Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Mohammad Abdullah Almubaidin"'
Autor:
Sarmad Dashti Latif, Mohammad Abdullah Almubaidin, Chua Guang Shen, Michelle Sapitang, Ahmed H. Birima, Ali Najah Ahmed, Mohsen Sherif, Ahmed El-Shafie
Publikováno v:
Ain Shams Engineering Journal, Vol 15, Iss 9, Pp 102916- (2024)
The objective of the current study is to investigate the effectiveness of specifically the Support Vector Machine (SVM) and the k-Nearest Neighbors (kNN) models for sea level prediction. The SVM and kNN models are compared using the predicted data de
Externí odkaz:
https://doaj.org/article/5a8686320a214774b2e9aa0d798379b8
Autor:
Mohammad Abdullah Almubaidin, Nur Shazwani binti Ismail, Sarmad Dashti Latif, Ali Najah Ahmed, Hayana Dullah, Ahmed El-Shafie, Christian Sonne
Publikováno v:
Results in Engineering, Vol 22, Iss , Pp 102114- (2024)
The increasing carbon emissions in Malaysia necessitate accurate methods to track and control pollution levels. This study focuses on predicting carbon monoxide (CO) concentrations in Petaling Jaya using various machine learning models, and two impor
Externí odkaz:
https://doaj.org/article/f061a3ba3e544b1e9e8833af2b34a287
Publikováno v:
Tikrit Journal of Engineering Sciences, Vol 30, Iss 4 (2023)
The evolving character of the environment makes it challenging to predict water levels in advance. Despite being the most common approach for defining hydrologic processes and implementing physical system changes, the physics-based model has some pra
Externí odkaz:
https://doaj.org/article/da84d49fbcb749e99a3a3b2a90175768
Autor:
Mohammad Abdullah Almubaidin, Ali Najah Ahmed, Lariyah Mohd Sidek, Khlaif Abdul Hakim AL-Assifeh, Ahmed El-Shafie
Recently, there has been increased interest in using optimization techniques to find the optimal operation for reservoirs by applying them to various aspects of the reservoir operating system, such as finding the optimal rule curves for reservoirs. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9306963a7e4361537fe31c6b1b94ed15
https://doi.org/10.21203/rs.3.rs-2358323/v1
https://doi.org/10.21203/rs.3.rs-2358323/v1