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    A bibliometric analysis of higher education and the sustainable development goals
    (IGI Global, 2023) Aydogmus, Hacer Yumurtaci; Türkan, Yusuf Sait; Aydoğmuş, Ufuk
    Universities are institutions where scientific activities and research are carried out, innovations and inventions are developed, and people are educated. In this context, higher education (HE) is of great importance in achieving the sustainable development goals (SDGs) defined by the United Nations (UN) in 2015. Activities such as awareness activities on sustainability in university education; inclusion of sustainable development themes, such as inequality, fairness, inclusion, and diversity in course content; and social activities related to sustainability, help raise people who are much more sensitive and motivated about SDGs. The critical role of HE on SDGs has made studies in this area more important. The aim of this bibliometric review is to document the volume, growth, and geographical distribution of the literature on this subject, to identify important publications, to analyze the intellectual structure of this knowledge base and to identify emerging research trends. © 2023, IGI Global. All rights reserved.
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    An In-Depth Bibliometric Analysis to Understand the Impact and Future of Artificial Intelligence in Higher Education
    (IGI Global, 2025) Aydogmus, Hacer Yumurtaci; Aydoğmuş, Ufuk; Türkan, Yusuf Sait
    Currently, artificial intelligence is used in many different ways in many different sectors-for example, in the diagnosis of diseases in the field of health by processing data, for inventory management in logistics management, and for customer service in tourism. Artificial intelligence is also used in higher education institutions in different fields such as intelligent learning systems and data management. There are different studies in the literature on the use of artificial intelligence in higher education. Considering its importance, analyzing the studies in the literature comprehensively is important for understanding the applications and making it easier for educational institutions to experience an artificial intelligence adaptation process. © 2025 by IGI Global Scientific Publishing.
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    Application of machine learning methods for passenger demand prediction in transfer stations of Istanbul's public transportation system
    (IGI Global, 2022) Aydogmus, Hacer Yumurtaci; Türkan, Yusuf Sait
    The rapid growth in the number of drivers and vehicles in the population and the need for easy transportation of people increases the importance of public transportation. Traffic becomes a growing problem in Istanbul which is Turkey's greatest urban settlement area. Decisions on investments and projections for the public transportation should be well planned by considering the total number of passengers and the variations in the demand on the different regions. The success of this planning is directly related to the accurate passenger demand forecasting. In this study, machine learning algorithms are tested in a real world demand forecasting problem where hourly passenger demands collected from two transfer stations of a public transportation system. The machine learning techniques are run in the WEKA software and the performance of methods are compared by MAE and RMSE statistical measures. The results show that the bagging based decision tree methods and rules methods have the best performance. © 2022 by IGI Global. All rights reserved.
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    ERP selection using picture fuzzy CODAS method
    (Ios Press, 2021) Aydogmus, Hacer Yumurtaci; Kamber, Eren; Kahraman, Cengiz
    The purpose of this study is to develop an extension of CODAS method using picture fuzzy sets. In this respect, a new methodology is introduced to figure out how picture fuzzy numbers can be applied to CODAS method. COmbinative Distance-based Assessment (CODAS) is a new MCDM method proposed by Ghorabaee et al. Picture fuzzy sets (PFSs) are a new extension of ordinary fuzzy sets for representing human's judgments having possibility more than two answers such as yes, no, refusal and neutral. Compared with other studies, the proposed method integrates multi-criteria decision analysis with picture fuzzy uncertainty based on Euclidean and Taxicab distances and negative ideal solution. ERP system selection problem is handled as the application area of the developed method, picture fuzzy CODAS. Results indicate that the new proposed method finds meaningful rankings through picture fuzzy sets. Comparative analyzes show that the presented method gives successful and robust results for the solutions of MCDM problems under fuzziness.
