

Development of customer segmentation system using supervised and unsupervised machine learning algorithms

Authors: Owoade, Ayoade Akeem

Source: Science World Journal; Vol. 20 No. 2 (2025); 554-565

Subject Terms: Customer, Segmentation, Clustering, Rule-based, Traditional Method, Machine learning

File Description: application/pdf

Access URL: https://www.ajol.info/index.php/swj/article/view/303104

Sustainable Selection of Machine Learning Algorithm for Gender-bias Attenuated Prediction

Authors: Orbay, Raik Wikner, Evelina

Source: IEEE Open Journal of Vehicular Technology. 6:348-358

Subject Terms: Feature engineering, Energy consumption, Battery electric vehicles, Unsupervised/supervised learning, Driving style classification, Machine learning

File Description: electronic

Access URL: https://research.chalmers.se/publication/543996
https://research.chalmers.se/publication/543996/file/543996_Fulltext.pdf

Unsupervised Deep-Learning-Based Error Image Prior (DLEIP) Algorithm for Lung Electrical Impedance Tomography (EIT)

Authors: Hanyu Zhang Rui Cai Yunjie Yang et al.

Source: IEEE Internet of Things Journal. 12:31399-31407

Intrusion Detection in an Industrial Control System Using Unsupervised Machine Learning Algorithms

Authors: Kiren Roopraj Nelendran Pillay Navin Singh

Source: 2025 Conference on Information Communications Technology and Society (ICTAS). :1-6

Comparative analysis of outcomes in high KDPI spectrum kidney transplants using unsupervised machine learning algorithm.

Authors: Moein M Golkarieh A Vlassis I et al.

Source: PloS one [PLoS One] 2025 Aug 26; Vol. 20 (8), pp. e0324265. Date of Electronic Publication: 2025 Aug 26 (Print Publication: 2025).

Publication Type: Journal Article; Comparative Study

Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

MeSH Terms: Kidney Transplantation*/adverse effects , Unsupervised Machine Learning*, Humans ; Female ; Male ; Middle Aged ; Retrospective Studies ; Adult ; Graft Survival ; Tissue Donors ; Algorithms ; Graft Rejection ; United States ; Treatment Outcome

Machine Learning contributions to the diagnosis of Categorical Psychiatric Constructs based on Structured Clinical Interviews: Screening Optimization and Unsupervised Classification.

Authors: Piqueras Marqués, Jorge

Contributors: University/Department: Universitat Internacional de Catalunya. Departament de Medicina

Thesis Advisors: García Forero, Carlos, Leiva, David

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Epidemiology, Mental Health, Phychiatric diagnosis, Machine learning, Genetic Algorithm, Deep Clustering, Screening, Major Depression, Epidemiologia

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/696035

Investigating Unsupervised Machine Learning in Public Procurement Fraud Detection: A Case Study of POTRAZ Using the Isolation Forest Algorithm

Authors: Richard Majilana

Source: International Journal of Computer Science and Mobile Computing. 14:134-136

Illegal Miner Detection based on Dynamic Mode Decomposition and Unsupervised Machine Learning Algorithms

Authors: Alireza Simorgh Khosro Khandani Maryam Amiri

Source: Journal of Green Energy Research and Innovation. 2:79-88

Incipient Stator Winding Fault Detection and Severity Estimation in Induction Motors With Unsupervised Machine Learning Algorithms

Authors: Rehaan Hussain Shady S. Refaat

Source: 2025 IEEE 19th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG). :1-6

Introduction to clustering unsupervised machine learning algorithms applied to power quality disturbances

Authors: Catelani M. Ciani L. Alfonso C. G. et al.

