Zobrazeno 1 - 10
of 4 042
pro vyhledávání: '"Shahab S."'
Autor:
Shahab S. Band, Sultan Noman Qasem, Rasoul Ameri, Hao-Ting Pai, Brij B. Gupta, Saeid Mehdizadeh, Amir Mosavi
Publikováno v:
Alexandria Engineering Journal, Vol 105, Iss , Pp 613-625 (2024)
Solar energy is one of the renewable and clean energy sources. Accurate solar radiation (SR) estimates are therefore needed in solar energy applications. Firstly, two deep learning models, including gated recurrent unit (GRU) and long short-term memo
Externí odkaz:
https://doaj.org/article/2ea6dbc20cfe4d13942d4dac4840641f
Publikováno v:
Results in Engineering, Vol 24, Iss , Pp 103319- (2024)
Accurate runoff forecasting is crucial for effective water resource management, yet existing models often face challenges due to the complexity of hydrological systems. This study addresses these challenges by introducing a novel bio-inspired metaheu
Externí odkaz:
https://doaj.org/article/fc6460149fa94209a82c118c64e76fbd
Autor:
Shuguang Li, Sultan Noman Qasem, Hojat Karami, Ely Salwana, Alireza Rezaei, Danyal Shahmirzadi, Shahab S. Band
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 18, Iss 1 (2024)
Vortex flow characteristics in a reservoir and horizontal water intake have been predicted by using regression models in this numerical research. In this paper, three standalone machine learning models – Random Forest (RF), K-nearest neighbours (KN
Externí odkaz:
https://doaj.org/article/7ced0a45cccb4d4ead602bf39eb52ccd
Autor:
Shuguang Li, Sultan Noman Qasem, Shahab S. Band, Rasoul Ameri, Hao-Ting Pai, Saeid Mehdizadeh
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 18, Iss 1 (2024)
ABSTRACTMonitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of t
Externí odkaz:
https://doaj.org/article/6d30f29819774b80b04b1067af99c549
Autor:
Anichur Rahman, Tanoy Debnath, Dipanjali Kundu, Md. Saikat Islam Khan, Airin Afroj Aishi, Sadia Sazzad, Mohammad Sayduzzaman, Shahab S. Band
Publikováno v:
AIMS Public Health, Vol 11, Iss 1, Pp 58-109 (2024)
In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further,
Externí odkaz:
https://doaj.org/article/cd06c89039e74f6f9a00d26e64c1befb
Publikováno v:
ACS Omega, Vol 8, Iss 31, Pp 28036-28051 (2023)
Externí odkaz:
https://doaj.org/article/fa589cd3931b4e67990d4282d3d5cafa
Autor:
Shamim Yousefi, Samad Najjar-Ghabel, Ramin Danehchin, Shahab S. Band, Chung-Chian Hsu, Amir Mosavi
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 2, Pp 101944- (2024)
Machine learning contributes in improving the accuracy of melanoma detection. There are extensive studies in classic and deep learning-based approaches for melanoma detection in the literature. Still, they are not accurate or require high learning da
Externí odkaz:
https://doaj.org/article/eb4e4fcb06eb4132ab912b398524164d
Autor:
Tao Hai, Biju Theruvil Sayed, Ali Majdi, Jincheng Zhou, Rafid Sagban, Shahab S. Band, Amir Mosavi
Publikováno v:
Geocarto International, Vol 0, Iss 0 (2023)
A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integr
Externí odkaz:
https://doaj.org/article/b4af9052260a4be0bca117dcc5f1f424
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 17, Iss 1 (2023)
Most bridge failures result from scouring around bridge piers, resulting in economic losses and risks to public safety. The conventional equations for predicting the depth of scour at bridge piers have several limitations: (1) They mainly use regress
Externí odkaz:
https://doaj.org/article/9d6961a3bcca454da493e930933b3591
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 17, Iss 1 (2023)
This paper presents a novel approach for enhancing long-term runoff simulations through the integration of empirical mode decomposition (EMD) with four machine learning (ML) models: ensemble, support vector machine (SVM), convolutional neural network
Externí odkaz:
https://doaj.org/article/b9e283c0b7814b00a8faefd2d0da5585