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Osman Ozkaraca
Muğla Sıtkı Koçman University

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Conference paper
Published: 06 July 2021 in Trends in Data Engineering Methods for Intelligent Systems
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One of the most serious problems is how the limited number of requencies (carriers) allocated to operators in broadband code division multiple access systems can be distributed to the base station cells in a way that captures the least interference value. This frequency planning is generally used for 2G network. In accordance with its technology, 3.5G systems are defined as interference independent. However, in this study, by applying GA algorithm for frequency planning in Wideband Code Division Multiple Access Systems (WCDMA-3.5G-3.75G) and by trying to obtain the best frequency plan, it was analyzed how the change occurred as a result of this planning. Actual system information was used in the study. Consequently, it was observed that GA decreased the fitness value of 268000 to 53999 levels according to the current plan working in the field with 1% mutation and 80% crossing rate and an improvement of 79% was achieved. Consequently, on the contrary of what is explained theoretically, it is seen that with a better frequency plan, better quality service continuity is provided in cases where interference is reduced.

ACS Style

Sencer Aksoy; Osman Özkaraca. Optimization in Wideband Code-Division Multiple Access Systems with Genetic Algorithm-Based Discrete Frequency Planning. Trends in Data Engineering Methods for Intelligent Systems 2021, 654 -668.

AMA Style

Sencer Aksoy, Osman Özkaraca. Optimization in Wideband Code-Division Multiple Access Systems with Genetic Algorithm-Based Discrete Frequency Planning. Trends in Data Engineering Methods for Intelligent Systems. 2021; ():654-668.

Chicago/Turabian Style

Sencer Aksoy; Osman Özkaraca. 2021. "Optimization in Wideband Code-Division Multiple Access Systems with Genetic Algorithm-Based Discrete Frequency Planning." Trends in Data Engineering Methods for Intelligent Systems , no. : 654-668.

Conference paper
Published: 06 July 2021 in Advances on P2P, Parallel, Grid, Cloud and Internet Computing
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In recent years swarm mentality algorithms are usually created with nature inspiration to keep their popularity. One of these optimization techniques is particle swarm optimization, and the other one is firefly algorithm. Firefly algorithm process is working with the lower light intensity directed to higher intensities principle. Particle swarm optimization based on the positions of individuals; swarm keeps following the individual who have great position. This article explains with mathematical testing functions of minimum international points, particle swarm optimization and firefly algorithm. Tried to specify that which function is working better with which algorithm. Also, this research tried to recognize that different parameters are changing the result or not.

ACS Style

Mervenur Demirhan; Osman Özkaraca; Ercüment Güvenç. Performance Analysis of Particle Swarm Optimization and Firefly Algorithms with Benchmark Functions. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2021, 643 -653.

AMA Style

Mervenur Demirhan, Osman Özkaraca, Ercüment Güvenç. Performance Analysis of Particle Swarm Optimization and Firefly Algorithms with Benchmark Functions. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2021; ():643-653.

Chicago/Turabian Style

Mervenur Demirhan; Osman Özkaraca; Ercüment Güvenç. 2021. "Performance Analysis of Particle Swarm Optimization and Firefly Algorithms with Benchmark Functions." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 643-653.

Research article
Published: 05 January 2021 in IET Signal Processing
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In recent years, data mining and algorithm-based methods have been used frequently for the prediction and diagnosis of various diseases. Traumas, being one of the significant health problems in the world, are also one of the most important causes of death. This study aims to predict the presence of traumatic pathology in the lung of the patients admitted to the emergency department due to blunt thorax trauma with no X-ray and computed tomography (CT) history by machine learning methods. The models developed in the study using the 5-fold cross-validation method are most accurately classified by the ensemble (voting) classifier, whether there is a pathology in X-ray (mean accuracy = 0.82) and CT (mean accuracy = 0.83). The K-nearest neighbourhood method classifies patients with pathology in X-ray by 83% accuracy, while the ensemble (voting) method classifies non-pathology patients by 94% accuracy in models. Of CT results, random forest, ensemble (voting), and ensemble (stacking) classifiers are precisely classified by 96%, while those patients with pathology are classified perspicuously by 77%. As a result, a mathematical framework using data mining methods was proposed based on estimating the X-ray and CT results for the thorax graph scan.

