Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/11727/4809

Browse

Now showing 1 - 20 of 38

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.
Albumin to alkaline phosphatase ratio is not a predictor of survival in cervical cancer patients
(2021) Onal, C; Yavas, G; Guler, O.C
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.
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
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.
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.
Course Selection with AHP & PROMETHEE Methods for Post Graduate Students: An Application in Kirikkale University Graduate School of Natural and Applied Sciences
(2016) Bedir, Neset; Ozder, Emir Huseyin; Eren, Tamer; 0000-0002-1895-8060; AAU-1584-2021
Post Graduate Study has a very important role in people's career planning. Besides, it helps for gaining expertising on their fields. Specializing must be in the right area to reach their targets in line with people's interests. The aim of the study is to propose a model for students that determines which courses will be chosen on master course selection and this model gives an application example. In this study, course selection problem is discussed for post graduate students in Industrial Engineering Department of Kirikkale University. All criteria that are effected for selecting a course are identified by the help of survey then, significance levels are determined with using Analytical Hierarchy Process (AHP) which is a Multi-Criteria Decision Making Method. According to the weights which are determined before in AHP results, six classes weighted by PROMETHEE method in Industrial Engineering Department.
Covid-19 In Pediatric Nephrology Centers In Turkey
(2021) Gulleroglu, Kaan
Early Postoperative Pulmonary Complications Following Heart Transplantation
(2015) Pirat, Aras; Firat, A. Camkiran; Komurcu, O.; Zeyneoglu, P.; Turker, M.; Sezgin, A.
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.
Fatou type weighted pointwise convergence of nonlinear singular integral operators Depending on two parameters
(2016) Uysal, Gumrah; Serenbay, Sevilay Kirci; ABF-5851-2020
In this paper we present some theorems concerning existence and Fatou type weighted pointwise convergence of nonlinear singular integral operators of the form: (T(lambda)f)(x) =integral K-R(lambda)(t-x; f(t))dt, x is an element of R, lambda is an element of Lambda where Lambda not equal empty set is a set of non-negative indices, at a common generalized Lebesgue point of the functions f is an element of L-1,L-empty set (R) and positive weight function empty set. Here, L-1,L-empty set(R) is the space of all measurable functions for which vertical bar f/empty set vertical bar is integrable on R.
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.
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.
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.
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.
Integrating features for accelerometer-based activity recognition
(2016) Erdas, C.Berke; Atasoy, Isil; Acici, Koray; Ogul, Hasan; 0000-0003-3467-9923
Activity recognition is the problem of predicting the current action of a person through the motion sensors worn on the body. The problem is usually approached as a supervised classification task where a discriminative model is learned from known samples and a new query is assigned to a known activity label using learned model. The challenging issue here is how to feed this classifier with a fixed number of features where the real input is a raw signal of varying length. In this study, we consider three possible feature sets, namely time-domain, frequency domain and wavelet-domain statistics, and their combinations to represent motion signal obtained from accelerometer reads worn in chest through a mobile phone. In addition to a systematic comparison of these feature sets, we also provide a comprehensive evaluation of some preprocessing steps such as filtering and feature selection. The results determine that feeding a random forest classifier with an ensemble selection of most relevant time-domain and frequency-domain features extracted from raw data can provide the highest accuracy in a real dataset. (C) 2016 The Authors. Published by Elsevier B.V.
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.
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
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.
The LOFT mission concept - A status update
(2016) Inam, S.C.
The Large Observatory For x-ray Timing (LOFT) is a mission concept which was proposed to ESA as M3 and M4 candidate in the framework of the Cosmic Vision 2015-2025 program. Thanks to the unprecedented combination of effective area and spectral resolution of its main instrument and the uniquely large field of view of its wide field monitor, LOFT will be able to study the behaviour of matter in extreme conditions such as the strong gravitational field in the innermost regions close to black holes and neutron stars and the supra-nuclear densities in the interiors of neutron stars. The science payload is based on a Large Area Detector (LAD, > 8m(2) effective area, 2-30 keV, 240 eV spectral resolution, 1 degree collimated field of view) and a Wide Field Monitor (WFM, 2-50 keV, 4 steradian field of view, 1 arcmin source location accuracy, 300 eV spectral resolution). The WFM is equipped with an on-board system for bright events (e. g., GRB) localization. The trigger time and position of these events are broadcast to the ground within 30 s from discovery. In this paper we present the current technical and programmatic status of the mission.