Yönetim Bilişim Sistemleri Bölümü Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/68

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Citation Count: 1
Active and Reactive Power Load Profiling Using Dimensionality Reduction Techniques and Clustering
(Institute of Electrical and Electronics Engineers Inc., 2019) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Papadopoulos, Theofilos A.; Kazaki, Anastasia G.; Barzegkar-Ntovom, Georgios A.
This paper proposes a methodology to characterize active and reactive power load profiles. Specifically, the approach makes use of fast Fourier Transform for conversion into frequency domain, principle component analysis to reduce the dimension and K-means++ to determine the representative load profiles. The data set consists of five-year measurements taken from the Democritus University of Thrace Campus. Test days were also classified as working and non-working. From the results it is observed that the proposed methodology determines representative load profiles effectively both regarding active and reactive power.
Citation Count: 4
An Adaptive Affinity Matrix Optimization for Locality Preserving Projection via Heuristic Methods for Hyperspectral Image Analysis
(IEEE-Inst Electrıcal Electronıcs Engıneers Inc, 2019) Ceylan, Oğuzhan; Ceylan, Oğuzhan
Locality preserving projection (LPP) has been often used as a dimensionality reduction tool for hyperspectral image analysis especially in the context of classification since it provides a projection matrix for embedding test samples to low dimensional space. However, the performance of LPP heavily depends on the optimization of two parameters of the graph affinity matrix: k-nearest neighbor and heat kernel width, when one considers an isotropic kernel. These two parameters might be optimally chosen simply based on a grid search. In case of using a generalized heat kernel where each feature is separately weighted by a kernel width, the number of parameters that need to be optimized is related to the number of features of the dataset, which might not be very easy to tune. Therefore, in this article, we propose to use heuristic methods, including genetic algorithm (GA), harmony search (HS), and particle swarm optimization (PSO), to explore the effects of the heat kernel parameters aiming to analyze the embedding quality of LPP's projection in terms of various aspects, including 1-NN classification accuracy, locality preserving power, and quality of the graph affinity matrix. The results obtained with the experiments on three hyperspectral datasets show that HS performs better than GA and PSO in optimizing the parameters of the affinity matrix, and the generalized heat kernel achieves better performance than the isotropic kernel. Additionally, a feature selection application is performed by using the kernel width of the generalized heat kernel for each heuristic method. The results show that very promising results are obtained in comparison with the state-of-the-art feature selection methods.
Citation Count: 13
Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik
(Jmır Publıcatıons, Inc, 130 Queens Quay E, 2020) Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Akdur, Gizdem
Background: Dietetics mobile health apps provide lifestyle tracking and support on demand. Mobile health has become a new trend for health service providers through which they have been shifting their services from clinical consultations to online apps. These apps usually offer basic features at no cost and charge a premium for advanced features. Although diet apps are now more common and have a larger user base, in general, there is a gap in literature addressing why users intend to use diet apps. We used Diyetkolik, Turkey's most widely used online dietetics platform for 7 years, as a case study to understand the behavioral intentions of users. Objective: The aim of this study was to investigate the factors that influence the behavioral intentions of users to adopt and use mobile health apps. We used the Technology Acceptance Model and extended it by exploring other factors such as price-value, perceived risk, and trust factors in order to assess the technology acceptance of users. Methods: We conducted quantitative research on the Diyetkolik app users by using random sampling. Valid data samples gathered from 658 app users were analyzed statistically by applying structural equation modeling. Results: Statistical findings suggested that perceived usefulness (P<.001), perceived ease of use (P<.001), trust (P<.001), and price-value (P<.001) had significant relationships with behavioral intention to use. However, no relationship between perceived risk and behavioral intention was found (P=.99). Additionally, there was no statistical significance for age (P=.09), gender (P=.98), or previous app use experience (P=.14) on the intention to use the app. Conclusions: This research is an invaluable addition to Technology Acceptance Model literature. The results indicated that 2 external factors (trust and price-value) in addition to Technology Acceptance Model factors showed statistical relevance with behavioral intention to use and improved our understanding of user acceptance of a mobile health app. The third external factor (perceived risk) did not show any statistical relevance regarding behavioral intention to use. Most users of the Diyetkolik dietetics app were hesitant in purchasing dietitian services online. Users should be frequently reassured about the security of the platform and the authenticity of the platform's dietitians to ensure that users' interactions with the dietitians are based on trust for the platform and the brand.
