TY  - JOUR
A1  - Frommholz, Ingo
A1  - Haider M., al-Khateeb
A1  - Potthast, Martin
A1  - Ghasem, Zinnar
A1  - Shukla, Mitul
A1  - Short, Emma
T1  - On Textual Analysis and Machine Learning for Cyberstalking Detection
JF  - Datenbank Spektrum
N2  - 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.
KW  - Text Mining
KW  - Maschinelles Lernen
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170418-31352
SP  - 127
EP  - 135
ER  - 
TY  - JOUR
A1  - Ouaer, Hocine
A1  - Hosseini, Amir Hossein
A1  - Amar, Menad Nait
A1  - Ben Seghier, Mohamed El Amine
A1  - Ghriga, Mohammed Abdelfetah
A1  - Nabipour, Narjes
A1  - Andersen, Pål Østebø
A1  - Mosavi, Amir
A1  - Shamshirband, Shahaboddin
T1  - Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids
JF  - Applied Sciences
N2  - 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.
KW  - Maschinelles Lernen
KW  - Machine learning
KW  - OA-Publikationsfonds2020
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200107-40558
UR  - https://www.mdpi.com/2076-3417/10/1/304
VL  - 2020
IS  - Volume 10, Issue 1, 304
PB  - MDPI
ER  - 
TY  - JOUR
A1  - Shamshirband, Shahaboddin
A1  - Joloudari, Javad Hassannataj
A1  - GhasemiGol, Mohammad
A1  - Saadatfar, Hamid
A1  - Mosavi, Amir
A1  - Nabipour, Narjes
T1  - FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks
JF  - Mathematics
N2  - 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.
KW  - Vernetzung
KW  - wireless sensor networks
KW  - machine learning
KW  - Funktechnik
KW  - Sensor
KW  - Maschinelles Lernen
KW  - Internet of Things
KW  - OA-Publikationsfonds2019
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200107-40541
UR  - https://www.mdpi.com/2227-7390/8/1/28
VL  - 2020
IS  - Volume 8, Issue 1, article 28
PB  - MDPI
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  - Harirchian, Ehsan
A1  - Lahmer, Tom
A1  - Rasulzade, Shahla
T1  - Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network
JF  - Energies
N2  - 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 Düzce 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.
KW  - Erdbeben
KW  - Maschinelles Lernen
KW  - earthquake damage
KW  - seismic vulnerability
KW  - artificial neural network
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200504-41575
UR  - https://www.mdpi.com/1996-1073/13/8/2060/htm
VL  - 2020
IS  - Volume 13, Issue 8, 2060
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  - 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  - Hassannataj Joloudari, Javad
A1  - Hassannataj Joloudari, Edris
A1  - Saadatfar, Hamid
A1  - GhasemiGol, Mohammad
A1  - Razavi, Seyyed Mohammad
A1  - Mosavi, Amir
A1  - Nabipour, Narjes
A1  - Shamshirband, Shahaboddin
A1  - Nadai, Laszlo
T1  - Coronary Artery Disease Diagnosis: Ranking the Significant Features Using a Random Trees Model
JF  - International Journal of Environmental Research and Public Health,  IJERPH
N2  - 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.
KW  - Maschinelles Lernen
KW  - Machine learning
KW  - Deep learning
KW  - coronary artery disease
KW  - heart disease diagnosis
KW  - health informatics
KW  - data science
KW  - big data
KW  - predictive model
KW  - ensemble model
KW  - random forest
KW  - industry 4.0
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40819
UR  - https://www.mdpi.com/1660-4601/17/3/731
VL  - 2020
IS  - Volume 17, Issue 3, 731
PB  - MDPI
ER  - 
TY  - JOUR
A1  - Kargar, Katayoun
A1  - Samadianfard, Saeed
A1  - Parsa, Javad
A1  - Nabipour, Narjes
A1  - Shamshirband, Shahaboddin
A1  - Mosavi, Amir
A1  - Chau, Kwok-Wing
T1  - Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms
JF  - Engineering Applications of Computational Fluid Mechanics
N2  - 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.
KW  - Maschinelles Lernen
KW  - Gaussian process regression
KW  - longitudinal dispersion coefficient
KW  - M5 model tree
KW  - random forest
KW  - support vector regression
KW  - rivers
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200128-40775
UR  - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1712260
VL  - 2020
IS  - Volume 14, No. 1
SP  - 311
EP  - 322
PB  - Taylor & Francis
ER  -