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1,587 results on '"Mosavi, Amir"'

215. Application of artificial intelligence techniques for automated detection of myocardial infarction: a review

Author

Hassannataj Joloudari, Javad, primary, Mojrian, Sanaz, additional, Nodehi, Issa, additional, Mashmool, Amir, additional, Kiani Zadegan, Zeynab, additional, Khanjani Shirkharkolaie, Sahar, additional, Alizadehsani, Roohallah, additional, Tamadon, Tahereh, additional, Khosravi, Samiyeh, additional, Akbari Kohnehshari, Mitra, additional, Hassannatajjeloudari, Edris, additional, Sharifrazi, Danial, additional, Mosavi, Amir, additional, Loh, Hui Wen, additional, Tan, Ru-San, additional, and Acharya, U Rajendra, additional

Published
2022
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227. A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis

Author

Band, Shahab S., primary, Ardabili, Sina, additional, Yarahmadi, Atefeh, additional, Pahlevanzadeh, Bahareh, additional, Kiani, Adiqa Kausar, additional, Beheshti, Amin, additional, Alinejad-Rokny, Hamid, additional, Dehzangi, Iman, additional, Chang, Arthur, additional, Mosavi, Amir, additional, and Moslehpour, Massoud, additional

Published
2022
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233. Modeling of carbon dioxide solubility in ionic liquids based on group method of data handling

Author

Moosanezhad-Kermani, Hamed, Rezaei, Farzaneh, Hemmati-Sarapardeh,Abdolhossein, Band, Shahab S., and Mosavi, Amir

Subjects
group method of data handling (gmgh), machine learning, solubility, carbon dioxide, TA1-2040, artificial intelligence, Engineering (General). Civil engineering (General), ionic liquid
Abstract

Due to industrial development, the volume of carbon dioxide (CO2) is rapidly increasing.. Several techniques have been used to eliminate CO2 from the output gas mixtures. One of these methods is CO2 capturing by ionic liquids (ILs). Computational models for estimating the CO2 solubility in ILS is of utmost importance. In this research, a white box model in the form of a mathematical correlation using the largest data bank in literature is presented by the group method of data handling (GMDH). This research investigates the application of GMDH intelligent method as a powerful computational approach for predicting CO2 solubility in different ionic liquids with temperature lower and upper than 324 K. In this regard, 4726 data points including the solubility of CO2 in 60 ILs were used for model development Moreover, seven different ionic liquids were selected to perform the external test. To evaluate the validity and efficiency of the suggested model, regression analysis was implemented on the actual and estimated target values. As a result, a proper fit between the experimental and predicted data was obtained and presented by various figures and statistical parameters. It is also worth noting that the predicted negative values in the proposed models are considered zero. Also, the results of the established correlation were compared to other proposed models exist in the literature of ionic liquids. The terminal form of the models suggested by GMDH approach and obtained based on temperature are two simple mathematical correlations by exerting input parameters of temperature (T), pressure (P), critical temperature (Tc ), critical pressure (Pc ) and, acentric factor (ω) which does not suffer from the black box property of other neural network algorithms. The model suggested in this work, would be a promising one which can act as an efficient predictor for CO2 solubility estimation in ILs and is capable of being used in different industries.

Published
2021

234. Feasibility of Random Forest and Multivariate Adaptive Regression Splines for Predicting Long-Term Mean Monthly Dew Point Temperature

Author

Zhang, Guodao, Bateni, Sayed M., Jun, Changhyun, Khoshkam, Helaleh, Band, Shahab S., and Mosavi, Amir

Subjects
General Environmental Science
Abstract

The accurate estimation of dew point temperature (Tdew) is important in climatological, agricultural, and agronomical studies. In this study, the feasibility of two soft computing methods, random forest (RF) and multivariate adaptive regression splines (MARS), is evaluated for predicting the long-term mean monthly Tdew. Various weather variables including air temperature, sunshine duration, relative humidity, and incoming solar radiation from 50 weather stations in Iran as well as their geographical information (or a subset of them) are used in RF and MARS as inputs. Three statistical indicators namely, root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) are used to assess the accuracy of Tdew estimates from both models for different input configurations. The results demonstrate the capability of the RF and MARS methods for predicting the long-term mean monthly Tdew. The combined scenarios in both the RF and MARS methods are found to produce the best Tdew estimates. The best Tdew estimates were obtained by the MARS model with the RMSE, MAE, and R of respectively 0.17°C, 0.14°C, and 1.000 in the training phase; 0.15°C, 0.12°C, and 1.000 in the validation phase; and 0.18°C, 0.14°C, and 0.999 in the testing phase.

