Yönetim Bilişim Sistemleri Bölümü Koleksiyonu

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

Browse

Now showing 1 - 13 of 13

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: 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: 97
Coordinated Electric Vehicle Charging With Reactive Power Support to Distribution Grids
(IEEE, 2019) Ceylan, Oğuzhan; Bharati, Guna R.; Paudyal, Sumit; Ceylan, Oğuzhan; Bhattarai, Bishnu P.; Myers, Kurt S.
We develop hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed electric vehicles (EVs) incorporating distribution grid level constraints. The frameworks consist of detailed mathematical models which can benefit the operation of both entities involved i.e. the grid operations and EV charging. The first model comprises of a comprehensive optimal power flow model at the distribution grid level while the second model represents detailed optimal EV charging with reactive power support to the grid. We demonstrate benefits of coordinated dispatch of active and reactive power from EVs using a 33-node distribution feeder with large number of EVs (more than 5000). Case studies demonstrate that in constrained distribution grids coordinated charging reduces the average cost of EV charging if the charging takes place at nonunity power factor mode compared to unity power factor. Similarly the results also demonstrate that distribution grids can accommodate charging of increased number of EVs if EV charging takes place at nonunity power factor mode compared to unity power factor.
Citation Count: 2
Double branch outage modeling and simulation: Bounded network approach
(Elsevier Science, 2015) Dağ, Hasan; Ceylan, Oğuzhan; Dağ, Hasan
Energy management system operators perform regular outage simulations in order to ensure secure operation of power systems. AC power flow based outage simulations are not preferred because of insufficient computational speed. Hence several outage models and computational methods providing acceptable accuracy have been developed. On the other hand double branch outages are critical rare events which can result in cascading outages and system collapse. This paper presents a double branch outage model and formulation of the phenomena as a constrained optimization problem. Optimization problem is then solved by using differential evolution method and particle swarm optimization algorithm. The proposed algorithm is applied to IEEE test systems. Computational accuracies of differential evolution based solutions and particle swarm optimization based solutions are discussed for IEEE 30 Bus Test System and IEEE 118 Bus Test System applications. IEEE 14 Bus Test System IEEE 30 Bus Test System IEEE 57 Bus Test System IEEE 118 Bus Test System and IEEE 300 Bus Test System simulation results are compared to AC load flows in terms of computational speed. Finally the performance of the proposed method is analyzed for different outage configurations. (C) 2015 Elsevier Ltd. All rights reserved.
Citation Count: 10
Dynamic network analysis of online interactive platform
(Springer, 2019) Aydın, Mehmet Nafiz; Perdahci, N. Ziya
The widespread use of online interactive platforms including social networking applications community support applications draw the attention of academics and businesses. The basic trust of this research is that the very nature of these platforms can be best described as a network of entangled interactions. We agree with scholars that these platforms and features necessitate the call for theory of network as a novel approach to better understand their underpinnings. We examine one of the leading online interactive health networks in Europe. We demonstrate that the interactive platform examined exhibits essential structural properties that characterize most real networks. In particular we focus on the largest connected component so-called a giant component (GC) to better understand network formation. Dynamic network analysis helps us to observe how the GC has evolved over time and to identify a particular pattern towards emerging a GC. We suggest that the network measures examined for the platform should be considered as novel and complementary metrics to those used in conventional web and social analytics. We argue that various stages of GC development can be a promising indicator of the strength and endurance of the interactions on the platform. Platform managers may take into account basic stages of the emergence of the GC to determine varying degrees of product attractiveness.
Citation Count: 68
Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation
(Elsevier, 2017) Bilge, Ayşe Hümeyra; Yücekaya, Ahmet; Bilge, Ayşe Hümeyra
