11 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 10 / 11
Yayın Spatial-Temporary analysis of Istanbul air pollution during the pandemic using Google Earth Engine and Google community mobility reports(Gök, Murat, 2023-06-30) Çavdaroğlu, Gülsüm Çiğdem; Arık, Ahmet OkanThe Covid-19 pandemic has brought drastic changes to people's daily life and environmental characteristics. To control the pandemic, all governments have implemented particular policies for their countries and imposed restrictions that affect people's daily life. The traffic index has decreased in many countries and cities depending on the restrictions. Therefore, restrictions in many countries and cities have positively impacted air quality. However, the opposite has also been observed in metropolitan cities. In this study, the change in the air quality of Istanbul, which is accepted as Turkey's largest metropolitan city, has been examined. First, the spatio-temporal distribution of air pollutants (NO2, CO, and SO2) has been analyzed using Sentinel-5P NRTI satellite images. Then six independent variable groups (traffic index of Istanbul, daily deaths in Istanbul, Google community mobility reports of Istanbul, fuel prices, stringency index of Turkey, two logical attributes regarding the Covid-19 restrictions and in-class education) were collected and combined to analyze the correlations between these variable groups and air pollutant concentrations. According to the spatial distribution graphs, there is a tendency to decrease NO2, CO, and SO2 pollutant concentrations in Istanbul when the restrictions are applied in Turkey. There was no significant relationship between the decrease in community mobility in Istanbul and pollutant concentrations, although an increase in air quality has been observed in many cities due to the restrictions of the Covid-19 pandemic.Yayın An intrusion detection approach based on the combination of oversampling and undersampling algorithms(Istanbul University Press, 2023-06-14) Arık, Ahmet Okan; Çavdaroğlu, Gülsüm ÇiğdemThe threat of network intrusion has become much more severe due to the increasing network flow. Therefore, network intrusion detection is one of the most concerned areas of network security. As demand for cybersecurity assurance increases, the requirement for intrusion detection systems to meet current threats is also growing. However, network-based intrusion detection systems have several shortcomings due to the structure of the systems, the nature of the network data, and uncertainty related to future data. The imbalanced class problem is also crucial since it significantly negatively affects classification performance. Although high performance has been achieved in deep learning-based methodologies in recent years, machine learning techniques may also provide high performance in network intrusion detection. This study suggests a new intrusion detection system called ROGONG-IDS (Robust Gradient Boosting – Intrusion Detection System) which has a unique two-stage resampling model to solve the imbalanced class problem that produces high accuracy on the UNSW-NB15 dataset using machine learning techniques. ROGONGIDS is based on gradient boosting. The system uses Synthetic Minority Over-Sampling Technique (SMOTE) and NearMiss-1 methods to handle the imbalanced class problem. The proposed model's performance on multi-class classification was tested with the UNSW-NB15, and then its robust structure was validated with the NSL-KDD dataset. ROGONG-IDS reached the highest attack detection rate and F1 score in the literature, with a 97.30% detection rate and 97.65% F1 score using the UNSW-NB15 dataset. ROGONG-IDS provides a robust, efficient intrusion detection system for the UNSW-NB15 dataset, which suffered from imbalanced class distribution. The proposed methodology outperforms state-of-the-art and intrusion detection methods.Yayın Correlation analysis between the community mobility and nighttime lights in the city of Istanbul, Turkey(2022) Çavdaroğlu, Gülsüm ÇiğdemThe COVID-19, which emerged in Wuhan, China, in 2019, has significantly affected people’s Daily lives, business environment, surrounding environment, and countries' economic and social conditions. This study aims to measure the correlation between community mobility changes in six different areas and nighttime lights in the city of Istanbul, Turkey. Nighttime light data used in the study was obtained from VIIRS Nighttime Day/Night Band Composites Version 1 using remote sensing methods via Google Earth Engine platform. Then the correlation between Nighttime light values and community mobility values was investigated. It has been observed that the correlation values have changed dramatically over the years. The most significant correlation values were observed for the year 2020. This is because 2020 is the year when the pandemic is most effective, and restrictions are at the highest level in Turkey. The increase in freedom in the following years caused a decrease in the correlation. When the correlation results covering the period of February 2020 - to January 2022 were examined, it was observed that there was no significant relationship between nighttime light values and Google Community Mobility Reports’ variables. Considering the correlation results for 2020, it was observed that there was a high negative correlation between nighttime light data and mobility trends for grocery and pharmacy, and mobility trends for places of work. In addition, there was a moderate negative correlation between nighttime light data and mobility trends for retail and recreation, and a moderate positive correlation between nighttime light and mobility trends for places of residence. When the correlation values of 2021 and the correlation values of the period 2021-2022 were examined, no significant relationship was observed.Yayın Istanbul’s community mobility changes during the COVID-19 pandemic: a spatial analysis(Istanbul University Press, 2023-08-15) Arık, Ahmet Okan; Çavdaroğlu, Gülsüm ÇiğdemCOVID-19 was the most recent pandemic to strike humanity. Moreover, this pandemic occurred during the most active period of global interaction and mobility, unlike pandemics like cholera, plague, and flu in earlier centuries. Many countries restricted domestic mobility after suspending international mobility to prevent the pandemic from spreading. Although these policies differ from nation to nation, they have affected the mobility of communities. This study examined spatial and non-spatial independent variables that affected how the community’s mobility patterns changed in various locations, including parks, transit stations, workplaces, grocery and pharmacies, and residential areas in Istanbul, Türkiye. The impact of the independent spatial variables on the mobility changes was examined after identifying the non-spatial independent variables influencing the mobility changes in 6 different areas. It was determined that the altitude variable, expected to impact how mobility changed, had no overall impact on the dependent variable. On the other hand, the dependent variables representing the mobility changes were affected by the independent variables representing the county center’s latitude and longitude values and whether the county is located near the sea. Regression analysis across Türkiye will be performed in upcoming studies using an updated version of the methodology used in this study.Yayın A geospatial analysis of the parks, emergency assembly areas, and urban green spaces in Izmir districts(IKSAD Publications, 2024-12-30) Çavdaroğlu, Gülsüm Çiğdem; Günay, NazanEnsuring equity in the allocation of public resources is a central objective for planners. In the context of planning, equitable distribution involves strategically placing resources or facilities to maximize accessibility for a diverse range of spatially distinct social groups. Equity in resource distribution has been a focal point of interest across numerous disciplines. The equity mapping method, which utilizes visualization techniques within geographic information systems (GIS), serves as a valuable tool for analyzing the spatial equity in the distribution of public resources. In this study, equity mapping was applied to parks, green spaces, and emergency assembly areas - resources of significant societal importance - to evaluate individual accessibility to these public facilities. The fundamental methodological approach to equity mapping involves overlaying the distribution of accessibility measures with socioeconomic data to analyze spatial variations in equity. This approach relies on spatial univariate, bivariate, or multivariate analysis, which examines the mapped data distributions and spatial patterns to identify and characterize spatial associations. The study answers eight research questions: (1) the number of emergency assembly areas per capita by district, (2) the number of children's playgrounds per capita among the population aged 0-19 by district, (3) the number of fitness areas per capita among the population aged 20 and above by district, (4) the amount of urban green space per capita among the entire population by district, (5) the distance of the nearest emergency assembly area to the district center, (6) the distance of the nearest emergency assembly area to the neighborhood center, (7) the number of parks within reach of the neighborhood center, (8) number of parks within 1 km of buildings on a district basis. Obtained fundamental patterns of inequity in the distribution of focused public resources in the study may help the municipalities better understand the current situation, make plans for the following years and ensure a more equitable distribution of public resources.Yayın Google Earth engine based approach for finding fire locations and burned areas in Muğla, Turkey(Science Publishing Group, 2021-10-05) Çavdaroğlu, Gülsüm ÇiğdemForests are considered as one of the main sources of biodiversity. Forest fires caused by various reasons pose a high risk in terms of biodiversity. Therefore, mapping of fire zones is of great importance in determining the damage caused by the fire, managing the fire process, and planning the interventions in the fire zone. Although remote sensing is a fast and cost-effective methodology for mapping fire zones, the implementation of the remote sensing methodologies is problematic in some respects. The web-based Google Earth Engine makes possible to access the satellite imagery and process the imagery easily. The research area of this study is Muğla, Turkey in where many forest fires broke out in 2021 summer. This study provides an implementation of normalized burn ratio which is widely used to highlight burned areas on Google Earth Engine platform. Both vector data and satellite images were used in the study. The vector data is in the shape file format and was uploaded to the Google Earth Engine platform as a table. The Sentinel-2 imagery was used to calculate normalized burn ratio. The satellite imagery was clipped using the table data. The difference pre-fire and post-fire images was