Özlem, Şirin

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Profile URL
https://hdl.handle.net/20.500.11779/2660
Name Variants
Özlem, Şirin
Özlem, Ş.
Sirin, Ozlem
Job Title
Email Address
ozlems@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
Website
ORCID ID
0000-0001-7248-1825
Scopus Author ID
55532291700
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
PDW-2628-2025
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Şirin Özlem.jpg (152.43 KB)

Sustainable Development Goals

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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6

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40

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Scholarly Output

6

Articles

2

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1103/2150

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0

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0

WoS Citation Count

18

Scopus Citation Count

24

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2

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2

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0

WoS Citations per Publication

3.00

Scopus Citations per Publication

4.00

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2

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2015 World Conference on Technology, Innovation and Entrepreneurship1
-- 8th International Conference on Collision and Grounding of Ships and Offshore Structures, ICCGS 2019 -- 2019-10-21 through 2019-10-23 -- Lisbon -- 2362191
Financial Innovation1
Journal of Navigation1
Proceedings in Marine Technology and Ocean Engineering1
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Now showing 1 - 6 of 6
  • Thumbnail Image
    Article
    Citation - Scopus: 14
    Grounding Probability in Narrow Waterways
    (Cambridge University Press, 2020) Özlem, Şirin; Altan, Yiğit Can; Otay, Emre N.; Or, İlhan
    The Strait of Istanbul is one of the world's busiest, narrowest and most winding waterways. As such, there is a high grounding probability for vessels. Although a number of grounding probability models exist, they have been deemed unsuitable by local maritime experts, due to their insufficient stopping distance criteria for narrow waterways. Thus, there is a need for a new model. This paper proposes a two-component grounding probability model that multiplies the geometric grounding probability (calculated with a kinematic-based model) with the causation probability (calculated with a specially designed Bayesian network). The geometric probability model is improved in terms of stopping distance parameters and the Bayesian network is crafted for narrow waterways. The model is then deployed with pre-determined parameters within the Strait of Istanbul to run risk analysis scenarios. The results, validated with actual grounding records, show that the causation probability is the key component for quantifying the probability of grounding in narrow waterways. If navigated without frequent evasive manoeuvres, grounding would be almost inevitable. Although this study focuses on the Strait of Istanbul, the proposed approach can be applied to research into grounding probability of vessels navigating through other waterways. Copyright © The Royal Institute of Navigation 2019.
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    Conference Object
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    Conference Object
    Quantifying the Grounding Probability in Narrow Waterways
    (CRC Press/Balkema, 2020) Özlem, Ş.; Altan, Y.C.; Otay, E.N.; Or İlhan
    The aim of this paper is to estimate the grounding probability of vessels while navigating in narrow waterways. In this study, the grounding probability is modelled as a combination the geometric probability, defined as vessel being on a grounding course and the causation probability, defined as the probability that the vessel is unable to avoid a grounding while being on a grounding course. A mathematical model is developed to estimate the geometric probability where the causation probability is estimated through a specially designed Bayesian network. The Strait of Istanbul, one of the narrowest waterways in the world, is used as a test case. The resulting grounding and ramming accidents are 2.8 times the ship collisions. The most critical causes of grounding accidents are the machine failure, steering inadequacy and lack of pilot support, respectively. With different input parameters, the proposed approach may be applied to other narrow waterways. © 2020 Taylor and Francis Group, London.
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    Conference Object
    Improved Business Model Representation of Innovation Concepts
    (World Conference on Technology, Innovation and Entrepreneurship, 2015) Dorantes-Gonzalez, Dante Jorge; Küçükaydın, Hande; Özlem, Şirin; Bulgan, Gökçe; Aydın, Utkun; Son Turan, Semen; Karamollaoğlu, Nazlı; Teixeira, Frederico Fialho
    Except for academics and consultants, the concept and purpose of innovation (not to mention related concepts such as “Open Innovation", "Free-Intellectual Property Innovation," or "Open Source Innovation") is usually unclear for most entrepreneurs and other practitioners. It often times happens that newly coined terminology becomes misleading or may even include a certain degree of sensationalism, hence turning into a matter of debate for specialists in the realm of technology management. Such has been the case for the term “Open Innovation”, since the word “open” is mainly related to crowd sourced innovation, but not for the openness on intellectual property rights. Since innovation is about the commercialization of original ideas, so we propose a simple and visual business model setting to resolve these concepts. To this regard, the “Business Model Canvas” has been used in business and entrepreneurship to sketch and frame the key points behind the development of a startup. This model was suggested by Alexander Osterwalder (2008) in his work on Business Model Ontology, as a strategic analysis template for developing startups or documenting existing businesses. It describes the firm’s value proposition, partners, resources, activities, customer relationships, distribution channels, customers, revenue streams and cost structure. However, when it comes to innovative startups, this template does not explicitly include such significant innovation components as intellectual property, its alignment to strategies, and intellectual property flow. The present paper proposes the use of an improved version of the Business Model Canvas to originally represent different models of innovation like Open Innovation, thus providing a clear, visual and quick representation of their meaning, and consequently, contribute to a better understanding of different concepts of innovation.
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    Article
    Citation - WoS: 5
    Citation - Scopus: 10
    Predicting Cash Holdings Using Supervised Machine Learning Algorithms
    (Springer, 2022) Özlem, Şirin; Tan, Ömer Faruk
    This study predicts the cash holdings policy of Turkish firms, given the 20 selected features with machine learning algorithm methods. 211 listed firms in the Borsa Istanbul are analyzed over the period between 2006 and 2019. Multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), decision trees (DT), extreme gradient boosting algorithm (XGBoost) and multi-layer neural networks (MLNN) are used for prediction. Results reveal that MLR, KNN, and SVR provide high root mean square error (RMSE) and low R2 values. Meanwhile, more complex algorithms, such as DT and especially XGBoost, derive higher accuracy with a 0.73 R2 value. Therefore, using advanced machine learning algorithms, we may predict cash holdings considerably.
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    Book Part
    Citation - WoS: 13
    Quantifying the Grounding Probability in Narrow Waterways
    (CRC Press, 2020) Özlem, Ş.; Altan, Y.C.; Otay, E.N.; Or, I.
    The aim of this paper is to estimate the grounding probability of vessels while navigating in narrowwaterways. In this study, the grounding probability is modelled as a combination the geometric probability, defined as vessel being on a grounding course and the causation probability, defined as the probability that the vessel is unable to avoid a grounding while being on a grounding course. A mathematical model is developed to estimate the geometric probability where the causation probability is estimated through a specially designed Bayesian network. The Strait of Istanbul, one of the narrowest waterways in the world, is used as a test case. The resulting grounding and ramming accidents are 2.8 times the ship collisions. The most critical causes of grounding accidents are the machine failure, steering inadequacy and lack of pilot support, respectively. With different input parameters, the proposed approach may be applied to other narrow waterways. © 2020 Taylor and Francis Group, London.