In deregulated electricity markets the independent system operator (ISO) oversees the power system and manages the supply and demand balancing process. In a typical day the ISO announces the electricity demand forecast for the next day and gives participants an option to prepare offers to meet the demand. In order to have a reliable power system and successful market operation it is crucial to estimate the electricity demand accurately. In this paper we develop an hourly demand forecasting method on annual weekly and daily horizons using a linear model that takes into account the harmonics of these variations and the modulation of diurnal periodic variations by seasonal variations. The electricity demand exhibits cyclic behavior with different seasonal characteristics. Our model is based solely on sinusoidal variations and predicts hourly variations without using any climatic or econometric information. The method is applied to the Turkish power market on data for the period 2012-2014 and predicts the demand over daily and weekly horizons within a 3% error margin in the Mean Absolute Percentage Error (MAPE) norm. We also discuss the week day/weekend/holiday consumption profiles to infer the proportion of industrial and domestic electricity consumption. (C) 2017 Elsevier Ltd. All rights reserved.
Citation Count: 8
An inverse coefficient problem for a quasilinear parabolic equation with periodic boundary and integral overdetermination condition
(Wiley-Blackwell, 2015) Bağlan, İrem Sakınç; Kanca, Fatma
In this paper the inverse problem of finding the time-dependent coefficient of heat capacity together with the solution periodic boundary and integral overdetermination conditions is considered. Under some natural regularity and consistency conditions on the input data the existence uniqueness and continuous dependence upon the data of the solution are shown. Some considerations on the numerical solution for this inverse problem are presented with an example. Copyright (c) 2014 John Wiley & Sons Ltd.
Citation Count: 3
Inverse Coefficient Problem for a Second-Order Elliptic Equation with Nonlocal Boundary Conditions
(Wiley, 2016) Kanca, Fatma
In this research article the inverse problem of finding a time-dependent coefficient in a second-order elliptic equation is investigated. The existence and the uniqueness of the classical solution of the problem under consideration are established. Numerical tests using the finite-difference scheme combined with an iteration method are presented and the sensitivity of this scheme with respect to noisy over determination data is illustrated. Copyright (C) 2015 John Wiley & Sons Ltd.
Citation Count: 12
Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters
(Springer, 2020) Ceylan, Oğuzhan
Renewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient usage of cascaded H-bridge multilevel inverters (MLIs) for power control applications becomes vital for sustainable electricity. Conventionally, selective harmonic elimination equations need to be solved for obtaining optimum switching angles of MLIs. The objective of this study is to obtain switching angles for MLIs to minimize total harmonic distortion. This study contributes to the solution of this problem by utilizing two recently developed intelligent optimization algorithms: multi-verse optimization algorithm and salp swarm algorithm. Moreover, well-known particle swarm optimization is utilized for MLI optimization problem. Seven-level, 11-level and 15-level MLIs are used to minimize total harmonic distortions. Simulation results with different modulation indexes for seven-, 11- and 15-level MLIs are calculated and compared in terms of the accuracy and solution quality. Numerical calculations are verified by using MATLAB/Simulink-based models.
Citation Count: 9
Ontology-based data acquisition model development for agricultural open data platforms and implementation of OWL2MVC tool
(Elsevier, 2020) Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz
In the open data world, it is difficult to collect data in compliance with a certain data model that is of interest to different types of stakeholders within a domain like agriculture. Ontologies that provide broad vocabularies and metadata with respect to a given domain can be used to create various data models. We consider that while creating data acquisition forms to gather data related to an agricultural product, which is hazelnut in this study, from stakeholders of the relevant domain, the traits can be modeled as attributes of the data models. We propose a generic ontology-based data acquisition model to create data acquisition forms based on model-view-controller (MVC) design pattern, to publish and make use of on the agricultural open data platforms. We develop a tool called OWL2MVC that integrates the Hazelnut Ontology, which illustrates the effectiveness of the proposed model for generating data acquisition forms. Because model creation is implemented in compliance with the selection of ontology classes, stakeholders; in other words, the users of OWL2MVC Tool could generate data acquisition forms quickly and independently. OWL2MVC Tool was evaluated in terms of usability by fifty-three respondents implementing the case-study scenario. Among others the findings show that the tool has satisfactory usability score overall and is promising to provide stakeholders with required support for agricultural open data platforms.
