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Kabarcık, Ahmet

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https://hdl.handle.net/20.500.12416/8657
Name Variants
Kabarcik, A.
Job Title
Öğr. Gör.
Email Address
a.kabarcik@cankaya.edu.tr
Main Affiliation
Endüstri Mühendisliği
Status
Current Staff
Website
academic.cankaya.edu.tr/~a.kabarcik
ORCID ID
0000-0002-3429-4997
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Sustainable Development Goals

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CLIMATE ACTION
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DECENT WORK AND ECONOMIC GROWTH
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GOOD HEALTH AND WELL-BEING
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LIFE ON LAND
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PARTNERSHIPS FOR THE GOALS
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LIFE BELOW WATER
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QUALITY EDUCATION
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SUSTAINABLE CITIES AND COMMUNITIES
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CLEAN WATER AND SANITATION
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REDUCED INEQUALITIES
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INDUSTRY, INNOVATION AND INFRASTRUCTURE
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RESPONSIBLE CONSUMPTION AND PRODUCTION
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AFFORDABLE AND CLEAN ENERGY
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PEACE, JUSTICE AND STRONG INSTITUTIONS
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Scholarly Output

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Communications in Computer and Information Science1
Savunma Bilimleri Dergisi1
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  • Thumbnail Image
    Master Thesis
    Platform independent database management
    (2006) Kabarcık, Ahmet
    üOZğ Ë ü Ë Ë PLATFORMDAN BAGIMSIZ VERITABANI YONETIMIKabarcık, AhmetRastgele değerler atanarak oluşturulan veritabanlarında en doğru tasarımı elde et-g s gmek işin tablolardaki üzelliklerin normalizasyon işin işlevsel bağımlılığının ve tablonunc o cs g gana anahtarının belirlenmesi gerekir.Bu şalışma; her satırı, atanmadan ünce, bir muamma olan ve her satırı birbirindencs obağımsız olarak atanmış rastgele veritabanlarında ortalama haldeki karışıklığa güreg s sgoË sbazı olasılık modellerini inceler. Işlevsel bağımlılık işin matematiksel değerler bulun-g c gmaya şalışılmıştır. Bu şalışma; en uygun ve en küşuk anahtarı bulmayı ve tablolar-cs s cs uc üdaki alanların işlevsel bağımlılıklarını otomatik olarak bulup normalizasyonun gerekips ggerekmediği konusunda tasarımcıya yardımcı olmayı hedeï¬ er.g
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    Article
    Ağların Hareketli Yol-Kesici Tarafından En Kısa Güzergâh Kullanılarak Kesilmesi
    (2012) Kabarcık, Ahmet; Kandiller, Levent; Aygüneş, Haluk
    Bu makalede yolların hareketli yol-kesici tarafından devre dışı bırakıldığı bir ağ kesme problemi ele alınmıştır. İçiçe geçmiş iki ağdan biri ağ-kullanıcı tarafından, diğeri ise yol-kesici tarafından kullanılmaktadır. Yol-kesici ağı üzerindeki düğümler ağ-kullanıcı ağındaki yolların ya da düğümlerin üzerinde konuşlanmıştır. Yol-kesici ağı üzerindeki düğümler imha edilmeye aday noktalardır. Bu çalışmada ağ-kullanıcının başlangıç ve hedef düğümleri arasındaki tüm güzergâhlarını imha etmek için yol-kesicinin kullanacağı en kısa güzergâh bulunmaya çalışılmaktadır. Problemin çözümü için dal-sınır yöntemi kullanılarak bir algoritma geliştirilmiştir.
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    Article
    Evaluation of Robust Evacuation Strategies for Resilient Urban Infrastructure Through Microscopic Traffic Simulation
    (Univ Studi Trieste, Ist Studio Trasporti integrazione Econ Europea-Istiee, 2025) Kabarcık, Ahmet; Qadri, Syed Shah Sultan Mohiuddin; Qadri, Shah Sultan Mohiuddin; Athar, Ambreen Ilyas; Albdairi, Mustafa; Kabarcik, Ahmet; Endüstri Mühendisliği
    Natural disasters are a global threat, highlighting the urgent need for effective disaster management systems worldwide. Many countries, both developed and developing, are not adequately prepared, emphasizing the importance of governmental action. Key to disaster management is the creation of specialized disaster management units that develop and implement rapid response plans for potential risks. A crucial aspect of disaster management is evacuation-the process of moving vulnerable populations to safer areas. However, evacuations face challenges such as timely alert issuance, traffic congestion, resident reluctance to evacuate, and potential damage to transportation infrastructure. These challenges can be mitigated through comprehensive evacuation plans that ensure smooth relocation to shelters. This paper addresses these issues by developing and evaluating traffic routing conditions in an evacuation study area using the microscopic simulator SUMO. It examines two algorithms, Dijkstra and A-star (A*), which optimize vehicle routes under different network conditions. By focusing on criteria such as Minimum Travel Time and Maximum Number of Evacuations (clearance time), the research aims to improve disaster response and resilience. The objective is to enhance evacuation procedures, thereby strengthening disaster management and ensuring the safety of affected populations. Results show that the A* algorithm outperforms Dijkstra, reducing travel times by up to 18% and network clearance times by up to 6.8% under optimal conditions. The Manhattan-based network design further enhances evacuation efficiency, reducing average waiting time by up to 35% compared to the actual map.
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    Conference Object
    Intelligent and Energy-Aware Task Scheduling in Cloud Systems
    (Springer Science and Business Media Deutschland GmbH, 2025) Böke, K.N.; Qadri, S.S.S.M.; Kabarcik, A.
    The rapid advancement of information technologies has significantly reshaped industrial operations and daily life, leading to a growing demand for responsive and scalable digital services. Among the technologies addressing this growing need, cloud computing has emerged as a foundational infrastructure for delivering on-demand computing resources over the internet. However, its increasing adoption presents complex challenges such as managing dynamic workloads and minimizing virtual machine (VM) usage costs. Therefore, cloud service providers aim to optimize performance and reduce the operational costs of VMs by integrating intelligent scheduling algorithms. In response to this need, the present study explores the use of algorithms, particularly focusing on machine learning driven approaches, to enhance the sustainability and efficiency of cloud systems. Specifically, the study investigates the effectiveness of reinforcement learning through Q-learning for optimizing task scheduling against the traditional Round Robin (RR) scheduling algorithm. The primary objective is to evaluate their performance in minimizing VM usage costs within dynamic and continuously evolving cloud environments. Experimental results indicate that in reducing costs, Q-learning outperforms RR with a 33.14% improvement, demonstrating its superior adaptability and cost efficiency under varying conditions. These insights highlight the potential of reinforcement learning to enable intelligent and cost-aware scheduling strategies in modern cloud computing systems. © 2025 Elsevier B.V., All rights reserved.