Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2022COVID-19 diagnosis with deep learning; [Diagnóstico de COVID-19 con Deep Learning]
Reis, Hatice Catal
The coronavirus disease 2019 (COVID-19) is fatal and spreading rapidly. Early detection and diagnosis of the COVID-19 infection will prevent rapid spread. This study aims to automatically detect COVID-19 through a chest computed tomography (CT) dataset. The standard models for automatic COVID-19 detection using raw chest CT images are presented. This study uses convolutional neural network (CNN), Zeiler and Fergus network (ZFNet), and dense convolutional network-121 (DenseNet121) architectures of deep convolutional neural network models. The proposed models are presented to provide accurate diagnosis for binary classification. The datasets were obtained from a public database. This retrospective study included 757 chest CT images (360 confirmed COVID-19 and 397 non-COVID-19 chest CT images). The algorithms were coded using the Python programming language. The performance metrics used were accuracy, precision, recall, F1-score, and ROC-AUC. Comparative analyses are presented between the three models by considering hyper-parameter factors to find the best model. We obtained the best performance, with an accuracy of 94,7%, a recall of 90%, a precision of 100%, and an F1-score of 94,7% from the CNN model. As a result, the CNN algorithm is more accurate and precise than the ZFNet and DenseNet121 models. This study can present a second point of view to medical staff.
- MakaleAçık Erişim2020A comparison of alternative curve fitting techniques for different earthquake fault parameters of Iranian earthquakes; [Comparación de técnicas de ajuste de curvas para diferentes parámetros de fallas en terremotos en Irán]
Öztürk, Serkan; Ghassemi, Mohammad R.; Sarı, Mahmut
In this study, we tried to estimate the optimum linear equations among the parameters associated with different earthquake fault mechanisms for Iranian earthquakes. For this purpose, we tested different curve fitting methods in order to present the most proper empirical relationships between several seismic parameters for different fault systems. In the present paper, 46 large and destructive Iranian earthquakes whose magnitudes change between 5.8 and 7.8 from 1900 to 2014 were used for the analyses. A comparison was made by using four types of curve fitting techniques. The estimation procedures are considered as (1) L2 or Least Squares Regression, (2) L1 or Least Sum of Absolute Deviations Regression, (3) Robust Regression and, (4) Orthogonal Regression. Confidence intervals were selected as 95% for all types of regression relationships. In the selection of the best probability distribution, we considered the correlation coefficients of the linear regressions as a powerful and conceptually simple method. Correlation coefficients of all relationships change between 0.299 and 0.986 with Orthogonal regression, between 0.168 and 0.792 with L1 regression, between 0.059 and 0.829 with Robust regression. For Iranian earthquakes, the most suitable and reliable empirical relationships between moment magnitude (Mw) and surface wave magnitude (Ms), Mw and surface rupture length (SRL), Mw and maximum displacement (MD), and SRL and MD were obtained by Orthogonal regression since it supplies stronger correlation coefficients than those of the other regression techniques in most estimates. The results show that estimated empirical relationships among the different fault parameters by using the Orthogonal regression method can be accepted as more up-to-date and more appropriate in comparison with the other regression norms. Consequently, these equations were suggested as more reliable in the estimation of the maximum surface displacement, maximum surface rupture length and associated with the maximum credible earthquakes for different areas of Iran. Furthermore, obtained relationships can be statistically significant for the assessment of seismic, tectonic and geologic activities, and they can be used to evaluate the rupture hazard of the Iranian Plateau.
- MakaleAçık Erişim2017Space-time assessing of the earthquake potential in recent years in the Eastern Anatolia region of Turkey; [Evaluación espacio-temporal del potencial de terremotos en los años recientes para la región de Anatolia, en el este de Turquía]
Öztürk, S.
In this study, a statistical analysis is carried out to reveal the current earthquake potential in the Eastern Anatolia region of Turkey. For this purpose, a space-time assessing based on the recent changes in Gutenberg-Richter b-value, seismic quiescence Z-value, cumulative moment, annual probability and recurrence time of earthquakes is achieved. Temporal variation of b-value indicates that average b-value decreased from 1.25±0.02 between 2002 and 2010 to 1.08±0.02 between 2011 and 2015. A clear quiescence in the seismic moment in recent years may be a clue foretelling the occurrence of an intense event. Annual probability of the earthquakes between 3.5 and 4.0 magnitude levels exhibits a value equal to and larger than 30. Recurrence time of the earthquakes has a value between 2.0 and 3.0 years for magnitude level of 6.0, and a value between 6.0 and 7.0 years for magnitude level of 7.0. A significant decrease in b-value and clear quiescence anomalies in Z-value at the beginning of 2015 are observed in the several same regions. These areas cover the Central Anatolia Fault Zone, Malatya and Ovacık faults, the southeastern part of the Eastern Anatolia Fault Zone, and the junction of the Eastern Anatolia and Dead Sea Fault Zones. In these anomaly regions, a decreasing trend in b-value may be an indicator of stress increases and an increasing trend in Z-value may show the quiescence areas before the next earthquake occurrences. As a remarkable fact, these areas in the Eastern Anatolia region may be interpreted as one of the most likely locations for the major earthquakes in the future.
- DergiIngenieria e Investigacion
- DergiEarth Sciences Research Journal