Published
2022

235. Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation

Author

Ehteram, Mohammad, Panahi, Fatemeh, Ahmed, Ali Najah, Mosavi, Amir H., El-Shafie, Ahmed, Ehteram, Mohammad, Panahi, Fatemeh, Ahmed, Ali Najah, Mosavi, Amir H., and El-Shafie, Ahmed

Abstract

Predicting evaporation is essential for managing water resources in basins. Improvement of the prediction accuracy is essential to identify adequate inputs on evaporation. In this study, artificial neural network (ANN) is coupled with several evolutionary algorithms, i.e., capuchin search algorithm (CSA), firefly algorithm (FFA), sine cosine algorithm (SCA), and genetic algorithm (GA) for robust training to predict daily evaporation of seven synoptic stations with different climates. The inclusive multiple model (IMM) is then used to predict evaporation based on established hybrid ANN models. The adjusting model parameters of the current study is a major challenge. Also, another challenge is the selection of the best inputs to the models. The IMM model had significantly improved the root mean square error (RMSE) and Nash Sutcliffe efficiency (NSE) values of all the proposed models. The results for all stations indicated that the IMM model and ANN-CSA could outperform other models. The RMSE of the IMM was 18, 21, 22, 30, and 43% lower than those of the ANNCSA, ANN-SCA, ANN-FFA, ANN-GA, and ANN models in the Sharekord station. The MAE of the IMM was 0.112 mm/day, while it was 0.189 mm/day, 0.267 mm/day, 0.267 mm/day, 0.389 mm/day, 0.456 mm/day, and 0.512 mm/day for the ANN-CSA, ANN-SCA, and ANN-FFA, ANN-GA, and ANN models, respectively, in the Tehran station. The current study proved that the inclusive multiple models based on improved ANN models considering the fuzzy reasoning had the high ability to predict evaporation.

Published
2022

236. Systematic Review of Deep Learning and Machine Learning for Building Energy

Author

Ardabili, Sina, Abdolalizadeh, Leila, Mako, Csaba, Torok, Bernat, Mosavi, Amir, Ardabili, Sina, Abdolalizadeh, Leila, Mako, Csaba, Torok, Bernat, and Mosavi, Amir

Abstract

The building energy (BE) management plays an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumption and energy demand datasets for a smarter energy management. The machine learning (ML) and deep learning (DL) methods and applications, in particular, have been promising for the advancement of accurate and high-performance energy models. The present study provides a comprehensive review of ML- and DL-based techniques applied for handling BE systems, and it further evaluates the performance of these techniques. Through a systematic review and a comprehensive taxonomy, the advances of ML and DL-based techniques are carefully investigated, and the promising models are introduced. According to the results obtained for energy demand forecasting, the hybrid and ensemble methods are located in the high-robustness range, SVM-based methods are located in good robustness limitation, ANN-based methods are located in medium-robustness limitation, and linear regression models are located in low-robustness limitations. On the other hand, for energy consumption forecasting, DL-based, hybrid, and ensemble-based models provided the highest robustness score. ANN, SVM, and single ML models provided good and medium robustness, and LR-based models provided a lower robustness score. In addition, for energy load forecasting, LR-based models provided the lower robustness score. The hybrid and ensemble-based models provided a higher robustness score. The DL-based and SVM-based techniques provided a good robustness score, and ANNbased techniques provided a medium robustness score.

Published
2022

237. Optimized Type-2 Fuzzy Frequency Control for Multi-Area Power Systems

Author

Shakibjoo, Ali Dokht, Moradzadeh, Mohammad, Din, Sami Ud, Mohammadzadeh, Ardashir, Mosavi, Amir A.H., Vandevelde, Lieven, Shakibjoo, Ali Dokht, Moradzadeh, Mohammad, Din, Sami Ud, Mohammadzadeh, Ardashir, Mosavi, Amir A.H., and Vandevelde, Lieven

Abstract

The objective of this study is minimizing the frequency deviation due to the load variations and fluctuations of renewable energy resources. In this paper, a new type-2 fuzzy control (T2FLC) approach is presented for load frequency control (LFC) in power systems with multi-areas, demand response (DR), battery energy storage system (BESS), and wind farms. BESS is used to reduce the frequency deviations caused by wind energy, and DR is utilized to increase network stability due to fast load changes. The suggested T2FLC is online tuned based on the extended Kalman filter to improve the LFC accuracy in coordination of DR, BESS, and wind farms. The system dynamics are unknown, and the system Jacobian is extracted by online modeling with a simple multilayer perceptron neural network (MLP-NN). The designed LFC is evaluated through simulating on 10-machine New England 39-bus test system (NETS-39b) in four scenarios. Simulation results verifies the desired performance, indicating its superiority compared to a classical PI controllers, and type-1 fuzzy logic controllers (FLCs). The mean of improvement percentage is about 20%., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2022

245. A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis

Author

Band, Shahab, primary, Ardabili, Sina, additional, Yarahmadi, Atefeh, additional, Pahlevanzadeh, Bahareh, additional, Kausar Kiani, Adiqa, additional, Beheshti, Amin, additional, Alinejad Rokny, Hamid, additional, Dehzangi, Iman, additional, Chang, Arthur, additional, Mosavi, Amir, additional, and Moslehpour, Massoud, additional

Published
2022
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248. Energetic thermo-physical analysis of MLP-RBF feed-forward neural network compared with RLS Fuzzy to predict CuO/liquid paraffin mixture properties

Author

Zhang, Xiaoluan, primary, Liu, Xinni, additional, Wang, Xifeng, additional, Band, Shahab S., additional, Bagherzadeh, Seyed Amin, additional, Taherifar, Somaye, additional, Abdollahi, Ali, additional, Bahrami, Mehrdad, additional, Karimipour, Arash, additional, Chau, Kwok-Wing, additional, and Mosavi, Amir, additional

Published
2022
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