Yazar "Ghorbani, Shahryar" seçeneğine göre listele

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    Applying fuzzy delphi and best-worst method for identifying and prioritizing key factors affecting on university-industry collaboration
    (Growing Science, 2020) Mosayebi, Alireza; Ghorbani, Shahryar; Masoomi, Behzad
    The collaboration between the universities and industries is currently in the focus of attention globally. Governments, universities, and industries are interested in good and effective collaboration, which would be beneficial for all parties. To foster University-Industry Collaboration, and to help transfer the knowledge and technology between these two parties, academics, politicians and companies are paying attention to science and technology policies more than ever. In this study, the factors affecting the improvement of University-Industry Collaboration are identified and prioritized. In the first step, 20 factors are identified and 12 factors are selected using the Fuzzy Delphi method. Then, using the BWM method, prioritizing the extracted factors is determined for industry sponsorship of the university research. Finally, based on the results, the discussion is conducted and six major strategies are presented to improve this relationship. (C) 2020 by the authors; licensee Growing Science, Canada.
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    Artificial intelligence-based fast and efficient hybrid approach for spatial modelling of soil electrical conductivity
    (Elsevier, 2019) Ghorbani, Mohammad Ali; Deo, Ravinesh C.; Kashani, Mahsa H.; Shahabi, Mahmoud; Ghorbani, Shahryar
    Expert systems adopted to support modem and rapidly advancing proximal soil sensing technologies can help generate accurately modelled electrical conductivity, as a decision-support parameter defining soil health in terms of the salinity, clay and bulk density compositions, among the other primal soil properties. Electrical conductivity (EC) is expected to correlate with the pertinent factors that regulate the crop yield (ie., soil texture, cation exchange, drainage conditions, organic matter level, salinity, and the subsoil characteristics), which in turn, can also acts to control the overall health of the crop. Utilizing a fast and efficient artificial intelligence approach, this study designs a hybrid predictive model integrating multilayer perception with the Firefly Algorithm (MLP-FFA), and then evaluates its performance in respect to the standalone MLP and the ordinary kriging (OK) model applied in agricultural locality of the Soofiyan, plains in Tabriz, northwest of Iran. To develop a spatial modelling framework, 126 distinct measurements of EC were obtained through a grid sampled at 1000 m x 1000 m spacing, partitioned in training (88) and testing (38) sets. Applying a ratio of nugget to sill to determine the spatial dependence, a spatial modelling strategy was utilized where the spherical, exponential, Gaussian and linear semi-variograms depicting the autocorrelation of the sampled points (latitude x longitude) over the study area were analyzed in ArcGIS to deduce the optimal data characteristics and the respective statistical model. Spherical semi-variogram was the optimal representor for EC, relative to its latitude and longitude, in agreement with the lowest Residual Sums of Squares (RSS) and the highest coefficient of determination (R-2). Hybrid MLP-FFA and standalone MLP models were thus developed with latitude, longitude and measured electrical conductivity in the training set and evaluated in respect to the OK method by means of statistical scores: root mean square error (RMSE), mean absolute error (MAE) including the normalized metrics represented by Willmott's Index (WI), Nash Sutcliffe's coefficient (ENs) and Legates & McCabe's Index (EJ. Verified by diagnostic plots and grid-averaged metrics, the results revealed that the hybrid MLP-FFA model performed significantly better than both comparative models, with WI = 0.780 relative to 0.637 (MLP) and 0.714 (OK) models and ENs = 0.725 (vs. 0.511 & 0.589) and E-Lm = 0.552 (vs. 0.402 & 0.413), respectively. Expanded uncertainty, t-statistic and global performance indicators combining the le, RMSE and mean bias error and a Taylor plot also confirmed the efficacy of the hybrid MLP-FFA over the standalone MLP and OK model. There was no significant difference between the results of standalone MLP and OK when evaluated through spatial trend maps, while the hybrid MLP-FFA model exhibited a better ability to establishing nonlinear relation. ships with electrical conductivity data, resulting in a better representation of the spatially modelled EC. Our results ascertain the prodigious performance of a hybrid MLP-FFA over a standalone MLP and ordinary kriging approaches, attributable to a better utility of the Firefly algorithm in improved spatial estimation of electrical conductivity. This study concludes that the hybrid MLP-FFA model should be explored to solve practical problems in soil physics, particularly in designing decision-support systems to attain agricultural precision.
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    Design and implementation of a hybrid MLP-GSA model with multi-layer perceptron-gravitational search algorithm for monthly lake water level forecasting
    (Springer, 2019) Ghorbani, Mohammad Ali; Deo, Ravinesh C.; Karimi, Vahid; Kashani, Mahsa H.; Ghorbani, Shahryar
    Lakes are primitive water holding geographicstructures containing most thefresh water on the Earth's surface, but the recent trends show that climate change can potentiallylead to a significant aberration in the Lake water level and itsoverall pristine state, and therefore, could alsothreaten the source of freshwater. The ability to forecast thelake water is a paramount decision-making and risk-reduction task, and this isrequired to retain the sustainability of the natural environment, and to reduce the risk tothe local and globalfood chain, recreationactivities, agriculture and ecosystems. In this study, we have designed and evaluated a new hybrid forecasting model, integrating the gravitational search algorithm (GSA), as a heuristic optimization tool, with the Multilayer Perceptron (MLP-GSA) algorithm to forecast water level in Winnipesaukee and Cypress Lakes in the United States of America. The performance of the resulting hybrid MLP-GSA model is benchmarked and compared with the traditional MLP trained with Levenberg-Marquadt back propagation learning algorithm, two other intelligent hybrid models (MLP-PSO and MLP-FFA) and also two stochastic models namely, ARMA and ARIMA models. In this case study, the monthly time scale water level data from 1938 to 2005 and 1942 to 2011 for the Lakes Winnipesaukee and Cypress, respectively, were applied to train and evaluate the MLP-GSA model. The best input combinations of the standalone (MLP) and the hybrid MLP-GSA forecasting models were determined by sensitivity analysis of historical water level training data for 1-month lead forecasting. The hybrid MLP-GSA model was evaluated independently with statistical score metrics: coefficient of correlation, coefficient of efficiency, the root mean square and relative root mean square errors, and the Bayesian Information Criterion. The results showed that the hybrid MLP-GSA4 and MLP-GSA5 model (where the 4 and 5months' of lagged input combinations of Lake water level data were utilized as the model inputs) performed more accurately than the ARIMA, ARMA, MLP4, MLP-PSO4 and MLP-FFA4 models for the Cypress Lake and ARIMA, ARMA, MLP5, MLP-PSO5 and MLP-FFA5 models for the Winnipesaukee lake, respectively. This study ascertained the robustness of hybrid MLP-GSA over ARMA, ARIMA, MLP, MLP-PSO and MLP-FFA for the forecasting of Lake water level. The high efficacy of the hybrid MLP-GSA model over theother applied models, indicate significant implications of its use in water resources management, decision-makingtasks, irrigation management,management ofhydrologic structures and sustainable use of water for agriculture andother necessities.