Source: 2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT). :286-291

Subject Terms: Anomaly Detection, Clustering, Electrical Systems, Power Quality, Unsupervised Machine Learning

Access URL: https://ieeexplore.ieee.org/document/10584168
https://doi.org/10.1109/MetroInd4.0IoT61288.2024.10584168
https://hdl.handle.net/2158/1393572

A Comparative Study of Unsupervised Machine Learning Algorithms for Recognition of Change in Vegetation, Water and Built up Land Areas

Authors: Santosh Kumar Pitam Singh Bharti Das et al.

Source: Disaster Advances. 18:1-8

Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning

Authors: Ai-Ho Liao Chih-Hung Wang Chong-Yu Wang et al.

Source: Ultrasound in Medicine & Biology. 50:1058-1068

Subject Terms: Muscular Dystrophy, Duchenne, Male, Deep Learning, Adolescent, Humans, Cluster Analysis, Diagnosis, Computer-Assisted, Child, Algorithms, Ultrasonography, Pattern Recognition, Automated

Access URL: https://pubmed.ncbi.nlm.nih.gov/38637169

Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms

Authors: Tânia F. Cova Cláudia Ferreira Sandra C. C. Nunes et al.

Source: Journal of Agricultural and Food Chemistry. 73:6173-6188

Access URL: https://pubmed.ncbi.nlm.nih.gov/40013497

A deep semi-supervised machine learning algorithm for detecting transportation modes based on GPS tracking data

Authors: Sadeghian, Paria Golshan, Arman Zhao, Xiaoyun, Ph.D. et al.

Source: Transportation. 52:1745-1765

Subject Terms: Travel identification, LSTM Autoencoder, Unsupervised learning, Deep learning, GPS tracking data

File Description: electronic

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:du-48203
https://doi.org/10.1007/s11116-024-10472-x

The value of unsupervised machine learning algorithms based on CT and MRI for predicting sarcopenia

Authors: Huayan Zuo Qiu Bi Xiaolong Liu et al.

Source: Frontiers in Public Health. 13

UNSUPERVISED MACHINE LEARNING FOR SEISMIC ANOMALY DETECTION: ISOLATION FOREST ALGORITHM APPLICATION TO INDONESIAN EARTHQUAKE DATA

Authors: Gregorius Airlangga

Source: Jurnal Lebesgue, Vol 4, Iss 3, Pp 1827-1836 (2023)

Subject Terms: earthquake, QA1-939, isolation forest algorithm, unsupervised machine learning algorithm, anomaly detection, Mathematics

Access URL: https://doaj.org/article/25150b700e8d4c028b54056750bc4c04

UNSUPERVISED MACHINE LEARNING FOR SEISMIC ANOMALY DETECTION: LOCAL OUTLIER FACTOR ALGORITHM TO INDONESIAN EARTHQUAKE DATA

Authors: Gregorius Airlangga

Source: Jurnal Lebesgue, Vol 4, Iss 3, Pp 1892-1901 (2023)

Subject Terms: earthquake, QA1-939, unsupervised machine learning, anomaly detection, Mathematics, local outlier factor algorithm

Access URL: https://doaj.org/article/e7ac36834ab64d97a274d3f3006d21e9

Feedback Attention to Enhance Unsupervised Deep Learning Image Registration in 3D Echocardiography

Authors: Md. Kamrul Hasan Yihao Luo Guang Yang et al.

Source: IEEE Transactions on Medical Imaging. 44:2230-2243

Subject Terms: Adult, Deep Learning, Echocardiography, Three-Dimensional, Humans, Heart, Female, Algorithms, Unsupervised Machine Learning

Access URL: https://pubmed.ncbi.nlm.nih.gov/40030923

Unveiling patterns in wildlife conservation attitudes in Gilgit-Baltistan, Pakistan, using unsupervised machine learning algorithms

Authors: Shahab Ud Din Sungwon Hong

Source: Biodiversity and Conservation.

Unsupervised machine learning in financial anomaly detection: clustering algorithms vs. dedicated methods

Authors: Radosław J. Woźniak

Source: Przegląd Teleinformatyczny. 11:29-46

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0101 mathematics, 01 natural sciences