TY - JOUR A1 - Meiabadi, Mohammad Saleh A1 - Moradi, Mahmoud A1 - Karamimoghadam, Mojtaba A1 - Ardabili, Sina A1 - Bodaghi, Mahdi A1 - Shokri, Manouchehr A1 - Mosavi, Amir Hosein T1 - Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication JF - polymers N2 - 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. KW - 3D-Druck KW - Polymere KW - Maschinelles Lernen KW - 3D printing KW - machine learning KW - fused filament fabrication KW - OA-Publikationsfonds2021 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220110-45518 UR - https://www.mdpi.com/2073-4360/13/19/3219 VL - 2021 IS - Volume 13, issue 19, article 3219 SP - 1 EP - 21 PB - MDPI CY - Basel ER - TY - JOUR A1 - Lashkar-Ara, Babak A1 - Kalantari, Niloofar A1 - Sheikh Khozani, Zohreh A1 - Mosavi, Amir T1 - Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel JF - Mathematics N2 - One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations. KW - Maschinelles Lernen KW - smooth rectangular channel KW - Tsallis entropy KW - genetic programming KW - artificial intelligence KW - machine learning KW - big data KW - computational hydraulics Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210504-44197 UR - https://www.mdpi.com/2227-7390/9/6/596 VL - 2021 IS - Volume 9, Issue 6, Article 596 PB - MDPI CY - Basel ER - TY - JOUR A1 - Band, Shahab S. A1 - Janizadeh, Saeid A1 - Chandra Pal, Subodh A1 - Saha, Asish A1 - Chakrabortty, Rabbin A1 - Shokri, Manouchehr A1 - Mosavi, Amir Hosein T1 - Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility JF - Sensors N2 - 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. KW - Geoinformatik KW - Maschinelles Lernen KW - gully erosion susceptibility KW - deep learning neural network KW - partical swarm optimization KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43341 UR - https://www.mdpi.com/1424-8220/20/19/5609 VL - 2020 IS - Volume 20, issue 19, article 5609 SP - 1 EP - 27 PB - MDPI CY - Basel ER - TY - JOUR A1 - Sadeghzadeh, Milad A1 - Maddah, Heydar A1 - Ahmadi, Mohammad Hossein A1 - Khadang, Amirhosein A1 - Ghazvini, Mahyar A1 - Mosavi, Amir Hosein A1 - Nabipour, Narjes T1 - Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network JF - Nanomaterials N2 - In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Text KW - Wärmeleitfähigkeit KW - Fluid KW - Neuronales Netz KW - Thermal conductivity KW - Nanofluid KW - Artificial neural network Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200421-41308 UR - https://www.mdpi.com/2079-4991/10/4/697 VL - 2020 IS - Volume 10, Issue 4, 697 PB - MDPI CY - Basel ER - TY - JOUR A1 - Saadatfar, Hamid A1 - Khosravi, Samiyeh A1 - Hassannataj Joloudari, Javad A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin T1 - A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning JF - Mathematics N2 - 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. KW - Maschinelles Lernen KW - Machine learning KW - K-nearest neighbors KW - KNN KW - classifier KW - big data KW - clustering KW - cluster shape KW - cluster density KW - classification KW - reinforcement learning KW - data science KW - computation KW - artificial intelligence KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200225-40996 UR - https://www.mdpi.com/2227-7390/8/2/286 VL - 2020 IS - volume 8, issue 2, article 286 PB - MDPI ER - TY - JOUR A1 - Ahmadi, Mohammad Hossein A1 - Baghban, Alireza A1 - Sadeghzadeh, Milad A1 - Zamen, Mohammad A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin A1 - Kumar, Ravinder A1 - Mohammadi-Khanaposhtani, Mohammad T1 - Evaluation of electrical efficiency of photovoltaic thermal solar collector JF - Engineering Applications of Computational Fluid Mechanics N2 - 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. KW - Fotovoltaik KW - Erneuerbare Energien KW - Solar KW - Deep learning KW - Machine learning KW - Renewable energy KW - neural networks (NNs) KW - adaptive neuro-fuzzy inference system (ANFIS) KW - least square support vector machine (LSSVM) KW - photovoltaic-thermal (PV/T) KW - hybrid machine learning model KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200304-41049 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1734094 VL - 2020 IS - volume 14, issue 1 SP - 545 EP - 565 PB - Taylor & Francis ER - TY - JOUR A1 - Shamshirband, Shahaboddin A1 - Babanezhad, Meisam A1 - Mosavi, Amir A1 - Nabipour, Narjes A1 - Hajnal, Eva A1 - Nadai, Laszlo A1 - Chau, Kwok-Wing T1 - Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants JF - Engineering Applications of Computational Fluid Mechanics N2 - 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. KW - Maschinelles Lernen KW - Machine learning KW - Bubble column reactor KW - ant colony optimization algorithm (ACO) KW - flow pattern KW - computational fluid dynamics (CFD) KW - big data KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200227-41013 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1715842 VL - 2020 IS - volume 14, issue 1 SP - 367 EP - 378 PB - Taylor & Francis ER - TY - JOUR A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin A1 - Esmaeilbeiki, Fatemeh A1 - Zarehaghi, Davoud A1 - Neyshabouri, Mohammadreza A1 - Samadianfard, Saeed A1 - Ghorbani, Mohammad Ali A1 - Nabipour, Narjes A1 - Chau, Kwok-Wing T1 - Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths JF - Engineering Applications of Computational Fluid Mechanics N2 - 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. KW - Bodentemperatur KW - Algorithmus KW - Maschinelles Lernen KW - Neuronales Netz KW - firefly optimization algorithm KW - soil temperature KW - artificial neural networks KW - hybrid machine learning KW - OA-Publikationsfonds2019 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200911-42347 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1788644 VL - 2020 IS - Volume 14, Issue 1 SP - 939 EP - 953 ER - TY - JOUR A1 - Amirinasab, Mehdi A1 - Shamshirband, Shahaboddin A1 - Chronopoulos, Anthony Theodore A1 - Mosavi, Amir A1 - Nabipour, Narjes T1 - Energy‐Efficient Method for Wireless Sensor Networks Low‐Power Radio Operation in Internet of Things JF - electronics N2 - The radio operation in wireless sensor networks (WSN) in Internet of Things (IoT)applications is the most common source for power consumption. Consequently, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. Among essential factors affecting radio operation, the time spent for checking the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in radio duty cycles and ContikiMAC. ContikiMAC is a low‐power radio duty‐cycle protocol in Contiki OS used in WakeUp mode, as a clear channel assessment (CCA) for checking radio status periodically. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Furthermore, we propose a lightweight CCA (LW‐CCA) as an extension to ContikiMAC to reduce the Radio Duty‐Cycles in false WakeUps and idle listening though using dynamic received signal strength indicator (RSSI) status check time. The simulation results in the Cooja simulator show that LW‐CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR). KW - Internet der Dinge KW - Internet of things KW - wireless sensor networks KW - ContikiMAC KW - energy efficiency KW - duty-cycles KW - clear channel assessments KW - fog computing KW - smart sensors KW - signal processing KW - received signal strength indicator KW - OA-Publikationsfonds2020 KW - RSSI Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40954 UR - https://www.mdpi.com/2079-9292/9/2/320 VL - 2020 IS - volume 9, issue 2, 320 PB - MDPI ER - TY - JOUR A1 - Homaei, Mohammad Hossein A1 - Soleimani, Faezeh A1 - Shamshirband, Shahaboddin A1 - Mosavi, Amir A1 - Nabipour, Narjes A1 - Varkonyi-Koczy, Annamaria R. T1 - An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System JF - IEEE Access N2 - 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. KW - Internet der dinge KW - IOT KW - Internet of things KW - wireless sensor network KW - congestion control KW - fuzzy decision making KW - back-pressure Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40805 UR - https://ieeexplore.ieee.org/document/8967114 IS - volume 8 SP - 20628 EP - 20645 PB - IEEE ER -