Citation Count: 0
Analysis and Implications of the Giant Component for an Online Interactive Platform
(Int Business Information Management Assoc-IBIMA, 2016) Aydın, Mehmet Nafiz; Perdahci, N. Ziya
This research is concerned with practical and research challenges related to understanding the nature of online interactive platforms. So-called network science is adopted to investigate the very nature of these systems as complex systems. In this regard we examine an online interactive health network and show that the interactive platform examined exhibits essential structural properties that characterize most real complex networks. We basically look into the largest connected component so-called a giant component (GC) to better understand how the representative network has established. In particular we apply dynamic network analysis to investigate how the GC has evolved over time. We identify a particular pattern towards emerging a GC. Implications of the patterns have been elaborated from a management perspective. We recommend that the basic stages of the emergence of the GC might be of interest to platform managers while evaluating performance of online platforms.
Citation Count: 1
Analysis of the Patients and Physicians Connection Network on an online Health Information Platform
(IOS Press, 2014) Aydın, Mehmet Nafiz; Perdahci, N. Ziya
Social network applications have gained popularity in the health domain as they bring health information seekers (patients and alike) and medication advice providers (physicians and other relevant actors) together. By employing a network science perspective this research is aimed to understand an information network establishing connections among and between information seekers and providers. We found that such a connection network surfaces most of the essential characteristics of a typical complex network. Furthermore a detailed structural analysis shows some intriguing relations and connection behaviours in the network. Implications of the findings are discussed from the perspectives of medical informatics and social network analysis.
Citation Count: 7
Anytime. Everywhere. Mobile learning in higher education: creating a GIS course
(Springer-Verlag Berlin, 2012) Oberer, Bırgıt; Oberer, Birgit
The course concepts introduced in this contribution were implemented in 2011 in a university in Turkey and show an approach for integrating mobile learning modules in higher education. The results of the course show the advantages as well as potential for improvement of the system and the use of it in higher education.
Citation Count: 0
Applications of Eigenvalue Counting and Inclusion Theorems in Model Order Reduction
(Springer-Verlag Berlin, 2010) Dağ, Hasan; Dağ, Hasan
We suggest a simple and an efficient iterative method based on both the Gerschgorin eigenvalue inclusion theorem and the deflation methods to compute a Reduced Order Model (ROM) to lower greatly the order of a given state space system. This method is especially efficient in symmetric state-space systems but it works for the other cases with some modifications.
Citation Count: 18
Applying a behavioural and operational diagnostic typology of competitive intelligence practice: empirical evidence from the SME sector in Turkey
(Taylor and Francis Group, 2012) Wright, Sheila; Bisson, Christophe; Duffy, Alistair P.
This paper reports on an empirical study conducted within the SME sector in the city of Istanbul Turkey. The findings from this study enabled the creation of a behavioural and operational typology of competitive intelligence practice one developed from the work of S. Wright D.W. Pickton and J. Callow (2002. Competitive intelligence in UK firms: A typology. Marketing Intelligence & Planning 20 349-360). Using responses to questions which indicated a type of behaviour or operational stance towards the various strands of CI practice under review it has been possible to identify areas where improvements could be made to reach an ideal situation which could garner significant competitive advantage for the SMEs surveyed. © 2012 Copyright Taylor and Francis Group LLC.
Citation Count: 11
Assessment of chromite liberation spectrum on microscopic images by means of a supervised image classification
(Elsevier Science Bv, 2017) Çavur, Mahmut; Çavur, Mahmut; Hosten, Cetin
Assessment of mineral liberation spectrum with all its aspects is essential for plant control and optimization. This paper aims to estimate 2D mineral map and its associated liberation spectrum of a particular chromite sample from optical micrographs by using Random Forest Classification a powerful machine-learning algorithm implemented on a user-friendly and an open-source software. This supervised classification method can be used to accurately generate 2D mineral map of this chromite sample. The variation of the measured spectra with the sample size is studied showing that images of 200 particles randomly selected from the optical micrographs are sufficient to reproduce liberation spectrum of this sample. In addition the 2D spectrum obtained with this classification method is compared with the one obtained from the Mineral Liberation Analyzer (MLA). Although 2D mineralogical compositions obtained by the two methods are quite similar microscopic analysis estimates poorer liberation than MLA due to the residual noise (misclassified gangue) generated by the classification. Nevertheless we cannot compare the reliabilities of the two methods as there is not a standard produce yet to quantify the accuracy of MLA analysis. (C) 2017 Elsevier B.V. All rights reserved.
Citation Count: 3
Assessment of Harmonic Distortion on Distribution Feeders with Electric Vehicles and Residential PVs
(IEEE, 2017) Ceylan, Oğuzhan; Paudyal, Sumit; Dahal, Sudarshan; Karki, Nava R.
