TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/4

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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.
The 2-Adic Valuation of Shifted Padovan and Perrin Numbers and Applications
(Tubitak Scientific & Technological Research Council Turkey, 2024) Bravo, Eric; Irmak, Nurettin
We characterize the 2-adic valuation of (Pn - 1) n >= 0 , where ( P n ) n >= 0 denotes the Padovan sequence. In addition, we use this formula to find all the Cullen and Proth numbers that are Padovan numbers. We also fully describe the 2-adic order of (Rn + 1) n >= 0 , where ( R n ) n >= 0 denotes the Perrin sequence, and use it to find all Woodall and Proth numbers of the second kind which are Perrin numbers. As a consequence we find that 3 , 5 , 9 , and 65 are the only Fermat numbers in the Padovan sequence; while 3 and 7 and 2 and 5 are the only numbers of Mersenne and Th & acirc;bit ibn Kurrah in the Perrin sequence respectively.
Citation - Scopus: 4
Analysis of the Studies Done on Laboratories in Turkey
(Ekip Buro Makineleri A., 2020) Yener, Dündar; Köklü, Niğmet; Yamaç, Ramazan Ziya; Yalçın, Seher
The aim of this study is to determine the trend of studies in the laboratory and put the current situation in Turkey. For this purpose, document analysis technique, one of the qualitative research methods, was used in the research. The data group of the research consists of thesis studies on laboratories in our country between 1999-2017. Theses in the fields of science, physics, chemistry, and biology have been determined and themes and sub-themes have been created through the keywords of these theses. Then, frequency tables were created according to the themes and sub-themes created. According to the findings obtained, it was seen that the traditional laboratory approach and inquiry-based laboratory approaches are compared in the studies. It was determined that the studies were done on physics subjects and it was determined that complementary measurement and evaluation studies performed for performance evaluation were used in very few numbers. In addition, it was concluded that the keywords did not give enough information about the studies. In this context, it can be suggested to examine the effectiveness of these approaches according to each other and experiment types by examining the approaches in which students can be more active in laboratories. © 2020. All Rights Reserved.
Citation - WoS: 11
Citation - Scopus: 19
Artificial Intelligence in Healthcare Competition (teknofest-2021): Stroke Data Set
(AVES, 2022) Koç, U.; Sezer, E.A.; Özkaya, Y.A.; Yarbay, Y.; Taydaş, O.; Ayyıldız, V.A.; Bahadır, Murat
Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a non-disclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflect-ing various cases and problems. Especially, annotated data set by domain experts is more valuable. © 2022, AVES. All rights reserved.
Citation - WoS: 6
Citation - Scopus: 7
Automated Elimination of Eog Artifacts in Sleep Eeg Using Regression Method
(2019) Dursun, Mehmet; Özşen, Seral; Güneş, Salih; Akdemir, Bayram; Yosunkaya, Şebnem
Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine Faculty of Necmettin Erbakan University. A dataset consisting of 58 h and 6941 epochs was used in the research. Then, in order to see the consequences of this process, we classified pure sleep EEG and artifact-eliminated EEG signals with artificial neural networks (ANN). The results showed that elimination of EOG artifacts raised the classification accuracy on each subject at a range of 1%– 1.5%. However, this increase was obtained for a single parameter. This can be regarded as an important improvement if the whole system is considered. However, different artifact elimination strategies combined with different classification methods for another sleep EEG artifact may give higher accuracy differences between original and purified signals.
Citation - WoS: 3
Citation - Scopus: 4
Cnn Based Sensor Fusion Method for Real-Time Autonomous Robotics Systems
(Tubitak Scientific & Technical Research Council Turkey, 2022) Yıldız, Berat; Durdu, Akif; Kayabaşı, Ahmet; Duramaz, Mehmet
Autonomous robotic systems (ARS) serve in many areas of daily life. The sensors have critical importance for these systems. The sensor data obtained from the environment should be as accurate and reliable as possible and correctly interpreted by the autonomous robot. Since sensors have advantages and disadvantages over each other they should be used together to reduce errors. In this study, Convolutional Neural Network (CNN) based sensor fusion was applied to ARS to contribute the autonomous driving. In a real-time application, a camera and LIDAR sensor were tested with these networks. The novelty of this work is that the uniquely collected data set was trained in a new CNN network and sensor fusion was performed between CNN layers. The results showed that CNN based sensor fusion process was more effective than the individual usage of the sensors on the ARS.
