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Institution

Islamic Azad University

EducationTehran, Iran
About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Adsorption. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.


Papers
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Journal ArticleDOI
TL;DR: Results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.
Abstract: Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method. The comparison of estimation accuracies of various models illustrated that the WANN was the most accurate model in SSL estimation in comparison to other models. For example, in Flathead River station, the determination coefficient was 0.91 for the best WANN model, while it was 0.65, 0.75, and 0.481 for the best ANN, ANFIS, and SRC models, and also in the Santa Clara River, amounts of this statistical criteria was 0.92 for the best WANN model, while it was 0.76, 0.78, and 0.39 for the best ANN, ANFIS, and SRC models, respectively. Also, the values of cumulative suspended sediment load computed by the best WANN model were closer to the observed data than the other models. In general, results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.

184 citations

Journal ArticleDOI
TL;DR: Results of the case study show the proper siting and sizing of REGs are important to improve the voltage profile, reduce costs, emission and losses of distribution system and the main feature of the algorithm refers to its accuracy and calculation speed.

184 citations

Journal ArticleDOI
TL;DR: In this paper, a higher order generalized uncertainty principle (GUP) in the form [ X, P ] = i ℏ / ( 1 − β P 2 ) is presented, which predicts both a minimal length uncertainty and a maximal observable momentum.

184 citations

Journal ArticleDOI
TL;DR: In this paper, Cufe12O19 nanoparticles were successfully synthesized using amino acids as non-toxic reagents to achieve the desired sample with high homogeneity and the fine size, various parameters such as calcination temperature, molar ratios of Cu+2 to Fe+3 and Cu +2 to amino acid were changed.

184 citations

Journal ArticleDOI
TL;DR: This paper presents an in-depth analysis of the Particle Swarm Optimization-based task and workflow scheduling schemes proposed for the cloud environment in the literature and provides a classification of the proposed scheduling schemes based on the type of the PSO algorithms which have been applied and illuminates their objectives, properties and limitations.
Abstract: Cloud computing provides effective mechanisms for distributing the computing tasks to the virtual resources. To provide cost-effective executions and achieve objectives such as load balancing, availability and reliability in the cloud environment, appropriate task and workflow scheduling solutions are needed. Various metaheuristic algorithms are applied to deal with the problem of scheduling, which is an NP-hard problem. This paper presents an in-depth analysis of the Particle Swarm Optimization (PSO)-based task and workflow scheduling schemes proposed for the cloud environment in the literature. Moreover, it provides a classification of the proposed scheduling schemes based on the type of the PSO algorithms which have been applied in these schemes and illuminates their objectives, properties and limitations. Finally, the critical future research directions are outlined.

184 citations


Authors

Showing all 83704 results

NameH-indexPapersCitations
Ajit Kumar Mohanty141112493062
Pierluigi Paolucci1381965105050
Eric Conte132120684593
Patrizia Azzi132127583686
D. Del Re131140687230
Jean-Laurent Agram128122184423
Seyed Mohsen Etesami128110176488
Jean-Charles Fontaine128119084011
Roberta Arcidiacono128132280917
Tejinder Virdee128120874372
Frank Hartmann127111681455
Paolo Azzurri126105881651
Achim Stahl1241248111121
Federica Primavera12087663895
Riccardo Andrea Manzoni12094667897
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202335
2022372
202111,539
202012,092
201911,011
201810,260