Browsing by Author "Kaya, Burak Erkan"

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Citation Count: 0
2009 A (H1N1) ve COVID-19 Pandemilerinde Nüfus Yoğunluğunun ve Temas Oranının Rolü
(2024) Bilge, Ayşe Hümeyra; Ahmetolan, Semra; Bilge, Ayşe Hümeyra; Demirci, Ali; Kaya, Burak Erkan
Sağlıklı-Virus bulaşmış-Bulaşıcılığı olmayan (SIR) salgın modelinin başlıca özellikleri, temel üreme sayısı olarak bilinen 𝑅0 parametresi tarafından belirlenir. Bu çalışmada, çeşitli Avrupa ülkeleri ve İstanbul'daki 2009 A(H1N1) pandemisi ile Almanya'nın federal eyaletlerindeki Covid-19 pandemisi olmak üzere iki farklı salgın için, 𝑅0'ın temas oranlarına olan bağımlılığı araştırılmıştır. 2009 A(H1N1) pandemisine ait veriler, Hollanda da dahil olmak üzere yedi Avrupa ülkesi ve İstanbul için ele alınmış olup, bu ülkeler için temel üreme sayısının nüfus yoğunluğuna orantılı olduğu gösterilmiştir. Yüksek nüfus yoğunluklarına sahip olmaları nedeniyle Hollanda ve İstanbul’a ait 𝑅0 değerlerinin, literatürde kabul edilen aralıkların oldukça dışında kaldığı gözlemlenmiştir. Covid-19 pandemisi için 2020 yılının Şubat ve Haziran ayları arasındaki döneme ait Almanya federal eyaletlerinin verileri kullanılarak, toplumdaki heterojenliklerin nüfus yoğunluğunun etkilerini domine ettiği gösterilmiştir. Bu durum, sokağa çıkma yasağı ve seyahat kısıtlamaları gibi uygulamaların ev içi dinamiklerinin rolünü arttırması olasılığı ile açıklanmıştır.
Citation Count: 31
Evaluation of the COVID-19 Pandemic Intervention Strategies with Hesitant F-AHP
(Hindawi, 2020) Samanlıoğlu, Funda; Kaya, Burak Erkan
In this study, a hesitant fuzzy AHP method is presented to help decision makers (DMs), especially policymakers, governors, and physicians, evaluate the importance of intervention strategy alternatives applied by various countries for the COVID-19 pandemic. In this research, a hesitant fuzzy multicriteria decision making (MCDM) method, hesitant fuzzy Analytic Hierarchy Process (hesitant F-AHP), is implemented to make pairwise comparison of COVID-19 country-level intervention strategies applied by various countries and determine relative importance scores. An illustrative study is presented where fifteen intervention strategies applied by various countries in the world during the COVID-19 pandemic are evaluated by seven physicians (a professor of infectious diseases and clinical microbiology, an infectious disease physician, a clinical microbiology physician, two internal medicine physicians, an anesthesiology and reanimation physician, and a family physician) in Turkey who act as DMs in the process.
Citation Count: 0
Evaluation of Various Machine Learning Methods to Predict Istanbul’s Freshwater Consumption
(2023) Hekimoğlu, Mustafa; Çetin, Ayse Irem; Kaya, Burak Erkan
Planning, organizing, and managing water resources is crucial for urban areas and metropolitans. Istanbul is one of the largest megacities, with a population of over 15 million. The large volume of water demand and increasing scarcity of clean water resources make long-term planning necessary for this city, as sustained water supply requires large-scale investment projects. Successful investment plans require accurate projections and forecasting for freshwater demand. This study considers different machine learning methods for freshwater demand forecasting for Istanbul. Using monthly consumption data provided by the municipality since 2009, we compare forecasting accuracies of ARIMA, Holt-Winters, Artificial Neural Networks, Recursive Neural Networks, Long-Short Term Memory, and Simple Recurrent Neural Network models. We find that the monthly freshwater demand of Istanbul is best predicted by Multi-Layer Perceptron and Seasonal ARIMA. From the predictive modeling perspective, this result is another indication of the combined usage of conventional forecasting models and novel machine learning techniques to achieve the highest forecasting accuracy.
Citation Count: 2
Optimum utilization of on-demand manufacturing and laser polishing in existence of supply disruption risk
(Elsevier, 2022) Ulutan, Durul; Hekimoğlu, Mustafa; Kaya, Burak Erkan; Hekimoglu, Mustafa
3D printing has moved from being a rapid prototyping tool to an additive manufacturing method within the last decade. Additive manufacturing can satisfy the need in dire situations where spare parts distribution is an issue but access to a 3D printer is much more likely and rapid than access to original parts. Managing inventories of spare parts can be tackled with more ease thanks to the reduced part types with additive manufacturing. While quality (in terms of reliability) of additively manufactured spare parts in terms of mechanical properties seem to be lower than original parts (particularly due to the inherent staircase appearance and the corresponding stress concentration zones that can lead to premature fatigue failure), use of post-processing subtractive techniques to correct such surface irregularities are found to improve reliability. While each process adds another layer of complexity to the cost minimization problem, demand uncertainty and risk of supply disruption represent the modern global problems faced recently. The problem tackled in this study is the joint optimization of the supply reliability considering the effect of laser polishing parameters and the demand uncertainty. In this problem, a condition of random breakdowns of identical products is considered. Also, the original supplier of machine components is subject to exogenous disruptions, such as strikes, raw material scarcity, or the COVID-19 pandemic. As a result, the optimum control policy with the right cost parameters was shown via numerical experiments originated from mathematical analyses. This optimality can be critical in managing the system in the best possible way, particularly during times of unforeseen circumstances such as pandemics. (C) 2022 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.