Power-electronic interfacing based devices such as photovoltaic (PV) panels and electric vehicles (EVs) cause voltage/current harmonic distortions on the power grid. The harmonic current profiles from EVs and PVs depend on the design of the controllers integrated to the PV inverters and EV chargers. Similarly the voltage and current harmonic distortions on a grid change throughout the day as the PV output power number of grid connected EVs and the other load pattern change. In this context we present harmonic assessment to demonstrate cumulative effect of large number of EVs and PVs on a medium voltage distribution grid. We will demonstrate the case studies on the IEEE 123-node distribution feeder with 20% 50% and 100% PV and EV penetrations based on time series simulations carried out for an entire day.
Citation Count: 37
Bitcoin Forecasting Using ARIMA and PROPHET
(IEEE, 2018) Yenidoğan, Işıl; Çayır, Aykut; Kozan, Ozan; Dağ, Tugce; Arslan, Çiğdem
This paper presents all studies methodology and results about Bitcoin forecasting with PROPHET and ARIMA methods using R analytics platform. To find the most accurate forecast model the performance metrics of PROPHET and AMNIA methods are compared on the same dataset. The dataset selected 16r this study starts from May 2016 and ends in March 2018 which is the interval that Bitcoin values changing significantly against the other currencies. Data is prepared for time series analysis by performing data preprocessing steps such as time stamp conversion and feature selection. Although the time series analysis has a univariate characteristics it is aimed to include some additional variables to each model to improve the forecasting accuracy. Those additional variables are selected based on different correlation studies between cryptocurrencies and real currencies. The model selection for both ARIMA and PROPHET is done by using threefold splitting technique considering the time series characteristics of the dataset. The threefold splitting technique gave the optimum ratios for training validation and test sets. Filially two different models are created and compared in terms of performance metrics. Based on the extensive testing we see that PROPHET outperforms ARIMA by 0.94 to 0.68 in R-2 values.
Citation Count: 2
Branch outage simulation based contingency screening by gravitational search algorithm
(Praise Worthy Prize Srl, 2012) Ceylan, Oğuzhan; Dağ, Hasan; Dağ, Hasan
Power systems contingency analysis is an important issue for electric power system operators. This paper performs branch outage simulation based contingency screening using a bounded network approach. Local constrained optimization problem representing the branch outage phenomena is solved by the gravitational search algorithm. The proposed method is applied to IEEE 14 30 57 and 118 Bus Test systems and its performance from the point of capturing violations is evaluated. In addition false alarms and the computational accuracy of the proposed method are also analyzed by using scattering diagrams. Finally the proposed gravitational search based contingency screening is compared with full AC load flow solutions from the point of computational speed. Copyright (C) 2012 Praise Worthy Prize S.r.l. - All rights reserved.
Citation Count: 15
Branch outage solution using particle swarm optimization
(2008) Ceylan, Oğuzhan; Dağ, Hasan; Dağ, Hasan
For post outage MW line flows and voltage magnitude calculations most of the methods use linear methods because of their simplicity. Especially for reactive power flow calculations one can face high errors. In this paper we use a minimization method that minimizes the errors resulting from the linear system model implementation. We solve the optimization problem using particle swarm optimization. We give some outage examples using IEEE 14 bus IEEE 30 bus and IEEE 57 bus data and compare the results with full ac load flow calculation. © 2008 Australasian Universities Power Engineering Conference (AUPEC'08).
Citation Count: 1
Case Study 8: IGaranti: Expanding the Frontiers of Mobile Banking Innovation
(Springer International Publishing, 2016) Ozturkcan, Selcen; Tunçalp, Deniz
iGaranti have claimed many awards and recognitions in addition to the wide press coverage. Two years into its launch iGaranti counted for an active user base of 110 k. However the spread of its reach was only about 2?% of the active mobile banking users in the market. Mr. Yilmaz worried about the bottlenecks that had limited further user acceptance and engagement. © Springer International Publishing Switzerland 2017.
Citation Count: 0
A characterization of totally umbilical hypersurfaces of a space form by geodesic mapping
(Springer, 2013) Canfes, Elif Ozkara; Özdeğer, Abdülkadir
The idea of considering the second fundamental form of a hypersurface as the first fundamental form of another hypersurface has found very useful applications in Riemannian and semi-Riemannian geometry especially when trying to characterize extrinsic hyperspheres and ovaloids. Recently T. Adachi and S. Maeda gave a characterization of totally umbilical hypersurfaces in a space form by circles. In our paper we give a characterization of totally umbilical hypersurfaces of a space form by means of geodesic mapping.
