WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/2

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Citation - WoS: 5
Citation - Scopus: 4
12th June 2017 Offshore Karaburun-Lesvos Island Earthquake Coseismic Deformation Analysis Using Continuous Gps and Seismological Data
(SCIENTIFIC TECHNICAL RESEARCH COUNCIL TURKEY-TUBITAK, 2021) Yıldız, Hasan; Çırmık, Ayça; Pamukçu, Oya; Özdağ, Özkan Cevdet; Gönenç, Tolga; Kahveci, Muzaffer
Understanding the tectonic mechanism generated by the earthquakes and faults is possible only if the preseismic, coseismic and postseismic crustal deformation related to the earthquakes is determined properly. By the analysis of continuous GPS (CGPS) coordinate time series, it is possible to estimate the crustal deformation. Besides, accelerometer records at strong motion stations (SMSs) may support the CGPS-based estimates. In this study, CGPS coordinate time series were analyzed in comparison with the accelerometer records for clarifying the coseismic deformation caused by the earthquake occurred in the surrounding of Lesvos fault located in the northern part of Karaburun within the active mechanism that controls the area where the earthquakes occurred during June 2017 on the offshore Karaburun. The activity of this fault continued throughout June 2017 until the time when the main shock (12th June 2017, M-W = 6.2) occurred. We analyzed CGPS coordinate time series of AYVL and CESM and DEUG stations to determine the coseismic deformation due to the offshore Karaburun-Lesvos Island earthquake using the empirical mode decomposition (EMD) method. Besides, the EMD method results were compared with the accelerometer records obtained from the SMSs close to the CGPS stations and CGPS-based results were found to be consistent with the accelerometer records. Additionally, the horizontal displacements were calculated by Coulomb 3.3 software using different focal plane solutions and compared with CGPS-based results. Consequently, it is suggested an integrated use of CGPS and strong motion accelerometer networks for the joint assessment of the crustal deformation and for the cost-effective use of existing observation networks as well as for the establishment of future observation networks at lower cost.
3b T1 Ağırlıklı Mr Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi
(Gazi Univ, 2022) Öziç, Muhammet Üsame; Ekmekci, Ahmet Hakan; Özşen, Seral; Barstuğan, Mücahid; Yıldoğan, Aydın Talip
Alzheimer Hastalığı yaşlılık ile beraber başlayan bir beyin hastalığıdır. Hastalığın teşhisi, takibi ve ilgili beyin bölgelerinin ölçümleri yüksek çözünürlüklü üç boyutlu yapısal manyetik rezonans görüntüleri ile yapılabilmektedir. Bu çalışmada, OASIS veri tabanından alınan 70 Alzheimer 70 Normal 3B T1 ağırlıklı MR görüntüleri üzerinde 116 subkortikal bölgenin hacimsel ölçümünü yapabilecek atlas tabanlı bir hacim ölçüm ve sınıflandırma modeli tasarlanmıştır. Ölçülen değerler her bir denekte gri madde, parankim, total beyin hacmi ile bölünerek normalizasyon işlemi yapılmıştır. Böylece ham ölçülen değerler dahil olmak üzere 140x116 matris boyutlu 4 farklı veri kümesi elde edilmiştir. Veri kümeleri entropi, t-test, roc, Bhattacharyya, Wilcoxon özellik derecelendirme yöntemleri ile en anlamlı özellikten en anlamsız özelliğe doğru derecelendirilmiştir. Derecelendirilen veriler her döngüde sırasıyla birleştirilmiş, lineer ve rbf kernel kullanan destek vektör makinelerine 10-kat çapraz geçerleme ile verilerek sınıflandırma işlemi yapılmıştır. Tüm senaryolar analiz edilerek, en az özellikle en iyi sonucu veren küme, özellik derecelendirme ve sınıflandırma metodu ortaya konulmuştur. Normalizasyon ve özellik derecelendirme yöntemlerinin sınıflandırma sonucuna etkisi incelenmiştir. Deneysel işlemler sonucunda roc özellik derecelendirme tabanlı lineer destek vektör makinesi, total beyin hacmi normalizasyonlu 107 özellik kullanarak %95.71 hassasiyet, %94.29 özgüllük, %95.00 doğruluk, 0.95 eğri altında kalan alan değerleri ile en yüksek oranları vermektedir.
