Scopus İndeksli Açık & Kapalı Erişimli Yayınlar

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    Aintshop Production Line Optimization Using Response Surface Methodology
    (2007) Dengiz, Berna; Belgin, Onder; 0000-0001-6702-2608; K-1080-2019
    This paper deals with the problem of determining the optimum number of workstations to be used in parallel and workers at some stations using simulation optimization approach in a paint shop line of an automotive factory in Ankara, Turkey. In the optimization stage of the study Response Surface Methodology (RSM) is used to find the optimum levels of considered factors. Simulation model and optimization stage integration is used both to analyse the performance of the current paint shop line and determine the optimum working conditions, respectively, with reduced cost, time and effort.
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    Automated Tuberculosis Detection Using Pre-Trained CNN and SVM
    (2021) Oltu, Burcu; Guney, Selda; Dengiz, Berna; Agildere, Muhtesem
    Tuberculosis (TB) is a dreadfully contagious and life-threatening disease if left untreated. Therefore, early and accurate diagnosis is critical for treatment. Today, invasive, expensive, or time-consuming tests are performed for diagnosis. Unfortunately, accurate TB diagnosis is still a major challenge. In the proposed study, a decision support system that can automatically separate normal and TB chest X-ray (CXR) images is presented for objective and accurate diagnosis. In the presented methodology, first various data augmentation methods were applied to the data set, then pre-trained networks (VGG16, MobileNet), were employed as feature extractors from augmented CXR's. Afterward, the extracted features for all images were fed into a support vector machine classifier. In training process, 5-fold cross-validation was applied. As a result of this classification, it was concluded that TB can be diagnosed with an accuracy of 96,6% and an area under the ROC curve (AUC) of 0,99.
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    Clinical Outcomes Of Liver Transplantation For Patients Over 60 Years Old; A Single Center Experience
    (2021) Akdur, Aydincan; Karakaya, Emre; Soy, Ebru H. Ayvazoglu; Karakayali, Feza; Moray, Gokhan; Haberal, Mehmet; 0000-0002-0993-9917; 0000-0002-8726-3369; 0000-0002-3462-7632; AAC-5566-2019; AAA-3068-2021; AAJ-8097-2021
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    Comment On 'Quantum Gravity Evolution In The Hawking Radiation Of A Rotating Regular Hayward Black Hole'
    (2022) Azreg-Ainou, Mustaph
    We review the derivations and conclusions made in Ali et al. (2022), and show that the metric derived there is not a valid rotating solution yielding a flawed analysis throughout the paper. (C) 2022 Elsevier B.V. All rights reserved.
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    Comparative Study for Tuberculosis Detection by Using Deep Learning
    (2021) Karaca, Busra Kubra; Guney, Selda; Dengiz, Berna; Agildere, Muhtesem
    Tuberculosis (TB) is an infectious disease which becomes a significant health problem worldwide. Many people have been affected by this disease owing to deficiency of treatment and late or inaccuracy of diagnosis. Therefore, accurate and early diagnosis is the very major solution to checking and preventing the disease. A chest x-ray is a main diagnostic tool used to diagnose tuberculosis. This diagnostic method is limited by the availability of radiologists and the experience and skills of radiologists in reading x-rays. To overcome such a challenge, a computer-aided diagnosis (CAD) system is supposed for the radiologist to interpret chest x-ray images easily. In this study, a CAD system based upon transfer learning is developed for TB detection using Montgomery Country chest x-ray images. We used the VGG16, VGG19, DenseNet121, MobileNet, and InceptionV3 pre-trained CNN models to extract features automatically and used the Support Vector Machine (SVM) classifier to the detection of tuberculosis. Furthermore, data augmentation techniques were applied to boost the performance results. The proposed method performed the highest accuracy of 98.9% and area under the curve (AUC) of 1.00, respectively, with the DenseNet121 on augmented images.
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    Early Postoperative Acute Kidney Injury Among Pediatric Liver Transplant Recipients
    (2018) Sahinturk, Helin; Kundakci, Aycan; Zeyneloglu, Pinar; Gedik, Ender; Pirat, Arash; Haberal, Mehmet; 0000-0003-0159-4771; 0000-0002-7175-207X; 0000-0002-3462-7632; AAJ-1419-2021; AAH-7003-2019; ABI-2971-2020; AAJ-8097-2021
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    Early Postoperative Pulmonary Complications Following Heart Transplantation
    (2015) Pirat, Aras; Firat, A. Camkiran; Komurcu, O.; Zeyneoglu, P.; Turker, M.; Sezgin, A.
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    Estimating the COVID-19 Death Counts Using a Hesitant Fuzzy Linear Regression Depend on Race, Age and Location
    (2022) Dengiz, Asiye Ozge; Atalay, Kumru Didem
    The COVID-19 pandemic that has struck the world has caused social and economic problems in people's lives. Many countries are trying to reduce the impact of the pandemic by taking precautions to prevent the spread of the virus and reduce the number of deaths. Despite the precautions, many people have contracted the virus and a significant number have lost their lives. This suggests that some factors about the exact mechanism of how people contract the virus and get sick are still unclear. Therefore, many researchers wonder if there are other factors that make people more susceptible to the COVID-19 virus. In this study, hesitant fuzzy linear regression (HFLR) models are applied depending on variables such as age, race, and place of residence that are thought to influence COVID-19 deaths. HFLR provides an alternative approach to statistical regression for modeling situations with incomplete information. The models include input and output variables as hesitant fuzzy elements (HFEs). The relationship between the considered variables and the number of deaths is examined using data related to people living in different states of the United States. In addition, HFLR is used as an estimation model due to the uncertainty in the data obtained from the Centers for Disease Control and Prevention (CDC). The proposed HFLR models are used both to estimate COVID-19 deaths and to determine the effects of selected variables on COVID-19 deaths. In this way, countries can estimate the risk of death for individuals given these factors and determine what precautions to take for high-risk groups.
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    GaN-based Single Stage Low Noise Amplifier for X-band Applications
    (2022) Caglar, Gizem Tendurus; Aras, Yunus Erdem; Urfali, Emirhan; Yilmaz, Dogan; Ozbay, Ekmel; Nazlibilek, Sedat
    Source degenerated HEMTs are used to achieve good noise matching and better input return loss without degrading the noise figure and reducing the stability. This work presents an MMIC design for the frequency band of 8 -11 GHz by using HEMTs with source degeneration in 0.15 mu m GaN on SiC technology. All design work is done in the Advanced Design System. The LNA delivers more than 6.9 dB gain with better than 8.5 dB and 9.5 dB input and output return losses, respectively. In addition, the gain ripple is around 2.7 dB. The noise figure of the amplifier is achieved below 1.1 dB with P1dB of 17.2 dBm and %12.7 drain efficiency within the operating bandwidth at the bias conditions of 9 V/20 mA.
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    A Genetic Algorithm for the Redundancy Allocation Problem with Repairable Components
    (2022) Sahin, Merve Uzuner; Dengiz, Orhan; Dengiz, Berna
    The complexity of the structures of modern engineering systems in the communication and electronic fields, the demand for high reliable system evaluation methods has increased. While improving the system's reliability, the cost is also on the upswing. Redundancy allocation is important approach for designing telecommunication systems. The reliability is increased by increasing number of redundant components in a system. The Redundancy Allocation Problem (RAP) is the design of new systems with higher reliability using redundant components in parallel arrangement. This paper presents a genetic algorithm (GA) with discrete event simulation (DES) to solve RAP with repairable components. The promising proposed approach is illustrated and investigated by some RAP benchmark/test problems involving repairable systems.
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    A hybrid ant colony optimization approach for the design of reliable networks
    (2007) Dengiz, B.; Altiparmak, F.; Belgin, O.; 0000-0003-1730-4214; 0000-0001-6702-2608; AAF-7020-2021; K-1080-2019
    This paper presents a new solution approach, which is a hybridization of ant colony optimization (ACO) and simulated annealing (SA), called (h_ACO) to design of communication networks. The design problem is to find the optimal network topology where total cost is minimum and all-terminal reliability is not less-than a given level of reliability. The effectiveness of the h_ACO is investigated comparing its results with those obtained by SA and ACO, which are basic forms of the h_ACO, and also GAs given in the literature for the design problem. Computational results show that the h_ACO is an effective heuristic approach to design of reliable networks.
