Zobrazeno 1 - 10
of 175
pro vyhledávání: '"Gradient boosting model"'
Publikováno v:
Вестник Дагестанского государственного технического университета: Технические науки, Vol 51, Iss 3, Pp 72-85 (2024)
Objective. The research aims to detect anomalies in data using machine learning models, in particular random forest and gradient boosting, to analyze network activity and detect cyberattacks. The research topic is relevant as cyber attacks are becomi
Externí odkaz:
https://doaj.org/article/d89dcfddd7194789b9a2270bb2d2d34f
Autor:
Shan Shan Li, Zhao Ming Liu, Jiao Li, Yi Bo Ma, Ze Yuan Dong, Jun Wei Hou, Fu Jie Shen, Wei Bu Wang, Qi Ming Li, Ji Guo Su
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-19 (2024)
Abstract Background Thermostability is a fundamental property of proteins to maintain their biological functions. Predicting protein stability changes upon mutation is important for our understanding protein structure–function relationship, and is
Externí odkaz:
https://doaj.org/article/df2058aadb2046688b578e324b035839
Publikováno v:
International Journal of Concrete Structures and Materials, Vol 18, Iss 1, Pp 1-26 (2024)
Abstract The contribution of shear resisted by flanges of T-beams is usually ignored in the shear design models even though it was proven by many experimental studies that the shear strength of T-beams is higher than that of equivalent rectangular cr
Externí odkaz:
https://doaj.org/article/4f1f71e6dbc34e96885e9cd342e6eed5
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-13 (2024)
Abstract Background & aim Cardiovascular disease (CVD) is the most important cause of death in the world and has a potential impact on health care costs, this study aimed to evaluate the performance of machine learning survival models and determine t
Externí odkaz:
https://doaj.org/article/1ef0b5a5a9ad47e8bda97c42459af14b
Publikováno v:
Zhongguo dizhi zaihai yu fangzhi xuebao, Vol 34, Iss 5, Pp 141-152 (2023)
Landslide susceptibility assessment forms the foundation for precise evaluation of landslide risk. To enhance the accuracy and robustness of landslide susceptibility mapping, a state-of-art machine learning algorithm named the extreme gradient boosti
Externí odkaz:
https://doaj.org/article/28ddf18154044962af3fd01f2eb3b419
Publikováno v:
Heliyon, Vol 9, Iss 12, Pp e22569- (2023)
This paper innovatively constructed an analytical and forecasting framework to predict PM2.5 concentration levels for 16 municipal districts in Shanghai. By means of XGBoost parameters adjustment, empirical mode decomposition, and model fusion, impro
Externí odkaz:
https://doaj.org/article/16d595d05dce4a9dbe28b536419a6acd
Machine learning application for prediction of sonic wave transit time - A case of Niger Delta basin
Autor:
Oluwaseun Daniel Akinyemi, Mohamed Elsaadany, Numair Ahmed Siddiqui, Sami Elkurdy, John Oluwadamilola Olutoki, Md Mahmodul Islam
Publikováno v:
Results in Engineering, Vol 20, Iss , Pp 101528- (2023)
Sonic wave transit times are the major logs for estimating pertinent geomechanical parameters including overburden stress, pore pressure, effective stress, and unconfined compressible strength among others. These logs contain much important informati
Externí odkaz:
https://doaj.org/article/605bf711bf4d40e98e79a3b8ef6cdcf2
Publikováno v:
Open Life Sciences, Vol 18, Iss 1, Pp 233-42 (2023)
Non-spatial structure of forest is an important aspect for harvesting regimes, silvicultural treatments, and ecosystem service provisions. In this pursuit, the present research envisaged the measurement of the crown and diameter structure of Pinus ma
Externí odkaz:
https://doaj.org/article/de2b293803ba4f0f814db789a318afea
Autor:
Jingyi Zhang, Kedong Yin
Publikováno v:
Frontiers in Environmental Science, Vol 11 (2023)
Corporate green innovation performance can serve as a critical tool for policymakers to identify the best practice and provide support to micro-entities in need. Accurate forecasting of corporate green innovation performance plays a vital role in inn
Externí odkaz:
https://doaj.org/article/ad2f90c6effb484992d3ba67c07e6bc6
Autor:
Mihail Kolev, Ludmil Drenchev
Publikováno v:
Data in Brief, Vol 50, Iss , Pp 109489- (2023)
This data article presents the experimental data of the wear behavior of four types of open-cell AlSi10Mg materials and open-cell AlSi10Mg-Al2O3 composites with different pore sizes under dry sliding conditions tested by pin-on-disk method. The data
Externí odkaz:
https://doaj.org/article/75bb5f508c484b75a2d01db28a5a6a9f