A 3d U-Net Based on Early Fusion Model: Improvement, Comparative Analysis With State-Of Models and Fine-Tuning
(Konya Teknik Univ, 2024) Kayhan, Beyza; Uymaz, Sait Ali
Multi-organ segmentation is the process of identifying and separating multiple organs in medical images. This segmentation allows for the detection of structural abnormalities by examining the morphological structure of organs. Carrying out the process quickly and precisely has become an important issue in today's conditions. In recent years, researchers have used various technologies for the automatic segmentation of multiple organs. In this study, improvements were made to increase the multi-organ segmentation performance of the 3D U-Net based fusion model combining HSV and grayscale color spaces and compared with state-of-the-art models. Training and testing were performed on the MICCAI 2015 dataset published at Vanderbilt University, which contains 3D abdominal CT images in NIfTI format. The model's performance was evaluated using the Dice similarity coefficient. In the tests, the liver organ showed the highest Dice score. Considering the average Dice score of all organs, and comparing it with other models, it has been observed that the fusion approach model yields promising results.
Citation - WoS: 1
Academic Text Clustering Using Natural Language Processing
(2022) Taşkıran, Fatma; Kaya, Ersin
Accessing data is very easy nowadays. However, to use these data in an efficient way, it is necessary to get the right information from them. Categorizing these data in order to reach the needed information in a short time provides great convenience. All the more, while doing research in the academic field, text-based data such as articles, papers, or thesis studies are generally used. Natural language processing and machine learning methods are used to get the right information we need from these text-based data. In this study, abstracts of academic papers are clustered. Text data from academic paper abstracts are preprocessed using natural language processing techniques. A vectorized word representation extracted from preprocessed data with Word2Vec and BERT word embeddings and representations are clustered with four clustering algorithms.
Citation - WoS: 1
Accuracy Assessment Toward Merging of Terrestrial Laser Scanner Point Data and Unmanned Aerial System Point Data
(2023) Karasaka, Lütfiye; Erdal, Kasım; Makineci, Hasan Bilgehan
Terrestrial Laser Scanning (TLS) techniques are widely preferred for 3D models of small and large objects, buildings, and historical and cultural heritages. However, sometimes relying on a single method for 3D modelling an object/structure is insufficient to arrive at a solution or meet expectations. For example, Unmanned Aerial Systems (UAS) provide perspective for building roofs, while terrestrial laser scanners provide general information about building facades. In this research, several facades of a selected building could not be modelled using terrestrial laser scanning, and UAS was used to complete the missing data for 3D modelling. The transformation matrix, a linear function, is created to merge different data types. In the transformation matrix, the scale was found to be 1:1.012. The accuracy analysis of the produced 3D model was also made by comparing the spatial measurements taken from different building facades and the differences in the measurement values obtained from the 3D model and calculating statistically. According to the accuracy analysis results, the Root Mean Square Error (RMSE) value is approximately 3 cm. The results of the accuracy research, which are within the 95% confidence interval with the three-sigma rule, are approximately 2 cm as RMSE. As a result of the study, it was determined that the data obtained from UAV photogrammetry and the data obtained by the TLS technique could be combined, and the integrated 3D model obtained can be used more efficiently.
An Adaptive and Hybrid State of Charge Estimation Method Integrating Sequence-To Learning and Coulomb Counting for Li-Ion Based Energy Storage Systems
(Konya Teknik Univ, 2025) Cımen, Halıl
For safe and long-lasting operation of Li-ion batteries used in electric vehicles and electric grid applications, the State of Charge (SOC) of the battery cell must be estimated with high accuracy. However, due to the uncertainty in environmental conditions and the complex nature of battery chemistry, SOC estimation still presents a significant challenge. In this study, an adaptive and hybrid method for SOC estimation of a Li-ion battery cell is proposed. Convolutional Neural Network (CNN) based Sequence-to-point learning architecture is used to estimate the initial SOC values at specific time intervals. In order to increase the estimation accuracy, a multi-scale CNN architecture is designed, and useful features are captured. The obtained estimation values are integrated with the partial coulomb counting method to increase the accuracy. In addition, the proposed model adaptively updates the estimation weights with the help of the estimation error data obtained during the full charging of the batteries. The proposed model is tested on the LG 18650HG2 dataset. The results prove that the proposed model is 23% more accurate than benchmark models at 25°C and 55.5% more accurate at 0°C.
