Zobrazeno 1 - 10
of 519
pro vyhledávání: '"Shahaboddin Shamshirband"'
Autor:
Saeed Samadianfard, Sajjad Hashemi, Katayoun Kargar, Mojtaba Izadyar, Ali Mostafaeipour, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband
Publikováno v:
Energy Reports, Vol 6, Iss , Pp 1147-1159 (2020)
Wind power as a renewable source of energy has numerous economic, environmental, and social benefits. To enhance and control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. In the current study, for pre
Externí odkaz:
https://doaj.org/article/721f11b629bb4820a171614924eab0ce
Autor:
Abdolhossein Hemmati-Sarapardeh, Sassan Hajirezaie, Mohamad Reza Soltanian, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband, Kwok-Wing Chau
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 14, Iss 1, Pp 27-37 (2020)
The natural gas compressibility factor indicates the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial method based on the group method of data handling (GMDH) is pres
Externí odkaz:
https://doaj.org/article/b42e3f3b8f2b4516825e82fb4596091a
Autor:
Narjes Nabipour, Amir Mosavi, Eva Hajnal, Laszlo Nadai, Shahaboddin Shamshirband, Kwok-Wing Chau
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 14, Iss 1, Pp 491-506 (2020)
Climate change impacts and adaptations are ongoing issues that are attracting the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information
Externí odkaz:
https://doaj.org/article/05a700de7dee4166a756a2acb794e099
Autor:
Katayoun Kargar, Saeed Samadianfard, Javad Parsa, Narjes Nabipour, Shahaboddin Shamshirband, Amir Mosavi, Kwok-wing Chau
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 14, Iss 1, Pp 311-322 (2020)
The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various st
Externí odkaz:
https://doaj.org/article/2b6031460e8d408ba0f7cbbcff8fe692
Autor:
Shahaboddin Shamshirband, Meisam Babanezhad, Amir Mosavi, Narjes Nabipour, Eva Hajnal, Laszlo Nadai, Kwok-Wing Chau
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 14, Iss 1, Pp 367-378 (2020)
A novel combination of the ant colony optimization algorithm (ACO)and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors. The proposed intelligent model presents a probabilistic computational strategy fo
Externí odkaz:
https://doaj.org/article/6d090cab65da4b6db15369fd2a295e26
Autor:
Shahaboddin Shamshirband, Fatemeh Esmaeilbeiki, Davoud Zarehaghi, Mohammadreza Neyshabouri, Saeed Samadianfard, Mohammad Ali Ghorbani, Amir Mosavi, Narjes Nabipour, Kwok-Wing Chau
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 14, Iss 1, Pp 939-953 (2020)
This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current
Externí odkaz:
https://doaj.org/article/81a7e19c72ae4d86b26ba725f0066234
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 14, Iss 1, Pp 805-817 (2020)
Estimation of wave height is essential for several coastal engineering applications. This study advances a nested grid numerical model and compare its efficiency with three machine learning (ML) methods of artificial neural networks (ANN), extreme le
Externí odkaz:
https://doaj.org/article/c6e49d23173944ffa39be952b24e864a
Autor:
Mohammad Hossein Ahmadi, Alireza Baghban, Milad Sadeghzadeh, Mohammad Zamen, Amir Mosavi, Shahaboddin Shamshirband, Ravinder Kumar, Mohammad Mohammadi-Khanaposhtani
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 14, Iss 1, Pp 545-565 (2020)
In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (P
Externí odkaz:
https://doaj.org/article/fe2e24c8a851491793f45480865938db
Autor:
Hossien Riahi-Madvar, Majid Dehghani, Kulwinder Singh Parmar, Narjes Nabipour, Shahaboddin Shamshirband
Publikováno v:
IEEE Access, Vol 8, Pp 60314-60337 (2020)
In this paper, a new hybrid model is proposed using Subset Selection by Maximum Dissimilarity (SSMD) and adaptive neuro-fuzzy inference system (ANFIS) hybridized with the firefly algorithm (FFA) to predict the longitudinal dispersion coefficient (Kx)
Externí odkaz:
https://doaj.org/article/95a165c2d15a475a82150ee470af76a8
Autor:
Fizza Shahid, Humaira Ashraf, Anwar Ghani, Shahbaz Ahmed Khan Ghayyur, Shahaboddin Shamshirband, Ely Salwana
Publikováno v:
IEEE Access, Vol 8, Pp 118285-118298 (2020)
Cloud Computing facilitates business by storing an enormous amount of data in the cloud transmitted over the Internet with seamless access to the data and no hardware compatibility limitations. However, data during transmission is vulnerable to man i
Externí odkaz:
https://doaj.org/article/908cd06474fb4680bffd0214cc72ef9a