@article{AbbaspourGilandehMolaeeSabzietal.,
  author    = {Abbaspour-Gilandeh, Yousef and Molaee, Amir and Sabzi, Sajad and Nabipour, Narjes and Shamshirband, Shahaboddin and Mosavi, Amir},
  title     = {A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars},
  series = {agronomy},
  volume    = {2020},
  journal   = {agronomy},
  number    = {Volume 10, Issue 1, 117},
  publisher = {MDPI},
  doi       = {10.3390/agronomy10010117},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200123-40695},
  pages     = {21},
  abstract  = {Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2\%, 87.7\%, and 83.1\%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100\% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{HarirchianKumariJadhavetal.,
  author    = {Harirchian, Ehsan and Kumari, Vandana and Jadhav, Kirti and Rasulzade, Shahla and Lahmer, Tom and Raj Das, Rohan},
  title     = {A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings},
  series = {Applied Sciences},
  volume    = {2021},
  journal   = {Applied Sciences},
  number    = {Volume 11, issue 16, article 7540},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app11167540},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210818-44853},
  pages     = {1 -- 33},
  abstract  = {A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings' present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.},
  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}
}