Citation - WoS: 1
Citation - Scopus: 2
Adaptive State Feedback Control Method Based on Recursive Least Squares
(Kauno Technologijos Universitetas, 2022) Levent, Mehmet Latif; Aydoğdu, Ömer
This study revealed an adaptive state feedback control method based on recursive least squares (RLS) that is introduced for a time-varying system to work with high efficiency. Firstly, a system identification block was created that gives the mathematical model of the time-varying system using the input/output data packets of the controller system. Thanks to this block, the system is constantly monitored to update the parameters of the system, which change over time. Linear quadratic regulator (LQR) is renewed according to these updated parameters, and self-adjustment of the system is provided according to the changed system parameters. The Matlab/Simulink state-space model of the variable loaded servo (VLS) system module was obtained for the simulation experiments in this study; then the system was controlled. Moreover, experiments were carried out on the servo control experimental equipment of the virtual simulation laboratories (VSIMLABS). The effectiveness of the proposed new method was observed taking the performance indexes as a reference to obtain the results of the practical application of the proposed method. Regarding the analysis of simulation and experimental results, the proposed approach minimizes the load effect and noise and the system works at high efficiency. © 2022 Kauno Technologijos Universitetas. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 2
Adaptive Traffic Management Model for Signalised Intersections
(Kauno Technologijos Universitetas, 2024) Yalcinli, F.; Akdemir, B.; Durdu, A.
As population increases, one of the factors affecting life is traffic. Efficient traffic management has a direct positive impact on issues such as time, carbon dioxide emissions, and fuel consumption. Today, an important parameter under the heading of traffic is the signalling systems for intersections, which are operated with fixed-time, semi-actuated, fully actuated, and fully adaptive control methods. In this study, an adaptive traffic management model is developed for signalised intersections. The adaptive traffic management model developed includes phase extension with minimum and maximum time intervals dependent on density and phase skip features. Additionally, the most distinctive feature of the model is its flexible phase structure rather than a sequential phase. The Heybe intersection, located within the boundaries of Antalya province, is modelled one-to-one in the simulation of urban mobility (SUMO) simulation programme with real intersection data. The developed adaptive traffic management model is applied to the Heybe intersection, and the effects of the model are revealed. Improvements obtained from the SUMO simulation programme were verified through visual inspection, and high-accuracy results were determined. As a result of the studies, it was found that the application of the adaptive traffic management model developed at Heybe intersection, which has approximately 50,000 vehicles passing daily, resulted in a 27.2% improvement in the average delay per vehicle parameter, a 32.4% improvement in the average waiting time per vehicle parameter, and a 16.7 % improvement in the average speed per vehicle parameter. © 2024 Kauno Technologijos Universitetas. All rights reserved.
Aerial Lidar and Imaging Based Earth Surface Digitization and Data Characteristics Comparison
(Sciendo, 2024) Altuntas, C.
The land topography and urban area digitization in the form of point clouds has become an indispensable method for providing many related services. Aerial point cloud measurements are made using active LiDAR or dense matching photogrammetry methods. Aerial LiDAR and dense image matching point clouds are obtained directly in the geodetic coordinate system thanks to navigation data. The geo-referencing based on ground control points require more labour and work time. All kinds of geometric and semantic information about the terrain can be extracted from the point cloud data. Therefore, it should have both location and visualization accuracy. The detection and definition accuracies of image area details depend on the scanning point density and its uniform distribution. In this study, after having been introduced the parameters of the aerial point cloud related to topographic measurement and urban area modelling, a comparison of these two source point clouds was made in areas with different land cover. The registration of a dense matching point cloud into a geospatial reference system was done with flight data and LiDAR measurements. As consequence, The LiDAR point density depends on the min angular step of the instrument scanning light, while the dense matching is relating to ground sampled distance of pixels.