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    A Hybrid Simulated Annealing For A Multi-Objective Stochastic Assembly Line Balancing Problem
    (2008) Cakir, Burcin; Dengiz, Berna; Altiparmak, Fulya; Xia, GP; Deng, XQ; 0000-0003-1730-4214; AAF-7020-2021
    Asssembly line balancing is the problem of assigning tasks to the workstations, while optimizing one or more objectives without violating restrictions imposed on the line. In practice, task times may be random due to the worker fatigue, low skill levels, job dissatisfaction, poorly maintained equipment, defects in raw material, etc. When stochastic task times are taken consideration in assembly lines, balancing procedure is more complex due to the probability of incompleteness of stations times in a given cycle time. In this study, a multi-objective simulated annealing algorithm (m_SAA) is proposed for single-model, stochastic assembly line balancing problem with the aim of minimizing of smoothness index and total design cost. To obtain Pareto-optimal solutions, m_SAA implements tabu list and a multinomial probability mass function approach. The effectiveness of the proposed m_SAA is comparatively investigated using another SA using weight-sum approach on the test problems. Computational results show that m_SAA with multinomial probability mass function approach is more effective than SA with weight-sum approach in terms of quality of Pareto-optimal solutions.
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    An International Facility Design Project
    (2008) Lacksonen, Thomas; Dengiz, Berna
    This paper describes an international facilities design project for Manufacturing and Industrial Engineering students. American and Turkish engineering students collaborated to create and implement the re-design of a Turkish wheelchair manufacturing facility. The company needed engineering assistance to improve the efficiency and increase the capacity of their existing factory. Turkish Industrial Engineering students went on-site to collect data and draw the existing facility layout. American Manufacturing Engineering students analyzed the data and developed new layout designs. Four American students traveled to Turkey between semesters to implement the initial phases of their design. In the second semester, the Turkish students simulated the new layout to see the performance improvements, completing their project. Student learning outcomes were positive for both groups of students. The paper explains critical steps in identifying projects and partners. Lessons are shown about successes and shortcomings in planning, operating, and communicating with design teams across cultures.
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    Intervention to hepatic and pulmonary METastases in breast cancer patients: Prospective, multi-institutional registry study-IMET; Protocol MF 14-02
    (2022) Soran, Atilla; Ozbas, Serdar; Ozcinar, Beyza; Isik, Arda; Dogan, Lutfi; Senol, Kazim; Dag, Ahmet; Karanlik, Hasan; Aytac, Ozgur; Cakmak, Guldeniz Karadeniz; Dalci, Kubilay; Dogan, Mutlu; Sezer, Yavuz Atakan; Gokgoz, Mustafa Sehsuvar; Ozyar, Enis; Sezgin, Efe
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    A Local Search Heuristic with Self-tuning Parameter for Permutation Flow-Shop Scheduling Problem
    (2009) Dengiz, Berna; Alabas-Uslu, Cigdem; Sabuncuoglu, Ihsan
    In this paper, a new local search metaheuristic is proposed for the permutation flow-shop scheduling problem. In general, metaheuristics are widely used to solve this problem due to its NP-completeness. Although these heuristics are quite effective to solve the problem, they suffer from the need to optimize parameters. The proposed heuristic, named STLS, has a single self-tuning parameter which is calculated and updated dynamically based on both the response surface information of the problem field and the performance measure of the method throughout the search process. Especially, application simplicity of the algorithm is attractive for the users. Results of the experimental study show that STLS generates high quality solutions and outperforms the basic tabu search, simulated annealing, and record-to-record travel algorithms which are well-known local search based metaheuristics.
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    Nasal chondromesencymal hamartoma; a rare case report
    (2022) Ziadinov, E.; Yagci, S.; Akcay, E. Yilmaz; Mustafayeva, S.; Tepeoglu, M.; Ozdemir, B.H.; 0000-0002-7528-3557; X-8540-2019