Özlem, Şirin
Loading...
Profile URL
Name Variants
Özlem, Şirin
Özlem, Ş.
Sirin, Ozlem
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
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
4
QUALITY EDUCATION
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
3
GOOD HEALTH AND WELL-BEING
1
Research Products
5
GENDER EQUALITY
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
2
ZERO HUNGER
0
Research Products
1
NO POVERTY
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
14
LIFE BELOW WATER
0
Research Products
15
LIFE ON LAND
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
13
CLIMATE ACTION
0
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
Documents
11
Citations
41
h-index
3
Documents
4
Citations
34
Scholarly Output
10
Articles
2
Views / Downloads
5/2
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
18
Scopus Citation Count
25
WoS h-index
2
Scopus h-index
2
Patents
0
Projects
0
WoS Citations per Publication
1.80
Scopus Citations per Publication
2.50
Open Access Source
2
Supervised Theses
0
| Journal | Count |
|---|---|
| International Conference on Computer Science and Engineering, UBMK -- 10th International Conference on Computer Science and Engineering, UBMK 2025 -- 17 September 2025 through 21 September 2025 -- Istanbul -- 214243 | 3 |
| -- 8th International Conference on Collision and Grounding of Ships and Offshore Structures, ICCGS 2019 -- 2019-10-21 through 2019-10-23 -- Lisbon -- 236219 | 1 |
| Financial Innovation | 1 |
| International Conference on Computer Science and Engineering, UBMK | 1 |
| Journal of Navigation | 1 |
Current Page: 1 / 2
Scopus Quartile Distribution
Competency Cloud
10 results
Scholarly Output Search Results
Now showing 1 - 10 of 10
Conference Object A Predictive Model for Bounced Check Risk Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2025) Kaya K.; Sayar A.; Memis E.C.; Ozlem S.; Ertugrul S.; Cakar T.Bounced checks result in direct monetary losses. Traditional rule-based systems cannot adapt to new patterns and lack flexibility. In this study, we used a large and imbalanced check dataset with customer profiles, credit limits, and historical check outcomes. We applied feature engineering emphasizing time-based transaction patterns, extensive clustering, anomaly detection, and inflation adjustment. We trained six models each for two datasets, which are undersampled to handle class imbalance: Logistic Regression, Random Forest, XGBoost, LightGBM, Extra Trees, and CatBoost. The best performing model, CatBoost, achieved macro F1 scores of 88.5 percent on individual checks dataset with a gross sunk rate of 4.92 percent, and 91.7 percent on corporate checks dataset with a gross sunk rate of 4.28 percent. These results show the model can identify checks most likely to bounce before granting and maintain a low gross sunk rate overall. This study presents a data-driven machine learning solution that enables financial companies to predict and prevent bounced checks before they occur. © 2025 IEEE.Article Citation - Scopus: 14Grounding Probability in Narrow Waterways(Cambridge University Press, 2020) Özlem, Şirin; Altan, Yiğit Can; Otay, Emre N.; Or, İlhanThe 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.Conference Object Churn Prediction for Subscription-Based Applications Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2025) Gozukara H.; Patel J.; Kara E.; Yildiz A.; Mese Y.K.; Obali E.; Cakar T.In this study, a predictive model was developed using machine learning techniques to forecast customer churn in subscription-based video streaming services. The data such as user login records, content viewing information, subscription details, and content-related features were used to interpret usage patterns and customer churn was defined based on subscription renewal status and renewal timing. Several usage-based features are extracted for users and several algorithms were used for modeling, such as Random Forest, CatBoost, XGBoost, Logistic Regression, K-Nearest Neighbors, and Gradient Boosting. Occurring class imbalance on the target variable is handled via BorderLineSMOTE. The model's performance was evaluated using training-test accuracy plots, classification reports, and hyperparameter tuning. Even though most of the models performed similarly, the CatBoost model emerged as the top performer, achieving a macro F1-score of 0.60. However, while effective in identifying churners, the models struggled to precisely classify non-churning customers, a common challenge in imbalanced datasets even after applying oversampling techniques. The analysis of feature importance yielded a crucial insight, early and consistent user engagement is the strongest predictor of customer retention. These findings provide valuable, actionable insights for streaming platforms, emphasizing that retention strategies should focus on maximizing engagement immediately after a user subscribes. © 2025 IEEE.