Analysing the Impact of Urban Growth on Agricultural Lands Using Sleuth Model and Google Earth Engine
(Konya Teknik Univ, 2024) Karasaka, Lutfiye; Gunes, Murat
In this study, it is aimed to determine the urban growth in the Sel & ccedil;uklu district of Konya, which is the study area with the SLEUTH model based on cellular automata, which is widely used in the modeling of urban growth and land use, and to examine the effect of urbanization on agricultural areas in the near future. In addition to the simulations carried out for the years 2030 and 2050 starting from 2015, which was determined as the last control year in the model, the simulation results of the year 2022 were compared with the terrain classes obtained from the Google Earth Engine (GEE) controlled classification of the 2022 Landsat satellite image. As a result of the creation of simulation models for the years 2030 and 2050, it was concluded that 10428.75-23747.49 hectares of agricultural land will be destroyed, respectively. The SLEUTH model has modeled a total of 56468.26 hectares of agricultural land for 2022. This corresponds to 95% of the classification result for 2022, which is an important factor in examining the accuracy of the model. This study, which aims to guide decision makers and planners, shows that the use of the SLEUTH model has for the examination of future land use.
Analysis and Design of a Permanent Magnet Linear Synchronous Motor Based on Inductance Calculation
(Polska Akad Nauk, Polish Acad Sciences, 2025) Yucel, Enes; Mutluer, Mumtaz; Cunkas, Mehmet
This paper presents a comprehensive design and analysis methodology for a Permanent Magnet Linear Synchronous Motor (PMLSM), with a focus on evaluating different inductance modeling approaches. The motor design begins with analytical dimensioning based on defined design parameters. A two-dimensional finite element analysis follows this in ANSYS Maxwell to verify magnetic saturation, back-EMF, flux linkage, and electromagnetic performance under full load conditions. The inductance parameters are calculated using both conventional and look-up table (LUT) based models. In the conventional model, seven different methods are tested under static and dynamic conditions, as well as in non-salient and salient scenarios, and their results are compared. In the LUT model, current-dependent inductance values are extracted from flux linkage maps. The motor designed in Maxwell, along with the calculated inductance data, is integrated into a dynamic cooperative simulation (co-sim) model controlled by an inverter in Simplorer to analyze the thrust force. The results show that the LUT model provides outputs that are closer to the co-sim reference than the traditional model. Furthermore, performance curves based on the Maximum Torque Per Ampere strategy are generated, and the force-speed and power-speed characteristics derived from both inductance models are compared. The findings emphasize the importance of accurate inductance modeling in capturing the actual electromagnetic behaviour of PMLSM under dynamic operating conditions.
Analysis of Performance Coefficients in Maximum Electrical Power Extraction From Stand-Alone Wind Energy Conversion System
(2022) Dursun, Emre Hasan
Increasing performance and improving efficiency in maximum power extraction from Wind Energy Conversion Systems (WECS) is a quite important research topic. Today, in the large-scale WECS, it is widely aimed to extract the maximum mechanical power from the wind turbine using the Maximum Power Point Tracking (MPPT) unit. Similarly, it can also be targeted to achieve maximum mechanical power in small-scale WECS applications. However, losses occur in structural subsystems and electrical subunits located in WECS. Due to these losses, the overall system efficiency decreases and the characteristic of the system is also affected. The operation of these systems can also be performed via maximum electrical output power extraction, which is one of the most up-to-date ideas. Thus, the overall WECS rather than the wind turbine can be optimally controlled. Eventually, maximum electrical power tracking (MEPT) based designs can provide higher power extraction with higher efficiency than MPPT-based ones. In this paper, considering the system operating concepts with MPPT and MEPT for a stand-alone Permanent Magnet Synchronous Generator (PMSG) based WECS, the changes in performance coefficients at defined focus points in terms of system efficiency are evaluated. Technical and theoretical comparative analyzes are also made for each specific wind speed between 8m/s and 12m/s.
Analysis of Spatial and Temporal Variability of Aerosol Optical Depth Over Karabuk Using Modis
(2023) Arıkan, Duygu; Yıldız, Ferruh
The concept of aerosol refers to the combination of microscopic solid or liquid particles present in the atmosphere along with a mixture of gases. These particles are suspended in the air at different sizes and are evaluated based on their ability to scatter or absorb light, which is quantified through a measurement known as aerosol optical depth. These particles' quantities are determined using specialized devices, commonly referred to as "aerosol optical depth meters" or "optical thickness meters." Additionally, through remote sensing technology, aerosol optical depth can also be measured via satellites. In this study, aerosol optical depth has been examined temporally and spatially in the Karabük province for 2022. For this aim, data from National Air Quality Monitoring Stations (NAQMS) situated nationwide was employed, along with MODIS satellite images. Data from five stations in Karabük province, namely Kardemir1, Kardemir2, Tören Alanı, 75.yıl, and Safranbolu, were used for temporal analysis, while satellite imagery was used for spatial analysis. The relationship between aerosol optical depths derived from MODIS satellite data using green and blue band information and station data was investigated. As a result, a 99% positive correlation was found between the two bands obtained from the MODIS satellite, and a significant correlation was observed between ground-based particulate matter 2.5(PM2.5) and particulate matter 10 (PM10) data. Data from the Tören Alanı station, which had a higher amount of data (357 days) compared to other stations, was used to determine this correlation. It was found that there was an 86.35% positive correlation among particulate matters. A moderate correlation was also identified between ground-based data and aerosol optical depth obtained from satellite imagery.
