@article{FaizollahzadehArdabiliNajafiAlizamiretal.,
  author    = {Faizollahzadeh Ardabili, Sina and Najafi, Bahman and Alizamir, Meysam and Mosavi, Amir and Shamshirband, Shahaboddin and Rabczuk, Timon},
  title     = {Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters},
  series = {Energies},
  journal   = {Energies},
  number    = {11, 2889},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en11112889},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181025-38170},
  pages     = {1 -- 20},
  abstract  = {The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86\% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. \%, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46\% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. \%, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6\% for ethyl ester and 3.1\% for methyl ester, compared with those for the experimental data.},
  subject      = {Biodiesel},
  language  = {en}
}
@article{LashkarAraKalantariSheikhKhozanietal.,
  author    = {Lashkar-Ara, Babak and Kalantari, Niloofar and Sheikh Khozani, Zohreh and Mosavi, Amir},
  title     = {Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel},
  series = {Mathematics},
  volume    = {2021},
  journal   = {Mathematics},
  number    = {Volume 9, Issue 6, Article 596},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/math9060596},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210504-44197},
  pages     = {15},
  abstract  = {One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{HarirchianLahmerBuddhirajuetal.,
  author    = {Harirchian, Ehsan and Lahmer, Tom and Buddhiraju, Sreekanth and Mohammad, Kifaytullah and Mosavi, Amir},
  title     = {Earthquake Safety Assessment of Buildings through Rapid Visual Screening},
  series = {Buildings},
  volume    = {2020},
  journal   = {Buildings},
  number    = {Volume 10, Issue 3},
  publisher = {MDPI},
  doi       = {10.3390/buildings10030051},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200331-41153},
  pages     = {15},
  abstract  = {Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bing{\"o}l region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@unpublished{RezakazemiMosaviShirazian,
  author    = {Rezakazemi, Mashallah and Mosavi, Amir and Shirazian, Saeed},
  title     = {ANFIS pattern for molecular membranes separation optimization},
  volume    = {2018},
  doi       = {10.25643/BAUHAUS-UNIVERSITAET.3821},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181122-38212},
  pages     = {1 -- 20},
  abstract  = {In this work, molecular separation of aqueous-organic was simulated by using combined soft computing-mechanistic approaches. The considered separation system was a microporous membrane contactor for separation of benzoic acid from water by contacting with an organic phase containing extractor molecules. Indeed, extractive separation is carried out using membrane technology where complex of solute-organic is formed at the interface. The main focus was to develop a simulation methodology for prediction of concentration distribution of solute (benzoic acid) in the feed side of the membrane system, as the removal efficiency of the system is determined by concentration distribution of the solute in the feed channel. The pattern of Adaptive Neuro-Fuzzy Inference System (ANFIS) was optimized by finding the optimum membership function, learning percentage, and a number of rules. The ANFIS was trained using the extracted data from the CFD simulation of the membrane system. The comparisons between the predicted concentration distribution by ANFIS and CFD data revealed that the optimized ANFIS pattern can be used as a predictive tool for simulation of the process. The R2 of higher than 0.99 was obtained for the optimized ANFIS model. The main privilege of the developed methodology is its very low computational time for simulation of the system and can be used as a rigorous simulation tool for understanding and design of membrane-based systems. Highlights are, Molecular separation using microporous membranes. Developing hybrid model based on ANFIS-CFD for the separation process, Optimization of ANFIS structure for prediction of separation process},
  subject      = {Fluid},
  language  = {en}
}