Suchergebnisse - "machine learning service"

Tire-Wear Estimation System Using Semantic Segmentation and Its Deployment

Autoren: Min Jun Kang Kyung Beom Kwon Hyung Il Koo

Quelle: IEEE Access, Vol 13, Pp 62591-62599 (2025)

Schlagwörter: machine learning service, Tire-wear estimation, Electrical engineering. Electronics. Nuclear engineering, semantic segmentation, TK1-9971

Zugangs-URL: https://doaj.org/article/cdf4a4448c4a4201947c3c842071658c

Research Status of Laser Additive Manufacturing Surface Integrity Theory and Control Technology

Autoren: XU Yifei, LIU Fuchao, SUN Yaoning

Quelle: Cailiao Baohu, Vol 58, Iss 5, Pp 1-11 (2025)

Schlagwörter: laser additive manufacturing； surface integrity； machine learning； service performance, Materials of engineering and construction. Mechanics of materials, TA401-492, Technology

Dateibeschreibung: electronic resource

Relation: http://www.mat-pro.com/fileup/1001-1560/PDF/20250501.pdf; https://doaj.org/toc/1001-1560

Zugangs-URL: https://doaj.org/article/74ad9606fa5547a983da02ae36080559

Visual Service Diagnostics and Service Provider Recommendation

Autoren: P Haneefa, Sabhamol Mathew, Meera Rose

Schlagwörter: Visual diagnostics, AI-powered service detection, machine learning, service provider recommendation, home service automation, CNN algorithm, real-time booking, image processing, predictive analytics, user experience enhancement

Trends in Web APIs Classification, Selection, and Composition

Autoren: Andročec, Darko Altinok, Riza

Weitere Verfasser: Andročec, Darko Altinok, Riza

Quelle: 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET). :1-5

Schlagwörter: web API, web service, service selection, service composition, IoT service, machine learning service, deep learning service

Zugangs-URL: https://www.bib.irb.hr/1179447
https://doi.org/10.1109/icecet52533.2021.9698547
https://ieeexplore.ieee.org/document/9698547
https://doi.org/10.1109/ICECET52533.2021.9698547

Proactive Container Auto-scaling for Cloud Native Machine Learning Services

Autoren: Buchaca Prats, David Berral García, Josep Lluís Wang, Chen et al.

Quelle: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

Schlagwörter: Cloud native, Computació en núvol, 02 engineering and technology, Auto-scaling, Assignació de recursos, Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors, Machine learning, Aprenentatge automàtic, Machine learning service, 0202 electrical engineering, electronic engineering, information engineering, Cloud computing, Resource allocation, Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC], Container

Dateibeschreibung: application/pdf

Zugangs-URL: https://upcommons.upc.edu/bitstream/2117/340053/1/_IEEE_CLOUD_2020_shortpaper_Proactive_Container_Auto_scaling_for_Cloud_Native_Machine_Learning_Services.pdf
https://upcommons.upc.edu/bitstream/2117/340053/1/_IEEE_CLOUD_2020_shortpaper_Proactive_Container_Auto_scaling_for_Cloud_Native_Machine_Learning_Services.pdf
https://upcommons.upc.edu/handle/2117/340053
https://dblp.uni-trier.de/db/conf/IEEEcloud/IEEEcloud2020.html#PratsB0Y20

A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine-Learning Approach

Autoren: Alireza Rezazadeh

Quelle: Forecasting, Vol 2, Iss 3, Pp 267-283 (2020)
Forecasting
Volume 2
Issue 3
Pages 15-283

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Science (General), microsoft azure machine-learning service, Machine Learning (stat.ML), 02 engineering and technology, costumer relation management, Machine Learning (cs.LG), business to business sales prediction, Q1-390, machine learning, Statistics - Machine Learning, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, predictive modeling, Mathematics

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.mdpi.com/2571-9394/2/3/15/pdf
http://arxiv.org/abs/2002.01441
https://doaj.org/article/6d2c1fff27844a4197bb9be3af3d4627
https://www.mdpi.com/2571-9394/2/3/15
https://dblp.uni-trier.de/db/journals/corr/corr2002.html#abs-2002-01441
https://ideas.repec.org/a/gam/jforec/v2y2020i3p15-283d395634.html
https://www.mdpi.com/2571-9394/2/3/15/pdf
https://doaj.org/article/6d2c1fff27844a4197bb9be3af3d4627
https://ui.adsabs.harvard.edu/abs/2020arXiv200201441R/abstract
https://arxiv.org/pdf/2002.01441.pdf
https://arxiv.org/abs/2002.01441

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Autoren: Yafei Lei Wanlu Jiang Anqi Jiang et al.

Quelle: Processes, Vol 7, Iss 9, p 589 (2019)

Schlagwörter: hydraulic valve, fault diagnosis, principal component analysis (PCA), extreme gradient boosting (XGBoost), HUAWEI Cloud machine learning service (MLS), Chemical technology, TP1-1185, Chemistry, QD1-999

Relation: https://www.mdpi.com/2227-9717/7/9/589; https://doaj.org/toc/2227-9717; https://doaj.org/article/f1d75dff2b414199849eea2892979eb3

Verfügbarkeit: https://doi.org/10.3390/pr7090589
https://doaj.org/article/f1d75dff2b414199849eea2892979eb3

Monitoring and Optimization of ATLAS Tier 2 Center GoeGrid

Autoren: Magradze, Erekle

Thesis Advisors: Quadt, Arnulf Prof. Dr.

Schlagwörter: 530, Physik (PPN621336750), ATLAS Experiment, Grid Computing, Machine Learning, Service Status Identification, Service Status Prediction, Automation

Verfügbarkeit: http://hdl.handle.net/11858/00-1735-0000-0028-86DB-0

Monitoring and Optimization of ATLAS Tier 2 Center GoeGrid

Autoren: Magradze, Erekle Quadt, Arnulf Yahyapour, Ramin

Weitere Verfasser: Magradze, Erekle Quadt, Arnulf Yahyapour, Ramin

Verfügbarkeit: http://data.europeana.eu/aggregation/europeana/2048441/item_EGNWT55RDOU7NYRISBFYVEKCQ7KGZYKI

Proactive container auto-scaling for cloud native machine learning services

Autoren: Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors Barcelona Supercomputing Center Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions et al.

Index Begriffe: Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors, Cloud computing, Machine learning, Resource allocation, Cloud native, Machine learning service, Container, Auto-scaling, Computació en núvol, Aprenentatge automàtic, Assignació de recursos, Conference lecture

URL: http://hdl.handle.net/2117/340053
https://ieeexplore.ieee.org/document/9284206
https://ieeexplore.ieee.org/document/9284206