Analyzing the Impact of the 2023 General Elections on Land Prices Using Machine Learning: a Case Study in Çanakkale, Turkey
(Konya Teknik Univ, 2025) Yalpır, Sükran; Genç, Levent Genc; Yucebas, Sait Can; Doğan, Simge
This study analyses the impact of the general elections to be held on 14 May 2023 on the real estate market in Turkey. The aim of the study is to develop a model to predict land unit prices (₺/m²) by analysing land prices, exchange rates and gold values observed before (February-March-April) and after (May-June-July) elections for Ayvacık, Bayramiç, Biga, Çan, Eceabat, Ezine, Gelibolu, Lapseki, Merkez and Yenice districts of Çanakkale province. Daily fluctuations in foreign exchange and gold values, which are the main economic parameters in the study, were recorded during the election period. The findings of this research, which predicts price movements in the property market using machine learning methods such as regression trees, reveal that unit prices of land generally tend to increase with increases in exchange rates, but in some districts where gold prices increase, the unit price shows a reverse trend. This is attributed to the fact that investors prefer gold as a safer asset in times of economic uncertainty. The results obtained can help investors and buyers to predict future trends in property prices, as well as contribute to the development of economic policies by experts to stabilise fluctuations in investment instruments.
Application of the Bees Algortihm Upon Hydraulic Cylinder Design and Optimization
(2022) Sözen, Betül; Kalyoncu, Mete; Şahin, Ömer Sinan
In this study, mass minimization of a simple double-acting hydraulic cylinder has been studied using The Bees Algorithm (BA) for a specific force and known material, considering the buckling and pressure constraints. A Hydraulic cylinder is a hydraulic actuator that creates linear movement by converting hydraulic energy back to a mechanical movement. Hydraulic-driven working machines are widespread in the industry today. Hydraulic cylinders are used in mobile applications such as container lifting devices, excavators, dump trucks, loaders, graders and dozers. Weight reduction in these cylinders plays a fundamental role in the performance of the machine in terms of lifting capacity, speed, costing, etc. The Bees Algorithm is a metaheuristic algorithm that mimics the natural foraging behavior of honey bees to find the optimum solutions. The advantages over other algorithms are its ability to search both locally and globally and being applicable for several optimization problems with the chance to be integrated with other algorithms. In this study, it is also aimed to determine the optimal parameters of the bees algorithm for minimum computation cost.
Citation - WoS: 3
Application of the Shrinking-Core Models for Determination of Dissolution Kinetics of Mn and Zn From Spent Zinc-Carbon Battery Powder in Organic Acid Solution
(SCIENDO, 2019) Ağaçayak, Tevfik; Taner, Hasan Ali
In this study, dissolution kinetics of manganese and zinc from spent zinc-carbon batteries in acetic acid solution was investigated. To determine the kinetics of dissolution of manganese and zinc, shrinking core model was applied to dissolution recoveries obtained at different temperatures. As a result of kinetic studies, it was determined that manganese and zinc were dissolved in acetic acid solution by diffusion from product layer and then activation energies (Ea) were calculated.
