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Debating the dual citizenship – integration nexus in Turkey
(Uluslararası İlişkiler Konseyi Derneği İktisadi İşletmesi, 2019)This article explores the institution of dual citizenship outside of the West and focuses on Turkey to assess the possible relationship between dual citizenship and the integration of migrants, drawing on Kymlicka and ... -
Debating voter defection in Turkey
(Taylor & Francis, 2023-10-20)This study examines patterns of voter defection from Turkey’s incumbent AKP amid major economic and democratic decline. As in other electoral autocracies, defectors constitute a small but politically significant group in ... -
A decentralized token-based negotiation approach for multi-agent path finding
(Springer, 2021)This paper introduces a negotiation approach to solve the Multi-Agent Path Finding problem. The approach aims to achieve a good trade-off between the privacy of the agents and the effectiveness of solutions. Accordingly, ... -
Deception and violence in the ottoman empire: the people's theory of crowd behavior during the hamidian massacres of 1895
(Cambridge University Press, 2020-10)This article is an historical ethnography of the popular conceptualizations of crowd behavior during the pogroms against the Armenians in the Ottoman East in 1895-1896. It draws on contemporary sources like official ... -
Decision model and application of electric vehicle charger installation to distribution transformers
(IEEE, 2022)It is very evident that the number of electric vehicles worldwide will increase and that the dominant mobility concept in the future will be at the center of EVs. As a requirement of e-mobility, EVs should be rechargeable ... -
Decision rule bounds for two-stage stochastic bilevel programs
(Society for Industrial and Applied Mathematics Publications, 2018)We study two-stage stochastic bilevel programs where the leader chooses a binary here-and-now decision and the follower responds with a continuous wait-and-see decision. Using modern decision rule approximations, we construct ... -
Decomposing time series data via mixed integer programming
(2020-01-13)Decomposing time series into seasonality, trend, and remainder reveals underlying insights to be used in forecasting and anomaly detection. Although there are several decomposition methods, no method guarantees all of the ... -
Decomposing transverse momentum balance contributions for quenched jets in PbPb collisions at √=2.76sNN=2.76 TeV
(Springer International Publishing, 2016)Interactions between jets and the quark-gluon plasma produced in heavy ion collisions are studied via the angular distributions of summed charged-particle transverse momenta (pT) with respect to both the leading and ... -
A decomposition based metaheuristic approach for solving rapid needs assessment routing problem
(Elsevier, 2021-09)This paper proposes a decomposition based tabu search algorithm for solving multi-cover routing problem in the case of rapid need assessment. Rapid needs assessment aims to evaluate impact of a disaster at different sites ... -
A decomposition-based heuristic for a waste cooking oil collection problem
(Springer, 2020-01-01)Every year, a tremendous amount of waste cooking oil (WCO) is produced by households and commercial organizations, which poses a serious threat to the environment if disposed improperly. While businesses such as hotels and ... -
A decomposition-based metaheuristic approach for solving the rapid needs assessment routing problem
(2021-01-18)This study proposes a decomposition-based tabu search algorithm for a multi-cover routing problem (MCRP), which aims to classify and evaluate the impacts of the disaster in different sites and the needs of different community ... -
Deep learning based event recognition in aerial imagery
(IEEE, 2023)In this paper, we investigate event recognition for aerial surveillance. This is a significant task especially when we consider the growing popularity of UAVs. The main purpose of the paper is to detect events both at the ... -
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimation
(Elsevier, 2023-08)Blind single image super-resolution (SISR) is a challenging task in image processing due to the ill-posed nature of the inverse problem. Complex degradations present in real life images make it difficult to solve this ... -
Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources
(Springer, 2024-02-12)Speech synthesis has made significant strides thanks to the transition from machine learning to deep learning models. Contemporary text-to-speech (TTS) models possess the capability to generate speech of exceptionally high ... -
Deep learning-based speaker-adaptive postfiltering with limited adaptation data for embedded text-to-speech synthesis systems
(Elsevier, 2023-06)End-to-end (e2e) speech synthesis systems have become popular with the recent introduction of text-to-spectrogram conversion systems, such as Tacotron, that use encoder–decoder-based neural architectures. Even though those ... -
Deep multi-object symbol learning with self-attention based predictors
(IEEE, 2023)This paper proposes an architecture that can learn symbolic representations from the continuous sensorimotor experience of a robot interacting with a varying number of objects. Unlike previous works, this work aims to ... -
Deep Q-learning based optimization of VLC systems with dynamic time-division multiplexing
(IEEE, 2020)The traditional method to solve nondeterministic-polynomial-time (NP)-hard optimization problems is to apply meta-heuristic algorithms. In contrast, Deep Q Learning (DQL) uses memory of experience and deep neural network ... -
Deep reinforcement based power allocation for the max-min optimization in non-orthogonal multiple access
(IEEE, 2020)NOMA is a radio access technique that multiplexes several users over the frequency resource and provides high throughput and fairness among different users. The maximization of the minimum the data-rate, also known as ... -
Deep reinforcement learning approach for trading automation in the stock market
Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price "prediction" ... -
Deep reinforcement learning approach for trading automation in the stock market
(IEEE, 2022)Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price 'prediction' ...
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