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  - 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  -