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 FialhoExcept 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.Article Citation - WoS: 5Citation - Scopus: 11Predicting Cash Holdings Using Supervised Machine Learning Algorithms(Springer, 2022) Özlem, Şirin; Tan, Ömer FarukThis 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.Conference Object A Multimodal AI and ML Framework for Fashion Image Segmentation, Recommendation, and Similarity Recognition(Institute of Electrical and Electronics Engineers Inc., 2025) Soyhan M.E.; Ay T.B.; Memis E.C.; Fatih Capal M.; Cakar T.; Gunay S.; Coskun H.This study presents a scalable multimodal Artificial Intelligence (AI) and Machine Learning (ML) framework designed to enhance decision making in the fashion industry. The proposed system integrates garment segmentation, multimodal feature extraction, and similarity recommendation into a unified pipeline. Using Segformer for segmentation, along with the convolutional neural network (CNN)-based feature extraction models ResNet152V2 and Xception, and the transformer-based vision-language model LLaVA, the framework generates visual and semantic embeddings of garments. These representations are processed through similarity detection using OpenAI embedding models and stored in the Pinecone vector database for efficient retrieval. Real-time similarity scoring is enabled through FastAPI endpoints, offering interactive search capabilities. Preliminary results demonstrate the system's strong ability to identify conceptually and visually similar items across a large catalog, providing actionable insights for designers. This framework lays the groundwork for intelligent, interpretable, and production-ready AI systems in the fashion industry. © 2025 IEEE.Book Part Citation - WoS: 13Quantifying 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.Conference Object Regression Analysis of Stock Exchanges During the Ramadan Period: Analysis of 16 Countries(2016) Tan, A. Serdar; Özlem S....Conference Object Quantifying the Grounding Probability in Narrow Waterways(CRC Press/Balkema, 2020) Özlem, Ş.; Altan, Y.C.; Otay, E.N.; Or İlhanThe 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.Conference Object Rag Based Interactive Chatbot for Video Streaming Services(Institute of Electrical and Electronics Engineers Inc., 2025) Gözükara H.; Patel J.; Kara E.; Yildiz A.; Köseoǧlu O.; Makaroǧlu D.; Çakar T.The proliferation of content within video streaming services presents a significant challenge for users seeking personalized recommendations and specific information. This research addresses this challenge by developing a Retrieval-Augmented Generation (RAG) chatbotn designed to enhance user experience through conversational AI. The primary contribution of this work is a novel Retrieval-Augmented Generation (RAG) architecture featuring a dual-retrieval system that combines semantic search for descriptive requests and structured queries for fact based inquiries. This approach grounds the Large Language Model (LLM) in a factual knowledge base, mitigating the risk of hallucinations. The system is engineered to handle empty data retrieval scenarios by dynamically relaxing search filters, ensuring a robust user experience. The effectiveness of this RAG approach was validated through a comprehensive set of automated evaluations. The system demonstrates high precision in ranked list retrieval with questions like "Recommend me the top 5 action movies with highest IMDb scores", achieving an average NDCG@k of 0.837. While the chatbot shows strong semantic understanding by achieving 91% accuracy with contextual clues such as "Which Batman movies are directed by Christopher Nolan?", its performance with more ambiguous, plot-only queries (59.5% accuracy) indicates clear opportunities for future refinement. These results confirm that the dual-tool architecture successfully combines the flexibility of semantic search with the precision of structured queries, paving the way for more intuitive and efficient content discovery on streaming platforms. © 2025 IEEE.
