@article{NabipourMosaviBaghbanetal.,
  author    = {Nabipour, Narjes and Mosavi, Amir and Baghban, Alireza and Shamshirband, Shahaboddin and Felde, Imre},
  title     = {Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions},
  series = {Processes},
  volume    = {2020},
  journal   = {Processes},
  number    = {Volume 8, Issue 1, 92},
  publisher = {MDPI},
  doi       = {10.3390/pr8010092},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200113-40624},
  pages     = {12},
  abstract  = {Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{OuaerHosseiniAmaretal.,
  author    = {Ouaer, Hocine and Hosseini, Amir Hossein and Amar, Menad Nait and Ben Seghier, Mohamed El Amine and Ghriga, Mohammed Abdelfetah and Nabipour, Narjes and Andersen, P{\aa}l {\O}steb{\o} and Mosavi, Amir and Shamshirband, Shahaboddin},
  title     = {Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, Issue 1, 304},
  publisher = {MDPI},
  doi       = {https://doi.org/10.3390/app10010304},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200107-40558},
  pages     = {18},
  abstract  = {Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80\% of the points for training and 20\% for validation). Two backpropagation-based methods, namely Levenberg-Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng-Robinson (PR) or Soave-Redlich-Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R2 = 0.9896 and RMSE = 0.0201.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{SaadatfarKhosraviHassannatajJoloudarietal.,
  author    = {Saadatfar, Hamid and Khosravi, Samiyeh and Hassannataj Joloudari, Javad and Mosavi, Amir and Shamshirband, Shahaboddin},
  title     = {A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning},
  series = {Mathematics},
  volume    = {2020},
  journal   = {Mathematics},
  number    = {volume 8, issue 2, article 286},
  publisher = {MDPI},
  doi       = {10.3390/math8020286},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200225-40996},
  pages     = {12},
  abstract  = {The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{ShabaniSamadianfardSattarietal.,
  author    = {Shabani, Sevda and Samadianfard, Saeed and Sattari, Mohammad Taghi and Mosavi, Amir and Shamshirband, Shahaboddin and Kmet, Tibor and V{\´a}rkonyi-K{\´o}czy, Annam{\´a}ria R.},
  title     = {Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis},
  series = {Atmosphere},
  volume    = {2020},
  journal   = {Atmosphere},
  number    = {Volume 11, Issue 1, 66},
  doi       = {10.3390/atmos11010066},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200110-40561},
  pages     = {17},
  abstract  = {Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{ShamshirbandBabanezhadMosavietal.,
  author    = {Shamshirband, Shahaboddin and Babanezhad, Meisam and Mosavi, Amir and Nabipour, Narjes and Hajnal, Eva and Nadai, Laszlo and Chau, Kwok-Wing},
  title     = {Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants},
  series = {Engineering Applications of Computational Fluid Mechanics},
  volume    = {2020},
  journal   = {Engineering Applications of Computational Fluid Mechanics},
  number    = {volume 14, issue 1},
  publisher = {Taylor \& Francis},
  doi       = {10.1080/19942060.2020.1715842},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200227-41013},
  pages     = {367 -- 378},
  abstract  = {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 for predicting various levels of three-dimensional bubble column reactor (BCR) flow. The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}