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publications
Text Analytics
Published in Business Analytics for Professionals, 2022
Book chapter for text analysis and natural language processing techniques.
Recommended citation: Sivri, M.S., Korkmaz, B.S. (2022). "Text Analytics." In: Ustundag, A., Cevikcan, E., Beyca, O.F. (eds) Business Analytics for Professionals. Springer Series in Advanced Manufacturing. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-93823-9_7
Data Driven Modelling of Centrifugal Compressor Maps for Control and Optimization Applications
Published in European Control Conference 2022 (ECC)., 2022
We apply Gaussian process regression to the problem of centrifugal compressor performance modelling using hyperparameter optimisation.
Recommended citation: B. S. Korkmaz and M. Mercangöz, (2022). "Data Driven Modelling of Centrifugal Compressor Maps for Control and Optimization Applications." 2022 European Control Conference (ECC), London, United Kingdom, 2022, pp. 2260-2265. https://ieeexplore.ieee.org/abstract/document/9838008
From Statistical to Deep Learning Models: A Comparative Sentiment Analysis Over Commodity News
Published in Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference, 2022
In this paper, several natural language processing techniques with a varying range from statistical methods to deep learning-based methods were applied on the commodity news. Firstly, the dictionary-based methods were investigated with the most common dictionaries in financial sentiment analysis such as Loughran & McDonald and Harvard dictionaries. Then, statistical models have been applied to the commodity news with count vectorizer and TF-IDF. The compression-based NCD has been also included to test on the labeled data. To improve the results of the sentiment extraction, the news data was processed by deep learning-based state-of-art models such as ULMFit, Flair, Word2Vec, XLNet, and BERT.
Recommended citation: M. S. Sivri, B. S. Korkmaz, A. Ustundag, (2022). "From Statistical to Deep Learning Models: A Comparative Sentiment Analysis Over Commodity News. " Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference, held August 24-26, 2021. Volume 2. https://link.springer.com/chapter/10.1007/978-3-030-85577-2_18
Ensemble Learning Based Stock Market Prediction Enhanced with Sentiment Analysis
Published in Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference, 2022
Our study consists of weekly predictions by ensemble learning and feature selection methods using 683 variables for stocks traded in the Borsa Istanbul 30 index. In addition, we predicted sentiment scores from news of 18 different sectors and combined both predictions with weighted normalized returns. With the proposed trade system, we combined the results obtained from these financial variables and the news sentiment scores. Final results show that we achieved a better performance than both predictions made by using sentiment scores and financial data in terms of weekly return and accuracy.
Recommended citation: M. S. Sivri, A.Ustundag, B. S. Korkmaz. (2022). "Ensemble Learning Based Stock Market Prediction Enhanced with Sentiment Analysis." Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference, held August 24-26, 2021. Volume 2. https://link.springer.com/chapter/10.1007/978-3-030-85577-2_53
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems:The ARTEO Algorithm
Published in arXiV.org. Under review for ICML 2023., 2022
We consider the problem of decision-making under uncertainty in an environment with safety constraints. We propose the ARTEO algorithm, where we cast multiarmed bandits as a mathematical programming problem subject to safety constraints and learn the unknown characteristics through exploration while optimizing the targets. We quantify the uncertainty in unknown characteristics by using Gaussian processes and incorporate it into the cost function as a contribution which drives exploration. We adaptively control the size of this contribution in accordance with the requirements of the environment. We guarantee the safety of our algorithm with a high probability through confidence bounds constructed under the regularity assumptions of Gaussian processes.
Recommended citation: B.S. Korkmaz, M. Zagorowska, M. Mercangoz, (2022). "Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm." https://arxiv.org/abs/2211.05495. https://arxiv.org/abs/2211.05495
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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