Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    Damage Assessment of Structures Following the February 6, 2023 Kahramanmaraş Earthquakes: A Dataset-Based Case Study in Gaziantep, Türkiye
    (Springer Heidelberg, 2025) Atasever, Kurtulus; Aydogdu, Hasan Huseyin; Narlitepe, Furkan; Goksu, Caglar; Demir, Ugur; Demir, Cem; Ilki, Alper
    Following the 2023 Kahramanmara & scedil; Earthquakes (Mw 7.7 and 7.6) that struck T & uuml;rkiye on February 6, 2023, the Ministry of Environment, Urbanization, and Climate Change (MoEUCC) initiated a large-scale post-earthquake damage assessment campaign, targeting more than 2,3 million structures within the affected region. A comprehensive field survey was carried out in and around Gaziantep, one of the most severely affected cities. The authors assessed more than 1700 structures representing a wide range of occupancy types, including residential, educational, healthcare, religious, administrative, industrial, and lodging structures. In this paper, the methodological process of post-earthquake data collection in and around Gaziantep is presented, together with the data on the distribution of damage with respect to construction period, number of stories, and building occupancy type, to ensure a complete understanding of the extent and characteristics of structural damage. The damage assessment employed two data sources: (i) the data gathered through the authors' newly developed, novel damage-assessment software, presented here for the first time, and (ii) the official post-earthquake damage database of the MoEUCC. A further novelty of this study is the presentation of the largest dataset to date for the investigated earthquake doublet, encompassing approximately 1700 buildings. Additionally, the relationship between damage states, peak ground accelerations, and fault distances is thoroughly investigated. The detailed earthquake-hit site investigations revealed that the examined structures displayed structural inadequacies akin to those witnessed in previous seismic events, with a notable focus on the arrangement of the structural system, the quality of construction materials and reinforcement detailing.
  • Article
    An Alternative Software Benchmarking Dataset: Effort Estimation With Machine Learning
    (Elsevier Science Inc, 2026) Yurum, Ozan Rasit; Unlu, Huseyin; Demirors, Onur
    Effort estimation plays a vital role in software project planning, as accurate estimates of required human resources are essential for success. Traditional estimation models often depend on historical size and effort data, yet organizations frequently struggle to access reliable effort records. Public benchmarking datasets like ISBSG offer useful data but may lack coverage or involve licensing fees. To address this issue, we previously introduced a free, extendable benchmarking dataset that integrates functional size and effort data extracted from 18 studies. In this study, we examine the effectiveness of our dataset for predictive effort estimation and compare it with the widely used ISBSG dataset. Our analysis includes 337 records from our dataset and 732 ISBSG projects, focusing on those with COSMIC size data. We first developed and compared models using linear regression and nine machine learning algorithms - Bayesian Ridge, Ridge Regression, Decision Tree, Random Forest, XGBoost, LightGBM, k-Nearest Neighbors, Multi-Layer Perceptron, and Support Vector Regression. Then, we selected the best-performing models and applied them to an unseen evaluation dataset to assess their generalization performance. The results show that machine learning performance varies based on evaluation method and dataset characteristics. Despite having fewer records, our dataset enabled more accurate predictions than ISBSG in most cases, highlighting its potential for effort estimation. This study demonstrates the viability of our dataset for building predictive models and supports the use of machine learning in improving estimation accuracy. Expanding this dataset could offer a valuable, open-access resource for organizations seeking effective and lowcost estimation solutions.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Towards the Construction of a Software Benchmarking Dataset Via Systematic Literature Review
    (IEEE, 2024) Yurum, Ozan Rasit; Unlu, Huseyin; Demirors, Onur
    Effort estimation is a fundamental task during the planning of software projects. Prediction models usually rely on two essential factors: software size and effort data. Measuring the size of the software can be done at various stages of the project with desired accuracy. Nevertheless, the industry faces challenges when it comes to collecting reliable actual effort data. Consequently, organizations encounter difficulties in establishing effort prediction models. Benchmarking datasets are available, but, in most cases, they have huge variances that make them less useful for effort prediction. In this study, we aimed to answer whether creating a software benchmarking dataset is possible by gathering the data from the literature. To the best of our knowledge, a comprehensive dataset that gathers the functional size and effort data of the studies from the literature is unavailable. For this purpose, we performed a systematic literature review to find studies that include projects measured with the COSMIC Functional Size Measurement (FSM) method and the related effort. As a result, we formed a dataset including 337 records from 18 studies that shared the corresponding size and effort data. Although we performed a limited search, we created a larger dataset than many datasets in the literature. In light of our review, we obtained that most studies did not share their dataset, and many lacked case details such as implementation environment and the scope of software development life cycle activities included in the effort data. We also compared the dataset with the ISBSG repository and found that our dataset has less variation in productivity. Our review showed the applicability of creating a software benchmarking dataset is possible by gathering the data from the literature. In conclusion, this study addresses gaps in the literature through a cost-free and easily extendable dataset.
  • Conference Object
    Doğruluk Problemi için Veri Kümesi Hazırlanması
    (CEUR Workshop Proceedings, 2018) Karabayır, Arif Kürşat; Tek, Ozan Onur; Çınar, Özgür Fırat; Tekir, Selma
    Internet has become one of the most important information sources. With the advent of Internet, the ease of access and sharing of information have caused the emergence of conflicting information. The increase in conflicting information makes it a challenge to find the truth out of it. This problem is named as the veracity problem. The algorithms that were developed in response to this problem accept structured data as in¬ put. Thus, to be able to use these algorithms on Internet, there is a need to transform the unstructured data on the Internet into a structured form. This need is hard to fulfill in a domain-independent and automatic way considering the variety on Internet. In this work; structured data preparation to test the effectiveness of the truth-finder algorithms is experienced. The process of transforming the unstructured data on the Internet into a structured form is described in steps to contribute its generalization in a domain-independent way. As a result of this process, a new quotes data set is constructed and a truth-finder algorithm is tested on this dataset by giving some comments on it.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 2
    Türkçe Manzara Metni Veri Kümesi
    (IEEE, 2017) Erdogmus, Nesli
    Scene text localization and recognition keeps attracting an increasing interest from researchers due to its valuable advantage in extracting content from real world images and in image retrieval via text search. Nevertheless, due to the fact that the majority of the image datasets that are commonly used in this field is comprised of text in English, the related studies have mostly been limited to a single language. On that account, in order to apply the technologies developed for scene text detection and recognition to Turkish scene text, analyze their performances and to develop Turkish language specific algorithms, a Turkish scene text database is collected for the first time in the literature. In this paper, the contents of this database, shortly called STRIT (Scene Text Recognition In Turkish), are detailed. Additionally, two baseline methods are tested to detect and recognize scene text in Turkish and the preliminary results are presented.