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    Assessing and mapping landslide susceptibility using different machine learning methods
    (Taylor & Francis, 2022) Orhan, Osman; Bilgilioğlu, Süleyman Sefa; Kaya, Zehra; Özcan, Adem Kürşat; Bilgilioğlu, Hacer
    The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide incidents occur in Turkey. Firstly, a landslide inventory was produced by identifying a total of 252 landslides. Secondly, a total of 11 landslide conditioning factors were considered for the landslide susceptibility mapping. Subsequently, the five machine learning techniques were constructed with the help of the training dataset for the landslide susceptibility maps. Finally, the receiver operating characteristic (ROC), sensitivity, specificity, F-measure, accuracy and kappa index were applied to compare and validate the performance of the five machine learning techniques.
  • Küçük Resim
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    Assessing and mapping landslide susceptibility using different machine learning methods
    (Taylor and Francis Ltd., 2020) Orhan, Osman; Bilgilioğlu, Süleyman Sefa; Kaya, Zehra; Özcan, Adem Kürşat; Bilgilioğlu, Hacer
    The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide incidents occur in Turkey. Firstly, a landslide inventory was produced by identifying a total of 252 landslides. Secondly, a total of 11 landslide conditioning factors were considered for the landslide susceptibility mapping. Subsequently, the five machine learning techniques were constructed with the help of the training dataset for the landslide susceptibility maps. Finally, the receiver operating characteristic (ROC), sensitivity, specificity, F-measure, accuracy and kappa index were applied to compare and validate the performance of the five machine learning techniques.
  • Küçük Resim
    Öğe
    Association of insulin-like growth factor binding protein-7 promoter methylation with esophageal cancer in peripheral blood
    (Springer Science and Business Media B.V., 2022) Kaya, Zehra; Almalı, Necat; Şahin, Elif Sena; Görgişen, Gökhan; Ateş, Can
    The insulin-like growth factor (IGF) signaling pathway has an important role in many cancers, including esophageal cancer (EC). IGF-binding protein 7 (IGFBP7) is one of the proteins in this signaling pathway, and its role in cancer has not yet been fully clarified. In the present study, we evaluated the clinical relevance of IGFBP7 methylation status and mRNA expression in EC patients compared to healthy controls. We also investigated whether IGFBP7 methylation status affects mRNA expression. Methods: The study comprised 100 EC patients and 105 healthy controls. Methylation specific PCR (MSP) was used to examine IGFBP7's promoter methylation and real-time quantitative reverse transcription PCR (qRT-PCR) was used to assess IGFBP7 mRNA expression. Results: The IGFBP7 promoter methylation was significantly higher in controls than in EC patients (p < 0.05). IGFBP7 mRNA expression was significantly lower in EC patients compared to controls, especially in those over 55 years old (p [removed] 0.05). Conclusion: Our study indicated that promoter unmethylation and mRNA expression of the IGFBP7 promoter in peripheral blood could be different biomarkers for EC. Furthermore, unmethylation of the IGFBP7 promoter in EC patients was associated with reflux and elevated globulin levels. More studies with a larger number of cases is needed to confirm this association.