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    Evaluation of Artificial Intelligence Tools for Universities With Fuzzy Multi-Criteria Decision-Making Methods
    (IGI Global, 2025) Aydogmus, Hacer Yumurtaci; Aydoğmuş, Ufuk
    The concept of Artificial Intelligence (AI), which was first used in the 1950s, can perform tasks that require human intelligence by using various algorithms and computer programs. This chapter aims to evaluate AI tools that can be used in universities. Firstly, the AI tools used in universities were researched from the literature, then Fuzzy Multi- Criteria Decision Making was applied to determine which of these tools should be prioritized. Five criteria (perceived ease of use, perceived usefulness, personalization, interaction, trust) obtained from expert opinions and the literature were weighted and the AI tools that were determined as alternatives were prioritized. Fuzzy numbers, first presented to the literature by Zadeh in 1965, aid in decision- making because it is frequently impossible to utilize precise formulations during evaluations. Since 1965, extensions of fuzzy numbers have been developed and integrated into multi criteria decision making methods. The decision making method to be used in this study is also integrated with the use of intuitionistic fuzzy. © 2025 by IGI Global Scientific Publishing.
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    Location and Capacity Planning of Freight Villages: The Case of Türkiye
    (Mehmet Akif Ersoy Univ, 2024) Gumus, Mehmet; Aydogmus, Hacer Yumurtaci; Ozder, Emir Huseyin
    Freight villages are logistics hubs where distribution and storage related activities meet. Determining their locations and capacities is a strategically important yet a difficult problem due to its complex structure. This paper provides a mixed integer linear programming model for identifying the locations, number, and capacities of freight villages. Objective is to set up a distribution network and minimize its total cost. The proposed model is computationally efficient as the optimal solution can be found within minutes. The model is applied using real life data in Turkey. The application includes major container ports as supply points and all cities as demand points, which are also alternative locations for freight villages. Optimal solution provides a plan and a budget to build the required capacity and number of logistics villages dispersed geographically. Sensitivity analysis is also conducted to investigate the system -wide costs and derive insights when there is a limit on the number of freight villages.
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    Prioritization of drip-irrigation pump alternatives in agricultural applications: An integrated picture fuzzy BWM&CODAS methodology
    (Elsevier, 2024) Kamber, Eren; Aydogmus, Ufuk; Aydogmus, Hacer Yumurtaci; Guemues, Mehmet; Kahraman, Cengiz
    One of the irrigation methods is drip irrigation, for which selecting the right pump has a significant impact. In this study, the process of choosing the appropriate pump for drip irrigation is regarded as a multi-criteria decision-making (MCDM) problem. The objective is to enhance productivity and minimize water consumption in agricultural areas by addressing the drip-irrigation pump-selection problem. Making decisions under uncertainty is a complex task, especially when dealing with intricate problems where complexity raises concerns about finding more dependable solutions. Fuzzy extensions of MCDM methods are designed to tackle such intricate and detailed problems compared to traditional MCDM methods. Therefore, we propose and implement a Picture Fuzzy CODAS (PF-CODAS) method to address the issue of drip-irrigation pump selection under vagueness, utilizing expert opinions. In comparison to other MCDM methods, our suggested approach combines multi-criteria decision analysis with picture fuzzy hesitancy and a negative ideal solution, supported by Euclidean and Taxicab distances. Furthermore, we present an integrated approach that uses the Best Worst Method (BWM) to determine criterion weights and the PF-CODAS method for ranking alternatives. Overall, this study offers valuable support for advancing sustainable agriculture through our proposed MCDM approach.
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    The prediction of the wind speed at different heights by machine learning methods
    (2016) Türkan, Yusuf S.; Aydogmus, Hacer Yumurtaci; Erdal, Hamit
    In Turkey, many enterprisers started to make investment on renewable energy systems after new legal regulations and stimulus packages about production of renewable energy were introduced. Out of many alternatives, production of electricity via wind farms is one of the leading systems. For these systems, the wind speed values measured prior to the establishment of the farms are extremely important in both decision making and in the projection of the investment. However, the measurement of the wind speed at different heights is a time consuming and expensive process. For this reason, the success of the techniques predicting the wind speeds is fairly important in fast and reliable decisionmaking for investment in wind farms. In this study, the annual wind speed values of Kutahya, one of the regions in Turkey that has potential for wind energy at two different heights, were used and with the help of speed values at 10 m, wind speed values at 30 m of height were predicted by seven different machine learning methods. The results of the analysis were compared with each other. The results show that support vector machines is a successful technique in the prediction of the wind speed for different heights.