@article{GhazvineiDarvishiMosavietal.,
  author    = {Ghazvinei, Pezhman Taherei and Darvishi, Hossein Hassanpour and Mosavi, Amir and Yusof, Khamaruzaman bin Wan and Alizamir, Meysam and Shamshirband, Shahaboddin and Chau, Kwok-Wing},
  title     = {Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network},
  series = {Engineering Applications of Computational Fluid Mechanics},
  volume    = {2018},
  journal   = {Engineering Applications of Computational Fluid Mechanics},
  number    = {12,1},
  publisher = {Taylor \& Francis},
  doi       = {10.1080/19942060.2018.1526119},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181017-38129},
  pages     = {738 -- 749},
  abstract  = {Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.},
  subject      = {K{\"u}nstliche Intelligenz},
  language  = {en}
}
@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{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{MeiabadiMoradiKaramimoghadametal.,
  author    = {Meiabadi, Mohammad Saleh and Moradi, Mahmoud and Karamimoghadam, Mojtaba and Ardabili, Sina and Bodaghi, Mahdi and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication},
  series = {polymers},
  volume    = {2021},
  journal   = {polymers},
  number    = {Volume 13, issue 19, article 3219},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/polym13193219},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220110-45518},
  pages     = {1 -- 21},
  abstract  = {Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5\% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.},
  subject      = {3D-Druck},
  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{AhmadiBaghbanSadeghzadehetal.,
  author    = {Ahmadi, Mohammad Hossein and Baghban, Alireza and Sadeghzadeh, Milad and Zamen, Mohammad and Mosavi, Amir and Shamshirband, Shahaboddin and Kumar, Ravinder and Mohammadi-Khanaposhtani, Mohammad},
  title     = {Evaluation of electrical efficiency of photovoltaic thermal solar collector},
  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       = {10.1080/19942060.2020.1734094},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200304-41049},
  pages     = {545 -- 565},
  abstract  = {In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.},
  subject      = {Fotovoltaik},
  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{HomaeiSoleimaniShamshirbandetal.,
  author    = {Homaei, Mohammad Hossein and Soleimani, Faezeh and Shamshirband, Shahaboddin and Mosavi, Amir and Nabipour, Narjes and Varkonyi-Koczy, Annamaria R.},
  title     = {An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System},
  series = {IEEE Access},
  journal   = {IEEE Access},
  number    = {volume 8},
  publisher = {IEEE},
  doi       = {10.1109/ACCESS.2020.2968524},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200213-40805},
  pages     = {20628 -- 20645},
  abstract  = {The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms.},
  subject      = {Internet der dinge},
  language  = {en}
}