Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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Now showing 1 - 10 of 56
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 5
    Vester's Sensitivity Model for Genetic Networks With Time-Discrete Dynamics
    (Springer international Publishing Ag, 2014) Moreno, Liana Amaya; Defterli, Ozlem; Fuegenschuh, Armin; Weber, Gerhard-Wilhelm; Amaya Moreno, Liana; Fügenschuh, Armin
    We propose a new method to explore the characteristics of genetic networks whose dynamics are described by a linear discrete dynamical model x(t+1) = Ax(t). The gene expression data x(t) is given for various time points and the matrix A of interactions among the genes is unknown. First we formulate and solve a parameter estimation problem by linear programming in order to obtain the entries of the matrix A. We then use ideas from Vester's Sensitivity Model, more precisely, the Impact Matrix, and the determination of the Systemic Roles, to understand the interactions among the genes and their role in the system. The method identifies prominent outliers, that is, the most active, reactive, buffering and critical genes in the network. Numerical examples for different datasets containing mRNA transcript levels during the cell cycle of budding yeast are presented.
  • Conference Object
    Parallel and Distributed Architecture for Multilingual Open Source Intelligence Systems
    (Springer international Publishing Ag, 2024) Karamanlioglu, Alper; Yurtalan, Gokhan; Karatas, Yahya Bahadir
    The proliferation of publicly available information across multiple languages presents both unique challenges and opportunities for Open Source Intelligence (OSINT) systems. This paper proposes a novel architecture for multilingual OSINT that is both parallel and distributed. The architecture integrates language identification and translation capabilities, enabling it to handle linguistically diverse data by transforming it into a unified format for efficient analysis. Designed specifically to address the challenges of parallel and distributed processing in OSINT systems, this architecture aims to offer scalability and performance benefits when dealing with massive data volumes. Our primary focus has been on devising strategies and tactics that address these concerns, providing a robust solution for the collection, processing and analysis of data in various languages. This work marks a significant step towards the development of more globally inclusive OSINT systems.
  • Conference Object
    Investigating Information Seeking Process Using Think-Aloud Protocol of Students Living in Rural Areas
    (Springer international Publishing Ag, 2022) Akkoyunlu, Buket; Cetin, Nihal Menzi; Menzi Çetin, Nihal
    In this study, we investigated how students in a rural secondary school searched for online information for their school assignments, and we employed the think-aloud protocol to reveal students' information seeking processes. In this study, three students were given three search tasks that were similar to their school assignments and they were asked to search the web for the tasks. To obtain data from each student's web search, we used screen-capture software and collected audio and video records. Recordings were subsequently transcribed and analyzed. Results showed that while the students completed the easy and medium-level search tasks to a great extent, they had difficulty in completing the difficult task. In addition, students performed the tasks with various steps and durations. Emotions such as hesitation, satisfaction, frustration and anxiety were observed in the students while they were performing the tasks. The results were also discussed in terms of the characteristics of the information seeking process.
  • Conference Object
    Citation - Scopus: 1
    Fractional Order Computing and Modeling With Portending Complex Fit Real-World Data
    (Springer international Publishing Ag, 2023) Rahman, Mati Ur; Baleanu, Dumitru; Karaca, Yeliz
    Fractional computing models identify the states of different systems with a focus on formulating fractional order compartment models through the consideration of differential equations based on the underlying stochastic processes. Thus, a systematic approach to address and ensure predictive accuracy allows that the model remains physically reasonable at all times, providing a convenient interpretation and feasible design regarding all the parameters of the model. Towards these manifolding processes, this study aims to introduce new concepts of fractional calculus that manifest crossover effects in dynamical models. Piecewise global fractional derivatives in sense of Caputo and Atangana-Baleanu-Caputo (ABC) have been utilized, and they are applied to formulate the Zika Virus (ZV) disease model. To have a predictive analysis of the behavior of the model, the domain is subsequently split into two subintervals and the piecewise behavior is investigated. Afterwards, the fixed point theory of Schauder and Banach is benefited from to prove the existence and uniqueness of at least one solution in both senses for the considered problem. As for the numerical simulations as per the data, Newton interpolation formula has been modified and extended for the considered nonlinear system. Finally, graphical presentations and illustrative examples based on the data for various compartments of the systems have been presented with respect to the applicable real-world data for different fractional orders. Based on the impact of fractional order reducing the abrupt changes, the results obtained from the study demonstrate and also validate that increasing the fractional order brings about a greater crossover effect, which is obvious from the observed data, which is critical for the effective management and control of abrupt changes like infectious diseases, viruses, among many more unexpected phenomena in chaotic, uncertain and transient circumstances.
  • Conference Object
    Citation - WoS: 4
    Citation - Scopus: 6
    Towards a Role Playing Game for Exploring the Roles in Scrum To Improve Collaboration Problems
    (Springer international Publishing Ag, 2018) Metin, Ozgun Onat; Gungor, Deniz; Yilmaz, Murat; Akarsu, Zulal
    Scrum is the most popular, useful and lightweight framework based on agile software development philosophy. In particular, software development organizations are willing to transform their software development culture to benefit from its fruitful practices. In addition, it is almost inevitable for the organizations with historical development practices to face many dysfunctions during transformation phase, which is normal and expected. It is important to uncover, analyze and solve these dysfunctions, which may take some time. One of the problems organizations may encounter is the confusion and misapplication of the roles in Scrum. This dysfunction creates problems from task creation, follow-up, taking responsibility to collaboration problems in the project. The goal of this study is to address such situations using an interactive role playing game-based approach among team members to improve collaboration.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 3
    Theory, Analyses and Predictions of Multifractal Formalism and Multifractal Modelling for Stroke Subtypes' Classification
    (Springer international Publishing Ag, 2020) Baleanu, Dumitru; Moonis, Majaz; Zhang, Yu-Dong; Karaca, Yeliz
    Fractal and multifractal analysis interplay within complementary methodology is of pivotal importance in ubiquitously natural and man-made systems. Since the brain as a complex system operates on multitude of scales, the characterization of its dynamics through detection of self-similarity and regularity presents certain challenges. One framework to dig into complex dynamics and structure is to use intricate properties of multifractals. Morphological and functional points of view guide the analysis of the central nervous system (CNS). The former focuses on the fractal and self-similar geometry at various levels of analysis ranging from one single cell to complicated networks of cells. The latter point of view is defined by a hierarchical organization where self-similar elements are embedded within one another. Stroke is a CNS disorder that occurs via a complex network of vessels and arteries. Considering this profound complexity, the principal aim of this study is to develop a complementary methodology to enable the detection of subtle details concerning stroke which may easily be overlooked during the regular treatment procedures. In the proposed method of our study, multifractal regularization method has been employed for singularity analysis to extract the hidden patterns in stroke dataset with two different approaches. As the first approach, decision tree, Naive bayes, kNN and MLP algorithms were applied to the stroke dataset. The second approach is made up of two stages: i) multifractal regularization (kulback normalization) method was applied to the stroke dataset and mFr stroke dataset was generated. ii) the four algorithms stated above were applied to the mFr stroke dataset. When we compared the experimental results obtained from the stroke dataset and mFr stroke dataset based on accuracy (specificity, sensitivity, precision, F1-score and Matthews Correlation Coefficient), it was revealed that mFr stroke dataset achieved higher accuracy rates. Our novel proposed approach can serve for the understanding and taking under control the transient features of stroke. Notably, the study has revealed the reliability, applicability and high accuracy via the methods proposed. Thus, the integrated method has revealed the significance of fractal patterns and accurate prediction of diseases in diagnostic and other critical-decision making processes in related fields.
  • Conference Object
    Citation - WoS: 6
    Citation - Scopus: 10
    The Impact of Situational Context on Software Process: a Case Study of a Very Small-Sized Company in the Online Advertising Domain
    (Springer international Publishing Ag, 2018) Yilmaz, Murat; O'Connor, Rory V.; Clarke, Paul M.; Giray, Gorkem; O’Connor, Rory V.
    A primary concern of software development is selecting a suitable methodology to implement a software project. However, this selection is affected by many factors, with evidence suggesting that a specific set of factors defines a specific situational context for a project. This situational context leads to a project-specific software process. In this paper, we report on our analysis of a very small-sized company's current software process based on a reference framework that identifies the factors of a situational context. The outcome of our case study confirms the earlier findings that a software process is highly dependent on situational factors. The company has a suitable situational context (such as very small-sized, experienced, skilled, cohesive team with low turnover) to apply agile practices and its software process is more close to an agile rather than plan-driven approach. Moreover, the company is continuously adopting its software process to the situational factors changing from project to project and over time.
  • Conference Object
    Citation - Scopus: 2
    Multifractional Gaussian Process Based on Self-Similarity Modelling for Ms Subgroups' Clustering With Fuzzy C-Means
    (Springer international Publishing Ag, 2020) Baleanu, Dumitru; Karaca, Yeliz
    Multifractal analysis is a beneficial way to systematically characterize the heterogeneous nature of both theoretical and experimental patterns of fractal. Multifractal analysis tackles the singularity structure of functions or signals locally and globally. While Holder exponent at each point provides the local information, the global information is attained by characterization of the statistical or geometrical distribution of Holder exponents occurring, referred to as multifractal spectrum. This analysis is time-saving while dealing with irregular signals; hence, such analysis is used extensively. Multiple Sclerosis (MS), is an auto-immune disease that is chronic and characterized by the damage to the Central Nervous System (CNS), is a neurological disorder exhibiting dissimilar and irregular attributes varying among patients. In our study, the MS dataset consists of the Expanded Disability Status Scale (EDSS) scores and Magnetic Resonance Imaging (MRI) (taken in different years) of patients diagnosed with MS subgroups (relapsing remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS)) while healthy individuals constitute the control group. This study aims to identify similar attributes in homogeneous MS clusters and dissimilar attributes in different MS subgroup clusters. Thus, it has been aimed to demonstrate the applicability and accuracy of the proposed method based on such cluster formation. Within this framework, the approach we propose follows these steps for the classification of the MS dataset. Firstly, Multifractal denoising with Gaussian process is employed for identifying the critical and significant self-similar attributes through the removal of MS dataset noise, by which, mFd MS dataset is generated. As another step, Fuzzy C-means algorithm is applied to the MS dataset for the classification purposes of both datasets. Based on the experimental results derived within the scheme of the applicable and efficient proposed method, it is shown that mFd MS dataset yielded a higher accuracy rate since the critical and significant self-similar attributes were identified in the process. This study can provide future direction in different fields such as medicine, natural sciences and engineering as a result of the model proposed and the application of alternative mathematical models. As obtained based on the model, the experimental results of the study confirm the efficiency, reliability and applicability of the proposed method. Thus, it is hoped that the derived results based on the thorough analyses and algorithmic applications will be assisting in terms of guidance for the related studies in the future.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 2
    Evaluation of Semantic Relatedness Measures for Turkish Language
    (Springer international Publishing Ag, 2018) Sopaoglu, Ugur; Ercan, Gonenc
    The problem of quantifying semantic relatedness level of two words is a fundamental sub-task for many natural language processing systems. While there is a large body of research on measuring semantic relatedness in the English language, the literature lacks detailed analysis for these methods in agglutinative languages. In this research, two new evaluation resources for the Turkish language are constructed. An extensive set of experiments involving multiple tasks: word association, semantic categorization, and automatic WordNet relationship discovery are performed to evaluate different semantic relatedness measures in the Turkish language. As Turkish is an agglutinative language, the morphological processing component is important for distributional similarity algorithms. For languages with rich morphological variations and productivity, methods ranging from simple stemming strategies to morphological disambiguation exists. In our experiments, different morphological processing methods for the Turkish language are investigated.
  • Correction
    Citation - WoS: 5
    Citation - Scopus: 1
    Meir-Keeler Α-Contractive Fixed and Common Fixed Point Theorems (Vol 2013, Pg 19, 2013)
    (Springer international Publishing Ag, 2013) Gopal, Dhananjay; Abdeljawad, Thabet
    In this note we correct some errors that appeared in the article (Abdeljawad in Fixed Point Theory Appl. 2013:19, 2013) by modifying some conditions in the main theorems and by giving an example to support. MSC: 47H10, 54H25.