TY - JOUR A1 - Abbaspour-Gilandeh, Yousef A1 - Molaee, Amir A1 - Sabzi, Sajad A1 - Nabipour, Narjes A1 - Shamshirband, Shahaboddin A1 - Mosavi, Amir T1 - A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars JF - agronomy N2 - 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. KW - Maschinelles Lernen KW - Machine learning KW - food informatics KW - big data KW - artificial neural networks KW - artificial intelligence KW - image processing KW - rice Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200123-40695 UR - https://www.mdpi.com/2073-4395/10/1/117 VL - 2020 IS - Volume 10, Issue 1, 117 PB - MDPI ER - TY - JOUR A1 - Artus, Mathias A1 - Alabassy, Mohamed Said Helmy A1 - Koch, Christian T1 - A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC JF - Applied Sciences N2 - Paper-based data acquisition and manual transfer between incompatible software or data formats during inspections of bridges, as done currently, are time-consuming, error-prone, cumbersome, and lead to information loss. A fully digitized workflow using open data formats would reduce data loss, efforts, and the costs of future inspections. On the one hand, existing studies proposed methods to automatize data acquisition and visualization for inspections. These studies lack an open standard to make the gathered data available for other processes. On the other hand, several studies discuss data structures for exchanging damage information among different stakeholders. However, those studies do not cover the process of automatic data acquisition and transfer. This study focuses on a framework that incorporates automatic damage data acquisition, transfer, and a damage information model for data exchange. This enables inspectors to use damage data for subsequent analyses and simulations. The proposed framework shows the potentials for a comprehensive damage information model and related (semi-)automatic data acquisition and processing. KW - Building Information Modeling KW - Brücke KW - Inspektion KW - Maschinelles Lernen KW - Bildverarbeitung KW - Building Information Modeling KW - Bridge KW - Inspection KW - Damage Segmentation KW - Machine Learning KW - OA-Publikationsfonds2022 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220314-46059 UR - https://www.mdpi.com/2076-3417/12/6/2772 VL - 2022 IS - volume 12, issue 6, article 2772 SP - 1 EP - 24 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 - Band, Shahab S. A1 - Janizadeh, Saeid A1 - Saha, Sunil A1 - Mukherjee, Kaustuv A1 - Khosrobeigi Bozchaloei, Saeid A1 - Cerdà, Artemi A1 - Shokri, Manouchehr A1 - Mosavi, Amir Hosein T1 - Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data JF - Land N2 - Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70%) and validation (30%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed. KW - Maschinelles Lernen KW - Bayes-Verfahren KW - Naturkatastrophe KW - random forest KW - support vector machine KW - geoinformatics KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43424 UR - https://www.mdpi.com/2073-445X/9/10/346 VL - 2020 IS - volume 9, issue 10, article 346 SP - 1 EP - 22 PB - MDPI CY - Basel ER - TY - JOUR A1 - Chakraborty, Ayan A1 - Anitescu, Cosmin A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Domain adaptation based transfer learning approach for solving PDEs on complex geometries JF - Engineering with Computers N2 - In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems. KW - Maschinelles Lernen KW - NURBS KW - Transfer learning KW - Domain Adaptation KW - NURBS geometry KW - Navier–Stokes equations Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220811-46776 UR - https://link.springer.com/article/10.1007/s00366-022-01661-2 VL - 2022 SP - 1 EP - 20 ER - TY - JOUR A1 - Dehghani, Majid A1 - Salehi, Somayeh A1 - Mosavi, Amir A1 - Nabipour, Narjes A1 - Shamshirband, Shahaboddin A1 - Ghamisi, Pedram T1 - Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices JF - ISPRS, International Journal of Geo-Information N2 - Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation. KW - Maschinelles Lernen KW - Machine learning KW - spatiotemporal database KW - spatial analysis KW - seasonal precipitation KW - spearman correlation coefficient Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200128-40740 UR - https://www.mdpi.com/2220-9964/9/2/73 VL - 2020 IS - Volume 9, Issue 2, 73 PB - MDPI ER - 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 - Harirchian, Ehsan A1 - Kumari, Vandana A1 - Jadhav, Kirti A1 - Rasulzade, Shahla A1 - Lahmer, Tom A1 - Raj Das, Rohan T1 - A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings JF - Applied Sciences N2 - 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. KW - Maschinelles Lernen KW - Neuronales Netz KW - Machine learning KW - Building safety assessment KW - artificial neural networks KW - supervised learning KW - damaged buildings KW - rapid classification KW - OA-Publikationsfonds2021 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210818-44853 UR - https://www.mdpi.com/2076-3417/11/16/7540 VL - 2021 IS - Volume 11, issue 16, article 7540 SP - 1 EP - 33 PB - MDPI CY - Basel ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Buddhiraju, Sreekanth A1 - Mohammad, Kifaytullah A1 - Mosavi, Amir T1 - Earthquake Safety Assessment of Buildings through Rapid Visual Screening JF - Buildings N2 - Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bingöl region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively. KW - Maschinelles Lernen KW - Machine learning KW - Erdbeben KW - buildings KW - earthquake safety assessment KW - earthquake KW - extreme events KW - seismic assessment KW - natural hazard KW - mitigation KW - rapid visual screening Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200331-41153 UR - https://www.mdpi.com/2075-5309/10/3/51 VL - 2020 IS - Volume 10, Issue 3 PB - MDPI ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Kumari, Vandana A1 - Jadhav, Kirti T1 - Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings JF - Energies N2 - The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning. KW - Erdbeben KW - Maschinelles Lernen KW - earthquake vulnerability assessment KW - rapid visual screening KW - machine learning KW - support vector machine KW - buildings KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200707-41915 UR - https://www.mdpi.com/1996-1073/13/13/3340 VL - 2020 IS - volume 13, issue 13, 3340 PB - MDPI CY - Basel ER -