Výsledky vyhledávání - "Ertekin, Seyda"

Report

CALPAGAN: Calorimetry for Particles using GANs

Autor: Simsek, Ebru, Isildak, Bora, Dogru, Anil, Reyhan, Bayrak, Aydogan Burak, Ertekin, Seyda

In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of pix2pix is tailored

Externí odkaz: http://arxiv.org/abs/2401.02248

Zobrazit plný text záznamu

Report

Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition

Autor: Büyükşahin, Ümit Çavuş, Ertekin, Şeyda

Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist which use linear and nonlinear models separately or combi

Externí odkaz: http://arxiv.org/abs/1812.11526

Zobrazit plný text záznamu

Akademický článek

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Akademický článek

Penetration rate prediction in heterogeneous formations: A geomechanical approach through machine learning

Autor: Kor, Korhan, Ertekin, Seyda, Yamanlar, Senol, Altun, Gursat

Publikováno v: In Journal of Petroleum Science and Engineering December 2021 207

Zobrazit plný text záznamu

Report

Reactive point processes: A new approach to predicting power failures in underground electrical systems

Autor: Ertekin, Şeyda, Rudin, Cynthia, McCormick, Tyler H.

Publikováno v: Annals of Applied Statistics 2015, Vol. 9, No. 1, 122-144

Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important problem within the domain of electrical grid reliability: short-term predict

Externí odkaz: http://arxiv.org/abs/1505.07661

Zobrazit plný text záznamu

Learning in extreme conditions online and active learning with massive, imbalanced and noisy data

Autor: Ertekin, Seyda. Giles, C. Lee

Thesis (Ph.D.)--Pennsylvania State University, 2009.
Mode of access: World Wide Web. Thesis advisor: C. Lee Giles.

Externí odkaz: http://etda.libraries.psu.edu/theses/approved/PSUonlyIndex/ETD-3853/index.html

Zobrazit plný text záznamu

Akademický článek

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Akademický článek

Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition

Autor: Büyükşahin, Ümit Çavuş, Ertekin, Şeyda

Publikováno v: In Neurocomputing 7 October 2019 361:151-163

Zobrazit plný text záznamu

Report

Learning to Predict the Wisdom of Crowds

Autor: Ertekin, Seyda, Hirsh, Haym, Rudin, Cynthia

The problem of "approximating the crowd" is that of estimating the crowd's majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently stretch a limited budget for a crowdsourcing task. We present an algo

Externí odkaz: http://arxiv.org/abs/1204.3611

Zobrazit plný text záznamu

Akademický článek

Time Series Prediction of Solar Power Generation Using Trend Decomposition.

Autor: Kavakci, Gurcan¹ (AUTHOR) gurcan.kavakci@metu.edu.tr, Cicekdag, Begum² (AUTHOR), Ertekin, Seyda¹ (AUTHOR)

Publikováno v: Energy Technology. Feb2024, Vol. 12 Issue 2, p1-9. 9p.

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání