Browsing by Author "Bozyigit, F"

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    Automatic concept identification of software requirements in Turkish
    Bozyigit, F; Aktas, Ö; Kilinç, D
    Software requirements include description of the features for the target system and express the expectations of users. In the analysis phase, requirements are transformed into easy-to-understand conceptual models that facilitate communication between stakeholders. Although creating conceptual models using requirements is mostly implemented manually by analysts, the number of models that automate this process has increased recently. Most of the models and tools are developed to analyze requirements in English, and there is no study for agglutinative languages such as Turkish or Finnish. In this study, we propose an automatic concept identification model that transforms Turkish requirements into Unified Modeling Language class diagrams to ease the work of individuals on the software team and reduce the cost of software projects. The proposed work is based on natural language processing techniques and a new rule-set containing twenty-six rules is created to find object-oriented design elements from requirements. Since there is no publicly available dataset on the online repositories, we have created a well-defined dataset containing twenty software requirements in Turkish and have made it publicly available on GitHub to be used by other researchers. We also propose a novel evaluation model based on an analytical hierarchy process that considers the experts' views and calculate the performance of the overall system as 89%. We can state that this result is promising for future works in this domain.
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    Collaborative Filtering based Course Recommender using OWA operators
    Bozyigit, A; Bozyigit, F; Kilinç, D; Nasiboglu, E
    Recommendation systems guide users to choose the most appropriate items among numerous alternatives based on predicting their interests. Recently, it is seen that recommendation systems have become to be widely used in educational domain, especially in course recommender applications. The objectives of these systems is facilitating course selection process of students and reducing their stresses. The current course recommendation studies generally consider the most recent grades of the courses taken by students and ignore the case of repeating the course under the pass-fail or grade replacement options. However, retaking a course is the primary parameter giving opinion about tendency of the students to the courses. In this study, we propose a novel collaborative filtering (CF) based course recommendation system considering the case of repeating a course and students' grades in the course for each repetition. We experiment different Ordered Weighted Averaging (OWA) operators which aggregates grades for each student's repeated courses to enhance the recommendation quality. The normalized mean absolute error (MAE) of our approach using CF and OWA is calculated as 0,063 which is encouraging for future work.
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    Comparison of Mobile Interaction Management Products Using Systematic Literature Review Method and a New Product Suggestion
    Öztürk, S; Elmas, C; Bozyigit, F; Kilinç, D
    Because of innovations and improvements in technology, the use of smartphones that make it easier for users to work has become widespread. At this point, companies can reach their customers more easily and can communicate continuously. Once mobile applications are created, the system infrastructure needs to be improved in response to changing needs and demands to actively retain registered users and continually capture their insights. In this case, a dynamic framework that will create user profiles in a mobile application and provide services according to different user needs. In this study, the main features of the mobile interaction management applications on the market and other features they provide to create a loyal user base have been evaluated using the Systematic Literature Review (SLI) method and the necessary gaps have been discussed. In order to acquire loyal mobile-app user, Machine Learning support system is proposed as solution.
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    Peripheral mononuclear cell response to nonspecific antigenic stimulation in children with obese asthma phenotype
    Yuksel, H; Yilmaz, O; Vatansever, S; Onur, E; Kirmaz, C; Nal, E; Turkeli, A; Bozyigit, F
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    TTC-3600: A new benchmark dataset for Turkish text categorization
    Kilinç, D; Özçift, A; Bozyigit, F; Yildirim, P; Yücalar, F; Borandag, E
    Owing to the rapid growth of the World Wide Web, the number of documents that can be accessed via the Internet explosively increases with each passing day. Considering news portals in particular, sometimes documents related to categories such as technology, sports and politics seem to be in the wrong category or documents are located in a generic category called others. At this point, text categorization (TC), which is generally addressed as a supervised learning task is needed. Although there are substantial number of studies conducted on TC in other languages, the number of studies conducted in Turkish is very limited owing to the lack of accessibility and usability of datasets created. In this paper, a new dataset named TTC-3600, which can be widely used in studies of TC of Turkish news and articles, is created. TTC-3600 is a well-documented dataset and its file formats are compatible with well-known text mining tools. Five widely used classifiers within the field of TC and two feature selection methods are evaluated on TTC-3600. The experimental results indicate that the best accuracy criterion value 91.03% is obtained with the combination of Random Forest classifier and attribute ranking-based feature selection method in all comparisons performed after pre-processing and feature selection steps. The publicly available TTC-3600 dataset and the experimental results of this study can be utilized in comparative experiments by other researchers.
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    Systematic literature review of photovoltaic output power forecasting
    Basaran, K; Bozyigit, F; Siano, P; Taser, PY; Kilinç, D
    Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world-wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.