ACS Style

Abdulkadir Karaci; Osman Ozkaraca; Ethem Acar; Ahmet Demir. Prediction of traumatic pathology by classifying thorax trauma using a hybrid method for emergency services. IET Signal Processing 2021, 14, 754 -764.

AMA Style

Abdulkadir Karaci, Osman Ozkaraca, Ethem Acar, Ahmet Demir. Prediction of traumatic pathology by classifying thorax trauma using a hybrid method for emergency services. IET Signal Processing. 2021; 14 (10):754-764.

Chicago/Turabian Style

Abdulkadir Karaci; Osman Ozkaraca; Ethem Acar; Ahmet Demir. 2021. "Prediction of traumatic pathology by classifying thorax trauma using a hybrid method for emergency services." IET Signal Processing 14, no. 10: 754-764.

Journal article
Published: 15 February 2019 in Energy Conversion and Management
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The limited energy resources globally, low efficiency of renewable energies, complicated and costly energy conversion systems and environmental pollution have significantly increased scholar’s interest in innovative and efficient systems and their improvement studies. Therefore, it is necessary to increase the efficiency of power generation systems used in geothermal sources of medium or low enthalpy. This study aims to improve the thermodynamic performance of an existing binary geothermal system with organic Rankine cycle and its system components while trying to comprehend the physical events/changes during these improvement processes. A model has been developed that simulates the system completely and accurately. Seventeen system parameters which were considered as crucial to maximize the exergy efficiency of the system like turbine inlet, condenser temperature and so on, are optimized using a gravitational search algorithm. The results of the study show that the exergy efficiency of the system is 14% and thus it can be maximized to 31% with optimization. During the optimization process, the pressure of work fluid on the evaporator line is increased and thus 2.1 MW more power is produced compared to normal power production. The condenser, with the highest exergy destruction in the system, has performance improvements of 75%. As a result, with the optimization process, a more compatible operating strategy between system components is ensured. This will allow the system and its components to run for longer and without failures.

ACS Style

Osman Özkaraca; Ali Keçebaş. Performance analysis and optimization for maximum exergy efficiency of a geothermal power plant using gravitational search algorithm. Energy Conversion and Management 2019, 185, 155 -168.

AMA Style

Osman Özkaraca, Ali Keçebaş. Performance analysis and optimization for maximum exergy efficiency of a geothermal power plant using gravitational search algorithm. Energy Conversion and Management. 2019; 185 ():155-168.

Chicago/Turabian Style

Osman Özkaraca; Ali Keçebaş. 2019. "Performance analysis and optimization for maximum exergy efficiency of a geothermal power plant using gravitational search algorithm." Energy Conversion and Management 185, no. : 155-168.

Journal article
Published: 25 October 2017 in Energies
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Geothermal energy is a renewable form of energy, however due to misuse, processing and management issues, it is necessary to use the resource more efficiently. To increase energy efficiency, energy systems engineers carry out careful energy control studies and offer alternative solutions. With this aim, this study was conducted to improve the performance of a real operating air-cooled organic Rankine cycle binary geothermal power plant (GPP) and its components in the aspects of thermodynamic modeling, exergy analysis and optimization processes. In-depth information is obtained about the exergy (maximum work a system can make), exergy losses and destruction at the power plant and its components. Thus the performance of the power plant may be predicted with reasonable accuracy and better understanding is gained for the physical process to be used in improving the performance of the power plant. The results of the exergy analysis show that total exergy production rate and exergy efficiency of the GPP are 21 MW and 14.52%, respectively, after removing parasitic loads. The highest amount of exergy destruction occurs, respectively, in condenser 2, vaporizer HH2, condenser 1, pumps 1 and 2 as components requiring priority performance improvement. To maximize the system exergy efficiency, the artificial bee colony (ABC) is applied to the model that simulates the actual GPP. Under all the optimization conditions, the maximum exergy efficiency for the GPP and its components is obtained. Two of these conditions such as Case 4 related to the turbine and Case 12 related to the condenser have the best performance. As a result, the ABC optimization method provides better quality information than exergy analysis. Based on the guidance of this study, the performance of power plants based on geothermal energy and other energy resources may be improved.

ACS Style

Osman Özkaraca; Pınar Keçebaş; Cihan Demircan; Ali Keçebaş. Thermodynamic Optimization of a Geothermal- Based Organic Rankine Cycle System Using an Artificial Bee Colony Algorithm. Energies 2017, 10, 1691 .

