Suchergebnisse - "Machine learning algorithm"

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

Autoren: Orbay, Raik Wikner, Evelina

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

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

Dateibeschreibung: electronic

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

Machine learning algorithm for GPCR-A17 ligand action prediction: A Novel Approach to Drug Discovery

MACHINE LEARNING ALGORITHM FOR GPCR-A17 LIGAND ACTION PREDICTION: A Novel Approach to Drug Discovery

Autoren: Silva, Rodolfo Miguel Tavares da

Schlagwörter: GPCRs, Artificial Intelligence, Machine Learning, Deep Learning, Drug Discovery, Inteligência Artificial, Descoberta de Medicamentos

Verfügbarkeit: https://hdl.handle.net/10316/117960

Study on the Color Characteristics of Reproduced Oil Paintings Using a Machine Learning Algorithm

Autoren: Hyeong Rok Song Young Hoon Jo

Quelle: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. :1395-1400

Machine Learning Algorithm to Extract Properties of ATE Phantoms from Microwave Measurements

Autoren: Mattsson, Viktor Perez, Mauricio D. Joseph, Laya et al.

Quelle: International journal of microwave and wireless technologies. 16(10):1624-1631

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-521534
https://doi.org/10.1017/S1759078724000102

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

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

Quelle: Transportation. 52:1745-1765

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

Dateibeschreibung: electronic

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

Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions

Autoren: Abdel-Hameed, Amal Mohamed Abuarab, Mohamed Al-Ansari, Nadhir et al.

Quelle: Potato Research. 67(4):1755-1774

Schlagwörter: Artifcial neural network, Blue water footprint, Random forest, Support vector regression, Water management, Soil Mechanics, Geoteknik

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-104949
https://doi.org/10.1007/s11540-024-09716-1

Review on PAPR Reduction and Improvement of OFDM System Performance Using Artificial Intelligence Machine Learning Algorithm

Autoren: Rahul Mishra

Quelle: International Journal of Scientific Research and Engineering Trends. 11:106-110

A proposal for a machine-learning algorithm for the prediction of seizure recurrence risk at 2 years after discontinuation of anti-seizure medications

Autoren: Contento, Margherita Bertaccini, Bruno Biggi, Martina et al.

Quelle: Seizure: European Journal of Epilepsy. 133:73-79

Schlagwörter: anti-epileptic drugs, ASM withdrawal, epilepsy, AED discontinuation, prediction models

Dateibeschreibung: application/pdf

Differentiating Type 1 and Type 2 myocardial infarction using a machine learning algorithm and biomarkers

Autoren: Anna Snavely Laurel Jackson Christian John Hunter et al.

Quelle: The American Journal of Emergency Medicine. 98:50-56

Anthropogenic sinkholes’ susceptibility assessment in Palermo, Italy, using a machine learning algorithm

Autoren: Bausilio, Giuseppe Allocca, Vincenzo Cappadonia, Chiara et al.

Quelle: Rendiconti Online della Società Geologica Italiana. 67:1-6

Machine Learning Algorithm-Based Tumor-Stroma Ratio Can Stratify the Prognosis of Intrahepatic Cholangiocarcinoma

Autoren: Xin Zhang Chen-Song Huang Xi-Tai Huang et al.

Quelle: Journal of Surgical Research. 315:184-193

Machine learning algorithm for strawberry fruit recognition and classification of their maturity level

Autoren: A. I. Kutyrev R. A. Filippov

Quelle: Siberian Herald of Agricultural Science. 55:106-117

A Comparative Analysis of Dataset Performance in Disease Prediction via Machine Learning Algorithm

Autoren: Mustafa COSAR

Quelle: The Eurasia Proceedings of Science Technology Engineering and Mathematics. 35:29-37

Comparison of machine learning algorithm performances in digital terrain model generation

Autoren: Abdullah Can Ozen Ozgul Vupa Cilengiroglu

Quelle: Journal of Geodesy and Geoinformation. 12:179-193

Melanoma Skin Cancer Prediction Using Machine Learning Algorithm

Autoren: null Harish Nair null Nandana Nair null Esther Renji et al.

Quelle: International Research Journal on Advanced Engineering and Management (IRJAEM). 2:3703-3709

Prediction of students’ feedback on faculty performance using stacking ensemble method: Machine learning algorithm

Autoren: Mayowa Samuel Alade Samuel Olujimi Adejumo Olufemi Deborah Ninan et al.

Quelle: Edelweiss Applied Science and Technology. 9:1149-1180

Calculating cost contingency for residential construction projects in Egypt using a machine learning algorithm

Autoren: Seif Mohamed Mohamedein Ayman Hamdy Nassar

Quelle: International Journal of Construction Management. :1-16

Sign Language Recognition Based Communication System Using Machine Learning Algorithm for Vocally Impaired People

Autoren: Mohammed Abdul Kader Md. Jahid Hasan Md. Ariful Islam Emon et al.

Quelle: European Journal of Artificial Intelligence and Machine Learning. 4:1-9

Machine Learning Algorithm for Modelling and Analysis of Faults in Secondary Power Distribution Networks

Autoren: Udofia, Daniel Ezekiel Akaninyene Bernard Obot Umoren Mfonobong Anthony

Quelle: Asian Journal of Research and Reviews in Physics. 9:12-33

Factors associated with the presence of anxiety and depression symptoms in rural hypertensive adults in Bangladesh: leveraging extreme gradient booster machine learning algorithm

Autoren: Raihana, Zarin Kader, Manzur Islam, Md Zahidul et al.

Quelle: Frontiers in Psychology. 16

Schlagwörter: anxiety, depression, hypertension, logistic regression, machine learning

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:du-51403
https://doi.org/10.3389/fpsyg.2025.1650667