Yönetim Bilişim Sistemleri Bölümü Koleksiyonu
Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/68
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Article Citation Count: 4An 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, OğuzhanLocality 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.Article Citation Count: 5A Competitive Intelligence Practices Typology in an Airline Company in Turkey(Springer, 2020) Şahin, Murat; Bisson, ChristopheOil prices, political instabilities, travel legislations, and many other competitive factors make it essential for any international airline with the instinct to survive to be on constant watch in such a fiercely competitive environment. To meet this need, it is vital for international airline companies to integrate competitive intelligence (CI) into their strategy building process. In this study, we create for the first time a typology of competitive intelligence practices of an international airline company (in Turkey), based on the model developed by (Wright et al.Journal of Strategic Marketing, 20(1), 19-33,2012), and it is one of the very first to investigate Competitive intelligence in this sector. Furthermore, we made a two-step cluster analysis to uncover hidden clusters that change the way of thinking within the company. Our findings show where the company would need to make improvements on the 6 strands of the model which are attitude, gathering, use, location, technological support, and IT support. Yet, that could lead towards stronger business performance. It might also inspire other companies of the airline sector and beyond.Article Citation Count: 7Dissidents 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, MarkPurpose - 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.Article Citation Count: 3Machine learning model to predict an adult learner's decision to continue ESOL course or not(Springer, 2019) Dağ, Hasan; Dağ, HasanThis 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.Article Citation Count: 7Two-dimensional inverse quasilinear parabolic problem with periodic boundary condition(Taylor & Francis Ltd, 2019) Baglan, İrem Sakınç; Kanca, FatmaIn this study we consider a coefficient problem of a quasi-linear two-dimensional parabolic inverse problem with periodic boundary and integral over determination conditions. We prove the existence uniqueness and continuously dependence upon the data of the solution by iteration method. Also we consider numerical solution for this inverse problem by using linearization and the implicit finite-difference scheme.Article Citation Count: 18Unsupervised 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 SelçukUnsupervised 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.