Suchergebnisse - "Unsupervised machine learning algorithms"

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

Autoren: Owoade, Ayoade Akeem

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

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

Dateibeschreibung: application/pdf

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

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

Autoren: Kiren Roopraj Nelendran Pillay Navin Singh

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

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

Autoren: Alireza Simorgh Khosro Khandani Maryam Amiri

Quelle: 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

Autoren: Rehaan Hussain Shady S. Refaat

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

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

Autoren: Santosh Kumar Pitam Singh Bharti Das et al.

Quelle: Disaster Advances. 18:1-8

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

Autoren: Huayan Zuo Qiu Bi Xiaolong Liu et al.

Quelle: Frontiers in Public Health. 13

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

Autoren: Shahab Ud Din Sungwon Hong

Quelle: Biodiversity and Conservation.

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

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

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

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

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

Advancing Sentiment Analysis in Restaurant Reviews through Unsupervised Machine Learning Algorithms

Quelle: International Journal of Intelligent Engineering and Systems. 17:1108-1121

Hardware Trojan Detection using Unsupervised Machine Learning Algorithms in the Gate-level Netlist

Autoren: Karthikeyan S Prabhu E

Quelle: 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). :1-6

Supervised and Unsupervised Machine Learning Algorithms: An Empirical Evaluation

Autoren: Rajarajan Rajkumar Li Zhang Vivian Sedov et al.

Quelle: Intelligent Management of Data and Information in Decision Making. :299-306

Discovery of Course Success Using Unsupervised Machine Learning Algorithms

Autoren: Cam, Emre Ozdag, Muhammet Esat

Quelle: Malaysian Online Journal of Educational Technology. 2021 9(1):26-47.

Peer Reviewed: Y

Page Count: 22

Descriptors: Artificial Intelligence, Academic Achievement, Mathematics, Computer Science Education, Educational Technology, College Students, Foreign Countries, Learning Analytics, Student Characteristics

Geografische Kategorien: Turkey

Hybrid modeling of deep neural networks and unsupervised machine learning algorithms for missing well log prediction based on geological lithofacies similarities

Autoren: Wakeel Hussain Miao Luo Muhammad Ali et al.

Quelle: Journal of Applied Geophysics. 241:105846

Segmentation for Factoring Customers: Using Unsupervised Machine Learning Algorithms

Autoren: Yalçuva, Berat Akçay, Ahmet Ertuğrul, Seyit et al.

Weitere Verfasser: Yalçuva, Berat Akçay, Ahmet Ertuğrul, Seyit et al.

Quelle: 2023 Innovations in Intelligent Systems and Applications Conference (ASYU). :1-7

Schlagwörter: Segmentation model, Machine learning, Customer segmentation, Clustering, Factoring customers

Dateibeschreibung: application/pdf

DETECTING ANOMALIES USING UNSUPERVISED MACHINE LEARNING ALGORITHMS

Autoren: Raya Jasim EASA Asma Salim YAHYA

Quelle: MINAR International Journal of Applied Sciences and Technology. :219-229

Anomaly detection using unsupervised machine learning algorithms: A simulation study

Autoren: Edmund Fosu Agyemang

Quelle: Scientific African, Vol 26, Iss , Pp e02386- (2024)

Schlagwörter: Anomaly detection, Unsupervised machine learning algorithms, One-class support vector machine, Isolation forest, Local outlier factor, Robust covariance, Science

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2468227624003284; https://doaj.org/toc/2468-2276

Zugangs-URL: https://doaj.org/article/aa6a3d4aa3194f3aac81cba8ab595940

Application of Unsupervised Machine Learning Algorithms for identifying critical river confluence in a mountainous watershed

Autoren: Naman Rajouria Pragati Parajapati Sanjeev Kumar Jha

Mathematical enhancement of unsupervised machine learning algorithms for optimal lifeline donor knowledge extraction

Autoren: P. Ramesh Naidu Pratik Gite Vaskar Deka et al.

Quelle: Journal of Statistics and Management Systems. 27:465-477

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

Unsupervised machine learning algorithms identify expected haemorrhage relationships but define unexplained coagulation profiles mapping to thrombotic phenotypes in hereditary haemorrhagic telangiectasia

Autoren: Ghazel Mukhtar Claire L. Shovlin

Quelle: EJHaem
eJHaem, Vol 4, Iss 3, Pp 602-611 (2023)

Schlagwörter: t‐SNE, anaemia, Sickle Cell, Thrombosis, and Classical Haematology, high‐dimensional data, genetic disorders, Diseases of the blood and blood-forming organs, haemorrhage, RC633-647.5, 3. Good health

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37601877
https://doaj.org/article/294455ac894e4d7faf5ded714f437c2a
http://hdl.handle.net/10044/1/106571

Damage Mechanism Characterization of Glass Fiber-Reinforced Polymer Composites: A Study Using Acoustic Emission Technique and Unsupervised Machine Learning Algorithms

Autoren: Jorge Palacios Moreno Hadi Nazaripoor Pierre Mertiny

Quelle: Journal of Composites Science. 9:426