@article{HassannatajJoloudariHassannatajJoloudariSaadatfaretal.,
  author    = {Hassannataj Joloudari, Javad and Hassannataj Joloudari, Edris and Saadatfar, Hamid and GhasemiGol, Mohammad and Razavi, Seyyed Mohammad and Mosavi, Amir and Nabipour, Narjes and Shamshirband, Shahaboddin and Nadai, Laszlo},
  title     = {Coronary Artery Disease Diagnosis: Ranking the Significant Features Using a Random Trees Model},
  series = {International Journal of Environmental Research and Public Health, IJERPH},
  volume    = {2020},
  journal   = {International Journal of Environmental Research and Public Health, IJERPH},
  number    = {Volume 17, Issue 3, 731},
  publisher = {MDPI},
  doi       = {10.3390/ijerph17030731},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200213-40819},
  pages     = {24},
  abstract  = {Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{KargarSamadianfardParsaetal.,
  author    = {Kargar, Katayoun and Samadianfard, Saeed and Parsa, Javad and Nabipour, Narjes and Shamshirband, Shahaboddin and Mosavi, Amir and Chau, Kwok-Wing},
  title     = {Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms},
  series = {Engineering Applications of Computational Fluid Mechanics},
  volume    = {2020},
  journal   = {Engineering Applications of Computational Fluid Mechanics},
  number    = {Volume 14, No. 1},
  publisher = {Taylor \& Francis},
  doi       = {10.1080/19942060.2020.1712260},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200128-40775},
  pages     = {311 -- 322},
  abstract  = {The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various studies have been conducted to provide simple equations for estimating LDC. In this study, machine learning methods, namely support vector regression, Gaussian process regression, M5 model tree (M5P) and random forest, and multiple linear regression were examined in predicting the LDC in natural streams. Data sets from 60 rivers around the world with different hydraulic and geometric features were gathered to develop models for LDC estimation. Statistical criteria, including correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE), were used to scrutinize the models. The LDC values estimated by these models were compared with the corresponding results of common empirical models. The Taylor chart was used to evaluate the models and the results showed that among the machine learning models, M5P had superior performance, with CC of 0.823, RMSE of 454.9 and MAE of 380.9. The model of Sahay and Dutta, with CC of 0.795, RMSE of 460.7 and MAE of 306.1, gave more precise results than the other empirical models. The main advantage of M5P models is their ability to provide practical formulae. In conclusion, the results proved that the developed M5P model with simple formulations was superior to other machine learning models and empirical models; therefore, it can be used as a proper tool for estimating the LDC in rivers.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{BandJanizadehSahaetal.,
  author    = {Band, Shahab S. and Janizadeh, Saeid and Saha, Sunil and Mukherjee, Kaustuv and Khosrobeigi Bozchaloei, Saeid and Cerd{\`a}, Artemi and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data},
  series = {Land},
  volume    = {2020},
  journal   = {Land},
  number    = {volume 9, issue 10, article 346},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/land9100346},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43424},
  pages     = {1 -- 22},
  abstract  = {Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70\%) and validation (30\%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}