calculated, and the classes were assigned to the pixels according to the normalized burn ratio ranges. The study indicates that finding the burned areas and constructing the burn severity levels can be realized in 1.32 minutes on Google Earth Engine platform.Yayın A character segmentation method to increase character recognition accuracy for Turkish license plates(Science Publishing Group, 2021-12-31) Çavdaroğlu, Gülsüm Çiğdem; Gökmen, MehmetAutomatic License Plate Recognition is a computer vision technology that provides a way to recognize the vehicle's license plates without direct human intervention. Developing Automatic License Plate Recognition methodologies is a widely studied topic among the computer vision community to increase the accuracy rates. Automatic License Plate Recognition systems include image acquisition and character segmentation phases. Although there are many studies, the research in character segmentation and improving recognition accuracy remains limited. The lack of an international standard for license plates and the misinterpretation of ambiguous characters are challenging problems for Automatic License Plate Recognition systems. Several academic works have shown that the ambiguous character problem can be overcome by using a second model that contains only these characters. In this study, we propose a new methodology to reduce the character recognition errors of Automatic License Plate Recognition systems. One of the reasons for the low accuracy rates is the problem of ambiguous characters. In most studies using OCR, it was observed that a single model was used for alphanumeric characters during the recognition phase. Instead of using a single model, using separate models for letters and digits will improve the recognition process and increase accuracy. Therefore, we determined whether the characters are letters or numbers, and we expressed the license plates in the form of letters - digits. The method suggested for segmenting blobs worked with an accuracy of 96.12% on the test dataset. The method recommended for generating letter-digit expressions for the license plates worked with an accuracy of 99.28% on the test dataset. The proposed methodology can work only on Turkish license plates. In future studies, we will expand our method by using the license plate dataset of a different country.Yayın An analysis of the effects of external factors on Covid-19 projections(ICONSOS Publishing House, 2021-05-10) Çavdaroğlu, Gülsüm Çiğdem; Nuhui, Agim; Yılmaz, Baha Ahmet[No abstract available]Yayın Topluluk Öğrenmesi(Nobel Akademik Yayıncılık, 2024-10) Çavdaroğlu, Gülsüm Çiğdem; Gök, Murat[No abstract available]Yayın İnsansız hava aracı ve Sentinel-2 görüntüleri kullanılarak ayçiçeği haritalamasına dayalı kovan yerleştirme karar destek sistemi(BZT Turan Publishing House, 2025-12-31) Yelmenoğlu, Elif Deniz; Aydın, Şahin; Çavdaroğlu, Gülsüm Çiğdem; Deniz, Hüseyin; Pajenado, Rex S.; Dilli, ŞirinAyçiçeği, yüksek nektar üretim kapasitesi nedeniyle gezici arıcılık faaliyetleri açısından stratejik öneme sahip tarımsal bitkilerden biridir. Ayçiçeği ekim alanlarının mekânsal ve zamansal dağılımı, arı kolonilerinin beslenme olanaklarını ve dolayısıyla bal verimini doğrudan etkilemektedir. Bu nedenle, arı kovanlarının uygun alanlara ve doğru zaman dilimlerinde yerleştirilmesi, gezici arıcılığın verimliliği açısından kritik bir karar sürecini oluşturmaktadır. Ancak mevcut uygulamalarda, kovan yer seçimi çoğunlukla arıcıların bireysel deneyimlerine ve sezgisel yaklaşımlarına dayalı olarak gerçekleştirilmekte; uzaktan algılama, görüntü işleme ve mekânsal analiz gibi veri temelli yöntemlerden yeterince yararlanılmamaktadır. Bu durum, potansiyel olarak verim kayıplarına ve kaynakların etkin kullanılmamasına yol açabilmektedir. Bu çalışmada, ayçiçeği yoğunluğunun doğru ve güvenilir biçimde belirlenmesi yoluyla kovan yerleştirme planlamasını desteklemeyi amaçlayan, çok ölçekli bir uzaktan algılama tabanlı karar destek çerçevesi önerilmektedir. Önerilen yaklaşım, saha ölçeğinde yüksek mekânsal çözünürlük sağlayan insansız hava aracı (İHA) görüntüleri ile bölgesel ölçekte geniş alan kapsama imkânı sunan Sentinel-2 uydu görüntülerinin entegrasyonuna dayanmaktadır. Çalışma alanı olarak, Türkiye’nin önemli ayçiçeği üretim merkezlerinden biri olan Kırklareli ili seçilmiş; veri seti, nektar üretiminin en yüksek olduğu ayçiçeği çiçeklenme dönemi dikkate alınarak oluşturulmuştur. Ayçiçeği tespiti, makine öğrenmesi tabanlı Random Forest sınıflandırma yöntemi kullanılarak gerçekleştirilmiş ve geliştirilen model %90,7 genel doğruluk değerine ulaşmıştır. Sınıf bazlı performans değerlendirmelerinde ise, ayçiçeği ekili alanlar ile ayçiçeği olmayan alanlar için F1-skoru her iki sınıf açısından da 0,91 olarak hesaplanmıştır. Bu sonuçlar, modelin hem nektar açısından zengin ayçiçeği alanlarını hem de ayçiçeği bulunmayan bölgeleri güçlü ve dengeli bir şekilde ayırt edebildiğini göstermektedir. Elde edilen ayçiçeği yoğunluk haritaları temel alınarak, ayçiçeği oranının yüksek olduğu alanlar arı kovanı yerleştirilmesi için uygun bölgeler olarak tanımlanmış; ayçiçeği yoğunluğunun düşük olduğu veya hiç bulunmadığı alanlar ise kovan yerleştirilmesine uygun olmayan bölgeler olarak değerlendirilmiştir. Çalışmadan elde edilen bulgular, çok ölçekli uzaktan algılama verilerinin makine öğrenmesi yöntemleriyle bütünleştirilmesinin, gezici arıcılık uygulamalarında veri temelli, güvenilir ve ölçeklenebilir karar destek sistemlerinin geliştirilmesine önemli katkılar sağlayabileceğini ortaya koymaktadır.
