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: 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: 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: 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: 7
Dissidents with an innovation cause? Non-institutionalized actors' online social knowledge sharing solution-finding tensions and technology management innovation
(Emerald Group Publishing Limited, 2015) De Kervenoael, Ronan; Bisson, Christophe; Palmer, Mark
Purpose - Traditionally most studies focus on institutionalized management-driven actors to understand technology management innovation. The purpose of this paper is to argue that there is a need for research to study the nature and role of dissident non-institutionalized actors' (i.e. outsourced web designers and rapid application software developers). The authors propose that through online social knowledge sharing non-institutionalized actors' solution-finding tensions enable technology management innovation. Design/methodology/approach - A synthesis of the literature and an analysis of the data (21 interviews) provided insights in three areas of solution-finding tensions enabling management innovation. The authors frame the analysis on the peripherally deviant work and the nature of the ways that dissident non-institutionalized actors deviate from their clients (understood as the firm) original contracted objectives. Findings - The findings provide insights into the productive role of solution-finding tensions in enabling opportunities for management service innovation. Furthermore deviant practices that leverage non-institutionalized actors' online social knowledge to fulfill customers' requirements are not interpreted negatively but as a positive willingness to proactively explore alternative paths. Research limitations/implications - The findings demonstrate the importance of dissident non-institutionalized actors in technology management innovation. However this work is based on a single country (USA) and additional research is needed to validate and generalize the findings in other cultural and institutional settings. Originality/value - This paper provides new insights into the perceptions of dissident non-institutionalized actors in the practice of IT managerial decision making. The work departs from but also extends the previous literature demonstrating that peripherally deviant work in solution-finding practice creates tensions enabling management innovation between IT providers and users.
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: 0
A Generic Framework for Building Heterogeneous Simulations of Parallel and Distributed Computing Systems
(Springer Heidelberg, 2017) Dağ, Hasan; Dağ, Hasan
There have been many systems available for parallel and distributed computing (PDC) applications such as grids clusters super-computers clouds peer-to-peer and volunteer computing systems. High-performance computing (HPC) has been an obvious candidate domain to take advantage of PDC systems. Most of the research on HPC has been conducted with simulations and has been generally focused on a specific type of PDC system. This paper however introduces a general purpose simulation model that can be easily enlarged for constructing simulations of many of the most well-known PDC system types. Although it might create a new vision for research activities in the simulation community current simulation tools do not provide proper support for cooperation between software working in real-time and simulation time. In this paper thus we also present a promising approach for constructing hybrid simulations that offers great potential for many research areas. As a proof of concept we implemented a prototype for our simulation model. Then we are able to rely on this prototype to build simulations of various PDC systems. Thanks to hybrid simulation support of our model we are able to combine and manage the simulated PDC systems with our previously developed policy-based management framework in simulation runs.
Citation Count: 3
Machine learning model to predict an adult learner's decision to continue ESOL course or not
(Springer, 2019) Dağ, Hasan; Dağ, Hasan
This study investigated the ability of the demographic and the affective variables to predict the adult learners' decision to continue ESOL courser. 278 adult learners, enrolled on ESOL course at FLS institution in Istanbul, Turkey, participated in the study. The result showed that the continued or dropped out groups, demonstrated statistical differences in the demographic variable (the placement test score) with a magnitude of large effect size (.378). Additionally, the result showed the effect size in the perception of the affective variables (motivation, attitude, and anxiety), accounts for about 50% of the variation between the continuation and dropout groups. Following that, three machine learning models were proposed; all possible subset regression analysis was used to compare the three models. The adequate model, which fitted the demographic variable (the placement test score) and the affective variables (motivation, attitude, and anxiety), correctly predicted 83.3% of the adult learners' decision to continue ESOL course. The model showed about 68% goodness-of-fit. The cultural implications of these findings are discussed, along with suggestions for future research.
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.
Citation Count: 18
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Öğrenci, Arif Selçuk
Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.
Citation Count: 1
When Sharing Less Means More: How Gender Moderates the Impact of Quantity of Information Shared in a Social Network Profile on Profile Viewers' Intentions About Socialization
(Routledge, 2014) Baruh, Lemi; Chisik, Yoram; Bisson, Christophe; Şenova, Başak
This study summarizes the results from a 2 (low vs. high information) × 2 (female vs. male profile) experiment that investigates the impact of quantity of information shared on a Social Network Site (SNS) profile on viewers' intentions to pursue further interactions with the profile owner. Quantity of information had no statistically significant effect on intentions to further socialize online. The two-way interaction between information quantity and profile gender was such that for male profiles more information increased profile viewers' intentions to further socialize with the profile owner whereas for female profiles the opposite was the case. The three-way interactions among quantity of information profile gender and profile viewer's gender underline a tendency for male profile viewers to respond more positively to higher information shared by profiles from their own gender. For female viewers this effect although in the same direction was smaller. © 2014 Copyright Eastern Communication Association.

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