Detecting earthquake damage levels using adaptive boosting

Herfeh, Mona Peyk; Shahbahrami, Asadollah; Miandehi, Farshad Parhizkar

doi:10.1109/iranianmvip.2013.6779989

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…In another study conducted for the prognosis of cervical cancer, the AdaBoost algorithm was more accurate than the genetic algorithm in cervical cancer prognosis classifications 17,18 . In other studies, the AdaBoost algorithm has been introduced as a strong algorithm in the classification of variables 18‐21 …”

Section: Introductionmentioning

confidence: 99%

“…17,18 In other studies, the AdaBoost algorithm has been introduced as a strong algorithm in the classification of variables. [18][19][20][21] It should be noted that there is no absolute "best" among different algorithms and statistical models, and the accuracy of different models in data classification depends on the nature of the data too. Therefore, the model should be selected according to the desired data and performance.…”

Section: Introductionmentioning

confidence: 99%

“… 17 , 18 In other studies, the AdaBoost algorithm has been introduced as a strong algorithm in the classification of variables. 18 , 19 , 20 , 21 …”

Section: Introductionmentioning

confidence: 99%

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The most important variables associated with death due to COVID‐19 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross‐sectional study

gharehhasani,

Rezaei,

Naghipour

et al. 2024

Health Science Reports

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IntroductionDeath due to covid‐19 is one of the biggest health challenges in the world. There are many models that can predict death due to COVID‐19. This study aimed to fit and compare Decision Tree (DT), Support Vector Machine (SVM), and AdaBoost models to predict death due to COVID‐19.MethodsTo describe the variables, mean (SD) and frequency (%) were reported. To determine the relationship between the variables and the death caused by COVID‐19, chi‐square test was performed with a significance level of 0.05. To compare DT, SVM and AdaBoost models for predicting death due to COVID‐19 from sensitivity, specificity, accuracy and the area under the rock curve under R software using psych, caTools, random over‐sampling examples, rpart, rpartplot packages was done.ResultsOut of the total of 23,054 patients studied, 10,935 cases (46.5%) were women, and 12,569 cases (53.5%) were men. Additionally, the mean age of the patients was 54.9 ± 21.0 years. There is a statistically significant relationship between gender, fever, cough, muscle pain, smell and taste, abdominal pain, nausea and vomiting, diarrhea, anorexia, dizziness, chest pain, intubation, cancer, diabetes, chronic blood disease, Violation of immunity, pregnancy, Dialysis, chronic lung disease with the death of covid‐19 patients showed (p < 0.05). The results showed that the sensitivity, specificity, accuracy and the area under the receiver operating characteristic curve were respectively 0.60, 0.68, 0.71, and 0.75 in the DT model, 0.54, 0.62, 0.63, and 0.71 in the SVM model, and 0.59, 0.65, 0.69 and 0.74 in the AdaBoost model.ConclusionThe results showed that DT had a high predictive power compared to other data mining models. Therefore, it is suggested to researchers in different fields to use DT to predict the studied variables. Also, it is suggested to use other approaches such as random forest or XGBoost to improve the accuracy in future studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The most important variables associated with death due to COVID‐19 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross‐sectional study

gharehhasani,

Rezaei,

Naghipour

et al. 2024

Health Science Reports

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“…Our results show that the accuracy of adaptive boosting is about 79 percent while the accuracy of neural network is about 65 percent. This paper is an extension version of [14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Adaptive Boosting and Neural Network in Earthquake Damage Levels Detection

PeykHerfeh¹,

Shahbahrami²

2014

IJCA

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When an earthquake happens, the image-based techniques are influential tools for detection and classification of damaged buildings. Obtaining precise and exhaustive information about the condition and state of damaged buildings after an earthquake is basis of disaster management. Today's using satellite imageries has been becoming more significant data for disaster management. In this paper, an approach for detecting and classifying of damaged buildings using satellite imageries and digital map is proposed. In this approach after extracting buildings position from digital map, they are located in the pre-and post-event images. After generating features, genetic algorithm applied for obtaining optimal features. For classification, adaptive boosting and neural networks are utilized and compared with each other. These machine learning algorithms divided the damage levels into three classes of high, moderate and low levels. Experimental results which have been obtained from Bam earthquake images show that total accuracy of adaptive boosting for detecting and classifying of collapsed through uncollapsed buildings is about 79 percent while total accuracy of neural networks is about 65 percent.

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Using high-resolution satellite imagery to provide a relief priority map after earthquake

et al. 2017

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Detecting earthquake damage levels using adaptive boosting

Cited by 5 publications

References 16 publications

The most important variables associated with death due to COVID‐19 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross‐sectional study

The most important variables associated with death due to COVID‐19 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross‐sectional study

Evaluation of Adaptive Boosting and Neural Network in Earthquake Damage Levels Detection

Using high-resolution satellite imagery to provide a relief priority map after earthquake

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