Citation - WoS: 2
Citation - Scopus: 2
An Approach To Determine Pathological Breast Tissue Samples With Free-Space Measurement Method at 24 Ghz
(WILEY, 2024) Toprak, Rabia; Gultekin, Seyfettin Sinan; Kayabasi, Ahmet; Çelik, Zeliha Esin; Tekin, Fatma Hicret; Uzer, Dilek
Pathology is an important branch of science in the diagnosis and treatment of several diseases. In cancer diseases, serious investigations have been made about the course of the diseases. A report that is essential for both the patient and the doctor is prepared by the pathologists as a result of a detailed cellular examination. These reports contain information about the disease. Access duration to these reports, which affects the form and duration of the treatment, is extremely important today. It is possible to shorten this period with systems using antenna technologies. The pathological breast tissue samples have been examined by using horn antenna structures with high gain in this study. Dual identical horn antennas have been placed opposite each other as receivers and transmitters in the measurement setup at 24 GHz. Measurements of normal and cancerous breast tissues have been made, and the normalization process has been applied to the measured scattering parameters. The different values between normal and cancerous breast tissues have been shown with this process. The normalized values are compared with other analyzed values. According to the results obtained, the percentage of normalized values for transmission is much more effective and meaningful than other results.
Citation - WoS: 3
Citation - Scopus: 4
Approaches To Automated Land Subdivision Using Binary Search Algorithm in Zoning Applications
(Ice Publishing, 2022) Koç, İsmail; Çay, Tayfun; Babaoğlu, İsmail
The planned development of urban areas depends on zoning applications. Although zoning practices are performed using different techniques, the parcelling operations that shape the future view of the city are the same. Preparing the parcelling plans is an important step that has a direct impact on ownership structure and reallocation. Parcelling operations are traditionally handled manually by a technician. This is a serious problem in terms of time and cost. In this study, by taking the zoning legislation, the production of a pre-land subdivision plan has been automatically performed for a region of Konya, which is one of the major cities in Turkey. The parcelling processes have been performed in three different ways: the first parcelling technique is parcelling with edge values, the second is parcelling with area values and the third is parcelling using both edge and area values together. For the entire parcelling process, the area of the parcel has been calculated using the Gauss method. Moreover, to effectively determine the boundaries and to calculate the parcel area in the parcelling process, the binary search technique has been used in all the methods. The experimental results show that the parcelling operations were carried out very quickly and successfully.
Citation - WoS: 24
Citation - Scopus: 23
Artificial Bee Colony Algorithm for Solving Multi-Objective Distributed Fuzzy Permutation Flow Shop Problem
(IOS Press BV, 2022) Baysal, Mehmet Emin; Sarucan, Ahmet; Büyüközkan, Kadir; Engin, Orhan
The distributed permutation flow shop scheduling (DPFSS) is a permutation flow shop scheduling problem including the multi-factory environment. The processing times of the jobs in a real life scheduling problem cannot be precisely know because of the human factor. In this study, the process times and due dates of the jobs are considered triangular and trapezoidal fuzzy numbers for DPFSS environment. An artificial bee colony (ABC) algorithm is developed to solve the multi-objective distributed fuzzy permutation flow shop (DFPFS) problem. First, the proposed ABC algorithm is calibrated with the well-known DPFSS instances in the literature. Then, the DPFSS instances are fuzzified and solved with the algorithm. According to the results, the proposed ABC algorithm performs well to solve the DFPFS problems. © 2022 - IOS Press. All rights reserved.
Citation - WoS: 2
Atlas-Based Segmentation Pipelines on 3d Brain Mr Images: a Preliminary Study
(EDUSOFT PUBLISHING, 2018) Öziç, Muhammet Üsame; Ekmekci, Ahmet Hakan; Özşen, Seral
Three dimensional structural MR imaging is a high-resolution imaging technique used in the detection and follow up of neurological disorders. Rigid changes in the brain are usually interpreted and reported manually by radiologists using MR images. The results of manual interpretation may vary with respect to the experts. At the same time, measurement and segmentation of the brain regions and the manual evaluation of the volume changes are a difficult process. With the increase of numerical methods, automated and semi-automated package programs have been developed for the analysis of brain measurements. These programs use electronic brain atlases or tissue probability maps. However, since the package programs have a lot of analysis time and give only certain outputs, they may be disadvantaged in the use of segmentation and measurement of brain regions. Hence, special pipelines are needed especially to obtain valuable features for artificial intelligence and classification studies. In this study, we propose pipelines to segment 3D certain brain regions, which will help to find the basic features such as volume changes, intensity variations, symmetry deteriorations, and tissue changes. With these pipelines, 3D segmentation of the brain regions defined in the atlas can be performed and normalized. It is aimed to use these studies as a preliminary study in order to quantitatively determine the basic changes in the brain by performing the volume of interest methods and to formulate a decision support system.

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