@article{FrommholzHaiderMPotthastetal.,
  author    = {Frommholz, Ingo and Haider M., al-Khateeb and Potthast, Martin and Ghasem, Zinnar and Shukla, Mitul and Short, Emma},
  title     = {On Textual Analysis and Machine Learning for Cyberstalking Detection},
  series = {Datenbank Spektrum},
  journal   = {Datenbank Spektrum},
  doi       = {10.1007/s13222-016-0221-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170418-31352},
  pages     = {127 -- 135},
  abstract  = {Cyber security has become a major concern for users and businesses alike. Cyberstalking and harassment have been identified as a growing anti-social problem. Besides detecting cyberstalking and harassment, there is the need to gather digital evidence, often by the victim. To this end, we provide an overview of and discuss relevant technological means, in particular coming from text analytics as well as machine learning, that are capable to address the above challenges. We present a framework for the detection of text-based cyberstalking and the role and challenges of some core techniques such as author identification, text classification and personalisation. We then discuss PAN, a network and evaluation initiative that focusses on digital text forensics, in particular author identification.},
  subject      = {Text Mining},
  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{ShamshirbandJoloudariGhasemiGoletal.,
  author    = {Shamshirband, Shahaboddin and Joloudari, Javad Hassannataj and GhasemiGol, Mohammad and Saadatfar, Hamid and Mosavi, Amir and Nabipour, Narjes},
  title     = {FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks},
  series = {Mathematics},
  volume    = {2020},
  journal   = {Mathematics},
  number    = {Volume 8, Issue 1, article 28},
  publisher = {MDPI},
  doi       = {10.3390/math8010028},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200107-40541},
  pages     = {24},
  abstract  = {Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.},
  subject      = {Vernetzung},
  language  = {en}
}
@article{BandJanizadehChandraPaletal.,
  author    = {Band, Shahab S. and Janizadeh, Saeid and Chandra Pal, Subodh and Saha, Asish and Chakrabortty, Rabbin and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility},
  series = {Sensors},
  volume    = {2020},
  journal   = {Sensors},
  number    = {Volume 20, issue 19, article 5609},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/s20195609},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43341},
  pages     = {1 -- 27},
  abstract  = {This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70\%) and testing (30\%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.},
  subject      = {Geoinformatik},
  language  = {en}
}
@article{HarirchianLahmerRasulzade,
  author    = {Harirchian, Ehsan and Lahmer, Tom and Rasulzade, Shahla},
  title     = {Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network},
  series = {Energies},
  volume    = {2020},
  journal   = {Energies},
  number    = {Volume 13, Issue 8, 2060},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en13082060},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200504-41575},
  pages     = {16},
  abstract  = {The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the D{\"u}zce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs.},
  subject      = {Erdbeben},
  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{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}
}
@article{MosaviShamshirbandEsmaeilbeikietal.,
  author    = {Mosavi, Amir and Shamshirband, Shahaboddin and Esmaeilbeiki, Fatemeh and Zarehaghi, Davoud and Neyshabouri, Mohammadreza and Samadianfard, Saeed and Ghorbani, Mohammad Ali and Nabipour, Narjes and Chau, Kwok-Wing},
  title     = {Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths},
  series = {Engineering Applications of Computational Fluid Mechanics},
  volume    = {2020},
  journal   = {Engineering Applications of Computational Fluid Mechanics},
  number    = {Volume 14, Issue 1},
  doi       = {10.1080/19942060.2020.1788644},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200911-42347},
  pages     = {939 -- 953},
  abstract  = {This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013-2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST5cm and ST 20cm of Tabriz station and ST10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.},
  subject      = {Bodentemperatur},
  language  = {en}
}
@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{DehghaniSalehiMosavietal.,
  author    = {Dehghani, Majid and Salehi, Somayeh and Mosavi, Amir and Nabipour, Narjes and Shamshirband, Shahaboddin and Ghamisi, Pedram},
  title     = {Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices},
  series = {ISPRS, International Journal of Geo-Information},
  volume    = {2020},
  journal   = {ISPRS, International Journal of Geo-Information},
  number    = {Volume 9, Issue 2, 73},
  publisher = {MDPI},
  doi       = {10.3390/ijgi9020073},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200128-40740},
  pages     = {23},
  abstract  = {Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Ni{\~n}o-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100\%. However, the seasonal precipitation may increase more than 100\% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{NabipourDehghaniMosavietal.,
  author    = {Nabipour, Narjes and Dehghani, Majid and Mosavi, Amir and Shamshirband, Shahaboddin},
  title     = {Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks},
  series = {IEEE Access},
  volume    = {2020},
  journal   = {IEEE Access},
  number    = {volume 8},
  publisher = {IEEE},
  doi       = {10.1109/ACCESS.2020.2964584},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200213-40796},
  pages     = {15210 -- 15222},
  abstract  = {Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{MengNomanQasemShokrietal.,
  author    = {Meng, Yinghui and Noman Qasem, Sultan and Shokri, Manouchehr and Shamshirband, Shahaboddin},
  title     = {Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis},
  series = {Mathematics},
  volume    = {2020},
  journal   = {Mathematics},
  number    = {volume 8, issue 8, article 1233},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/math8081233},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200811-42125},
  pages     = {15},
  abstract  = {In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful tool for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-wavelet-ANN models are compared with those obtained from wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicator in rivers.},
  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}
}
@article{MousaviSteinkeJuniorTeixeiraetal.,
  author    = {Mousavi, Seyed Nasrollah and Steinke J{\´u}nior, Renato and Teixeira, Eder Daniel and Bocchiola, Daniele and Nabipour, Narjes and Mosavi, Amir and Shamshirband, Shahaboddin},
  title     = {Predictive Modeling the Free Hydraulic Jumps Pressure through Advanced Statistical Methods},
  series = {Mathematics},
  volume    = {2020},
  journal   = {Mathematics},
  number    = {Volume 8, Issue 3, 323},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/math8030323},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200402-41140},
  pages     = {16},
  abstract  = {Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k\%), and the statistical coefficient of the probability distribution (Nk\%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk\%. The values of the Nk\% coefficient indicated a single mean value for each probability.},
  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}
}
@article{HarirchianLahmerKumarietal.,
  author    = {Harirchian, Ehsan and Lahmer, Tom and Kumari, Vandana and Jadhav, Kirti},
  title     = {Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings},
  series = {Energies},
  volume    = {2020},
  journal   = {Energies},
  number    = {volume 13, issue 13, 3340},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en13133340},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200707-41915},
  pages     = {15},
  abstract  = {The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 D{\"u}zce Earthquake in Turkey, where the building's data consists of 22 performance modifiers that have been implemented with supervised machine learning.},
  subject      = {Erdbeben},
  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{AbbaspourGilandehMolaeeSabzietal.,
  author    = {Abbaspour-Gilandeh, Yousef and Molaee, Amir and Sabzi, Sajad and Nabipour, Narjes and Shamshirband, Shahaboddin and Mosavi, Amir},
  title     = {A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars},
  series = {agronomy},
  volume    = {2020},
  journal   = {agronomy},
  number    = {Volume 10, Issue 1, 117},
  publisher = {MDPI},
  doi       = {10.3390/agronomy10010117},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200123-40695},
  pages     = {21},
  abstract  = {Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2\%, 87.7\%, and 83.1\%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100\% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@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}
}