Citation Count: 2
Cloud eLearning: transforming education through cloud technology: preliminaries for generation C
(ACAD Conferences Ltd, 2012) Oberer, Bırgıt; Erkollar, Alptekin
Cloud computing is a buzz word that is also spilling over into the education industry which enables access to software applications hardware data and computer processing power on the Web rather than installing software onto one's computer or server. For education it offers new possibilities to structure and perform learning processes. In this study the potential impact of cloud computing on education is analyzed showing what it means for educators and students as well as institutions and summarized under the heading 'Generation C' where C stands for cloud. The results of the study revealed that cloud computing can be used by educators institutions and individual students as well as by jurisdiction to support particular teaching and learning experiences and to organize software availability. Challenges for educators and institutions using cloud computing in teaching could be summarized under the following keywords: interoperability and transferability terms and conditions security and privacy issues backup and perpetuity denial of service and content issues. On a whole some implications of using cloud computing need to be kept in mind good understanding of the applications in use development of guidelines for use and migration strategies as well as the implementation of risk management could allow educators and institutions to take advantage of cloud computing in turn offering rich online learning experiences for students.
Citation Count: 6
Comparative Study of Active Power Curtailment Methods of PVs for Preventing Overvoltage on Distribution Feeders
(IEEE, 2018) Ceylan, Oğuzhan; Bhattarai, Bishnu P.; Tonkoski, Reinaldo; Dahal, Sudarshan; Ceylan, Oğuzhan
Overvoltage is one of the major issues on distribution grids with high penetration of photovoltaic (PV) generation. Overvoltage could be prevented through the control of active/reactive power of PVs. However given the high R/X ratio of low voltage feeders voltage control by using reactive power would not be as effective as using active power. Therefore active power curtailment (APC) of PVs though not desirable becomes necessary at times to prevent the overvoltage issues. Existing literature is rich in centralized and droop-based methods for APC and/or reactive power control of PVs to prevent overvoltage issues. In this context this paper revisits the most popular existing methods and evaluates the performance of droop-based and centralized methods using a typical North American 240 V low voltage feeder with 24 residential homes. In this work our key findings are: a) droop-based methods provided conservative solutions or did not eliminate the overvoltages completely b) power flow sensitivity based droop approach led to 13% more curtailment than the centralized approaches c) centralized approach had 40% less energy curtailed compared with standard droop while no overvoltages were observed and d) operating PVs at non-unity power factor in centralized approach led to 5% less energy curtailment.
Citation Count: 1
A comparative study of surrogate based learning methods in solving power flow problem
(IEEE, 2020) Ceylan, Oğuzhan; Taşkın, Gülsen; Paudyal, Sumit
Due to increasing volume of measurements in smart grids, surrogate based learning approaches for modeling the power grids are becoming popular. This paper uses regression based models to find the unknown state variables on power systems. Generally, to determine these states, nonlinear systems of power flow equations are solved iteratively. This study considers that the power flow problem can be modeled as an data driven type of a model. Then, the state variables, i.e., voltage magnitudes and phase angles are obtained using machine learning based approaches, namely, Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). Several simulations are performed on the IEEE 14 and 30-Bus test systems to validate surrogate based learning based models. Moreover, input data was modified with noise to simulate measurement errors. Numerical results showed that all three models can find state variables reasonably well even with measurement noise.
Citation Count: 0
Comparison of Cost-Free Computational Tools for Teaching Physics
(IEEE, 2010) Dağ, Hasan; Dağ, Hasan
It is widely accepted that it is quite difficult to engage today's students, from high schools to university, both in educational activities in class and "teaching" them physics due to their prejudices about the complexity of physics. The difficulty in capturing students' attention in class for a long time also plays a role in less effective teaching during learning activities. Research shows that students learn little from traditional lectures. According to constructivist learning theories, visual aids and hands-on activities play a major role in learning physics. In addition to laboratory work there are many computational tools for teaching physics, which help teachers and students in constructing a conceptual framework. With this in mind, this paper compares freeware and open source computational tools for teaching physics.
Citation Count: 16
Comparison of feature selection algorithms for medical data
(IEEE, 2012) Dağ, Hasan; Sayın, Kamran Emre; Yenidoğan, Işıl; Albayrak, Songül Varli; Acar, Can
Data mining application areas widen day by day. Among those areas medical area has been receiving quite a big attention. However working with very large data sets with many attributes is hard. Experts in this field use heavily advanced statistical analysis. The use of data mining techniques is fairly new. This paper compares three feature selection algorithms on medical data sets and comments on the importance of discretization of attributes. © 2012 IEEE.

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