AMA Style

Osman Özkaraca, Pınar Keçebaş, Cihan Demircan, Ali Keçebaş. Thermodynamic Optimization of a Geothermal- Based Organic Rankine Cycle System Using an Artificial Bee Colony Algorithm. Energies. 2017; 10 (11):1691.

Chicago/Turabian Style

Osman Özkaraca; Pınar Keçebaş; Cihan Demircan; Ali Keçebaş. 2017. "Thermodynamic Optimization of a Geothermal- Based Organic Rankine Cycle System Using an Artificial Bee Colony Algorithm." Energies 10, no. 11: 1691.

Conference paper
Published: 01 October 2017 in 2017 International Conference on Computer Science and Engineering (UBMK)
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Neuroendocrine tumors (NET) are a heterogeneous group of tumors that can develop in almost every localization of the body. The study is the computation of the Ki-67 proliferation index, which is an important information for physicians, from the NET images. The physician control evaluations were carried out on histopathologic forms taken from healthy and NET-diagnosed patients with the designed automated cell counting system performed. The pathological image analysis of the physician were operated for about 10 minutes while the performed system the average of 5 sec depending on resolution and density of the image. The obtained results were close to 98,71% when compared to the findings of the physician. It has been seen that the realized application has produced very short time and much more accurate in the analysis of these NET images than the analysis of the eyes.

ACS Style

Osman Ozkaraca; Yelda Dere; Gurcan Cetin; Musa Peker. A computer aided system for calculation of Ki-67 proliferation index. 2017 International Conference on Computer Science and Engineering (UBMK) 2017, 580 -585.

AMA Style

Osman Ozkaraca, Yelda Dere, Gurcan Cetin, Musa Peker. A computer aided system for calculation of Ki-67 proliferation index. 2017 International Conference on Computer Science and Engineering (UBMK). 2017; ():580-585.

Chicago/Turabian Style

Osman Ozkaraca; Yelda Dere; Gurcan Cetin; Musa Peker. 2017. "A computer aided system for calculation of Ki-67 proliferation index." 2017 International Conference on Computer Science and Engineering (UBMK) , no. : 580-585.

Journal article
Published: 16 December 2016 in Mugla Journal of Science and Technology
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ACS Style

Gürcan Çetin; Osman Özkaraca; Ercüment Güvenç; Murat Sakal. ALGORİTMALAR VE PROGRAMLAMAYA GİRİŞ İÇİN DİNAMİK İÇERİKLİ BİR E-KİTABIN GELİŞTİRİLMESİ. Mugla Journal of Science and Technology 2016, 2, 199 -199.

AMA Style

Gürcan Çetin, Osman Özkaraca, Ercüment Güvenç, Murat Sakal. ALGORİTMALAR VE PROGRAMLAMAYA GİRİŞ İÇİN DİNAMİK İÇERİKLİ BİR E-KİTABIN GELİŞTİRİLMESİ. Mugla Journal of Science and Technology. 2016; 2 (2):199-199.

Chicago/Turabian Style

Gürcan Çetin; Osman Özkaraca; Ercüment Güvenç; Murat Sakal. 2016. "ALGORİTMALAR VE PROGRAMLAMAYA GİRİŞ İÇİN DİNAMİK İÇERİKLİ BİR E-KİTABIN GELİŞTİRİLMESİ." Mugla Journal of Science and Technology 2, no. 2: 199-199.

Journal article
Published: 18 March 2015 in International Journal of Computer Applications
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ACS Style

Ercüment Güvenç; Osman Ozkaraca; Gürcan Çetin; Gürbüz Akçay. Development of a Web-based Decision Support System for Pediatric Patients. International Journal of Computer Applications 2015, 114, 15 -20.

AMA Style

Ercüment Güvenç, Osman Ozkaraca, Gürcan Çetin, Gürbüz Akçay. Development of a Web-based Decision Support System for Pediatric Patients. International Journal of Computer Applications. 2015; 114 (6):15-20.

Chicago/Turabian Style

Ercüment Güvenç; Osman Ozkaraca; Gürcan Çetin; Gürbüz Akçay. 2015. "Development of a Web-based Decision Support System for Pediatric Patients." International Journal of Computer Applications 114, no. 6: 15-20.