@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{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{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{SaqlaiGhaniKhanetal., author = {Saqlai, Syed Muhammad and Ghani, Anwar and Khan, Imran and Ahmed Khan Ghayyur, Shahbaz and Shamshirband, Shahaboddin and Nabipour, Narjes and Shokri, Manouchehr}, title = {Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification}, series = {Applied Sciences}, volume = {2020}, journal = {Applied Sciences}, number = {volume 10, issue 16, article 5453}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/app10165453}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200904-42322}, pages = {24}, abstract = {Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.}, subject = {Bildanalyse}, 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{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{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{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{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} } @unpublished{MosaviTorabiHashemietal., author = {Mosavi, Amir and Torabi, Mehrnoosh and Hashemi, Sattar and Saybani, Mahmoud Reza and Shamshirband, Shahaboddin}, title = {A Hybrid Clustering and Classification Technique for Forecasting Short-Term Energy Consumption}, doi = {10.25643/bauhaus-universitaet.3755}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20180907-37550}, abstract = {Electrical energy distributor companies in Iran have to announce their energy demand at least three 3-day ahead of the market opening. Therefore, an accurate load estimation is highly crucial. This research invoked methodology based on CRISP data mining and used SVM, ANN, and CBA-ANN-SVM (a novel hybrid model of clustering with both widely used ANN and SVM) to predict short-term electrical energy demand of Bandarabbas. In previous studies, researchers introduced few effective parameters with no reasonable error about Bandarabbas power consumption. In this research we tried to recognize all efficient parameters and with the use of CBA-ANN-SVM model, the rate of error has been minimized. After consulting with experts in the field of power consumption and plotting daily power consumption for each week, this research showed that official holidays and weekends have impact on the power consumption. When the weather gets warmer, the consumption of electrical energy increases due to turning on electrical air conditioner. Also, con-sumption patterns in warm and cold months are different. Analyzing power consumption of the same month for different years had shown high similarity in power consumption patterns. Factors with high impact on power consumption were identified and statistical methods were utilized to prove their impacts. Using SVM, ANN and CBA-ANN-SVM, the model was built. Sine the proposed method (CBA-ANN-SVM) has low MAPE 5 1.474 (4 clusters) and MAPE 5 1.297 (3 clusters) in comparison with SVM (MAPE 5 2.015) and ANN (MAPE 5 1.790), this model was selected as the final model. The final model has the benefits from both models and the benefits of clustering. Clustering algorithm with discovering data structure, divides data into several clusters based on similarities and differences between them. Because data inside each cluster are more similar than entire data, modeling in each cluster will present better results. For future research, we suggest using fuzzy methods and genetic algorithm or a hybrid of both to forecast each cluster. It is also possible to use fuzzy methods or genetic algorithms or a hybrid of both without using clustering. It is issued that such models will produce better and more accurate results. This paper presents a hybrid approach to predict the electric energy usage of weather-sensitive loads. The presented methodutilizes the clustering paradigm along with ANN and SVMapproaches for accurate short-term prediction of electric energyusage, using weather data. Since the methodology beinginvoked in this research is based on CRISP data mining, datapreparation has received a gr eat deal of attention in thisresear ch. Once data pre-processing was done, the underlyingpattern of electric energy consumption was extracted by themeans of machine learning methods to precisely forecast short-term energy consumption. The proposed approach (CBA-ANN-SVM) was applied to real load data and resulting higher accu-racy comparing to the existing models. 2018 American Institute of Chemical Engineers Environ Prog, 2018 https://doi.org/10.1002/ep.12934}, subject = {Data Mining}, language = {en} }