Citation Count: 28
Random CapsNet forest model for imbalanced malware type classification task
(Elsevier, 2021) Dağ, Hasan; Ünal, Uğur; Dağ, Hasan
Behavior of malware varies depending the malware types, which affects the strategies of the system protection software. Many malware classification models, empowered by machine and/or deep learning, achieve superior accuracies for predicting malware types. Machine learning-based models need to do heavy feature engineering work, which affects the performance of the models greatly. On the other hand, deep learning-based models require less effort in feature engineering when compared to that of the machine learning-based models. However, traditional deep learning architectures components, such as max and average pooling, cause architecture to be more complex and the models to be more sensitive to data. The capsule network architectures, on the other hand, reduce the aforementioned complexities by eliminating the pooling components. Additionally, capsule network architectures based models are less sensitive to data, unlike the classical convolutional neural network architectures. This paper proposes an ensemble capsule network model based on the bootstrap aggregating technique. The proposed method is tested on two widely used, highly imbalanced datasets (Malimg and BIG2015), for which the-state-of-the-art results are well-known and can be used for comparison purposes. The proposed model achieves the highest F-Score, which is 0.9820, for the BIG2015 dataset and F-Score, which is 0.9661, for the Malimg dataset. Our model also reaches the-state-of-the-art, using 99.7% lower the number of trainable parameters than the best model in the literature.
Citation Count: 1
School-wide friendship metadata correlations
(Pergamon-Elsevier Science Ltd, 2019) Aydın, Mehmet Nafiz; Perdahçı, Nazım Ziya
Managers and education practitioners desire to know an extent to which sustainable school-wide friendship exists. Drawing on theory of network this research focuses on bestfriendships that may contribute to positive school experience or school belonging in the context of school-wide interactions. We emphasize that school-wide unity is essential to refer to shared perceived friendship experience at the school level. The basic trust of this study is that managers should consider interconnectedness as a complex system of entangled interactions among students. We investigate best friendship network on the meso-to-macro scale. Particular attention is paid to the network phenomena of the largest component and network correlations for examining school wide unity. The results show that abundance of asymmetric friendships leads to unity around school wide interactions. As suggested by network theory popular students' tendency to avoid forming closed clusters assures sustainability in school-wide friendships and having same gender type or being classmates correlate highly with the choice of best friends in contrast to achievement scores. Metadata correlations reveal same-gender and same-class clubs. Incorporating meso level findings into macro level indicates that some metadata (e.g. gender) may be considered as salient characteristics of the communities while other metadata (e.g. achievement scores) may be irrelevant.
Citation Count: 11
Strategic Early Warning System for the French milk market: A graph theoretical approach to foresee volatility
(Elsevier, 2017) Bisson, Christophe; Diner, Öznur Yaşar
This paper presents a new approach for developing a Strategic Early Warning System aiming to better detect and interpret weak signals. We chose the milk market as a case study in line with the recent call from the EU Commission for governance tools which help to better address such highly volatile markets. Furthermore on the first of April 2015 the new Common Agricultural Policy ended quotas for milk which led to a milk crisis in the EU. Thus we collaborated with milk experts to get their inputs for a new model to analyse the competitive environment. Consequently we constructed graphs to represent the major factors that affect the milk industry and the relationships between them. We obtained several network measures for this social network such as centrality and density. Some factors appear to have the largest major influence on all the other graph elements while others strongly interact in cliques. Any detected changes in any of these factors will automatically impact the others. Therefore scanning ones competitive environment can allow an organisation to get an early warning to help it avoid an issue (as much as possible) and/or seize an opportunity before its competitors. We conclude that Strategic Early Warning Systems as a corporate foresight approach utilising graph theory can strengthen the governance of markets. (C) 2017 Elsevier Ltd. All rights reserved.

Browse

Browsing Yönetim Bilişim Sistemleri Bölümü Koleksiyonu by WoS Q "Q1"