Citation - Scopus: 1
Comparison of Ml Algorithms To Distinguish Between Human or Human-Like Targets Using the Hog Features of Range-Time and Range-Doppler Images in Through-The Applications
(Scientific and Technological Research Council Turkey, 2022) Acar, Yunus Emre; Saritas, İsmail; Yaldız, Ercan
When detecting the human targets behind walls, false detections occur for many systematic and environmental reasons. Identifying and eliminating these false detections is of great importance for many applications. This study investigates the potential of machine learning (ML) algorithms to distinguish between the human and human-like targets behind walls. For this purpose, a stepped-frequency continuous-wave (SFCW) radar has been set up. Experiments have been carried out with real human targets and moving plates imitating a regular breath of a healthy human. Unlike conventional methods, human and human-like returns are classified using range-Doppler images containing range and Doppler information. Then, the histogram of oriented gradients (HOG) features of the range-Doppler images are extracted, and the number of these features is reduced by principal component analysis (PCA). Finally, popular ML algorithms are executed to distinguish the human and human-like returns. The performances of the ML algorithms are compared for both range-time and range-Doppler images with or without HOG features. Experiments have indicated that the HOG features of the range-Doppler profiles provide the best results with the support vector machine (SVM) classifier with an accuracy of 93.57%.
Citation - WoS: 2
Citation - Scopus: 3
Consensus-Based Virtual Leader Tracking Algorithm for Flight Formation Control of Swarm Uavs
(Tubitak Scientific & Technological Research Council Turkey, 2024) Yıldız, Berat; Durdu, Akif; Kayabaşı, Ahmet
Technological developments in industrial areas also impact unmanned aerial vehicles (UAVs). Recent improvements in both software and hardware have significantly increased the use of many UAVs in social and military fields. In particular, the widespread use of these vehicles in social areas such as entertainment, shipping, transportation, and delivery and military areas such as surveillance, tracking, and offensive measures has accelerated the research on swarm systems. This study examined the previous investigations on swarm UAVs and aimed to create a more efficient algorithm. The effectiveness of the proposed algorithm was compared with other leader -based applications. A swarm consisting of 5 UAVs scattered throughout the environment was directed to a fixed altitude using a gathering algorithm. Afterward, a virtual leader was added to the swarm and moved toward the target point by maintaining the flight formation with the consensus -based virtual leader tracking algorithm (CBVLTA). Unlike leader -based applications, where leader or member failure is not taken into account, here, in the event that a random number of UAVs crash and their communication is broken in different scenarios, a new formation shape is created and a flight is made to the target point. The swarm performs the determined formation flight with an error rate below 2% throughout its movement. If the error rate equals or exceeds 2%, push -and -pull functions are applied between members and the error is reduced below 2%. Thus, the results show that the proposed algorithm allows robust and flexible swarm structures against the distortions in topology caused by external factors. In this way, swarm applications such as area coverage, target tracking or detection, collision avoidance, and defense or attack can be performed.
Citation - WoS: 4
Citation - Scopus: 3
Determining Highway Slope Ratio Using a Method Based on Slope Angle Calculation
(2021) Yılmaz, Osman Salih; Özkan, Gülgün; Kılıç, Batuhan
Geographic Information System (GIS) is a vital tool used in numerous areas related to natural science and engineering studies. Managing complex data and obtaining accurate results from the analysis are essential functions of GIS. It is also efficiently used in highway designing both in project and application phases. This study proposes a new calculation method of slope angles to determine the suitable slope modal of a road by using topographic and geological datasets in a GIS environment. Using this method in the preparation phase of the project enables a more accurate calculation of earthwork volume. The proposed method was applied to a highway to prove this idea. The selected road is a significant tertiary of which project was completed by the Turkish General Directorate of Highways. In this study, the calculated values of the project were considered as references. Comparing both results obtained from the proposed method and application project, the accuracy of the slope modal of the proposed method is 71%, and the accuracy of its earthwork volume is 99%. The proposed approach will enable project managers and designers to determine more reliable earthwork volume during project feasibility studies without any application in the field.
Citation - WoS: 13
Citation - Scopus: 16
Developing a Mobile Gis Application Related To the Collection of Land Data in Soil Mapping Studies
(2021) İşcan, Fatih; Güler, Erkan
Soil is the one of the most important natural resources having direct and indirect effects on human life, foremost on food supply. Moreover, two of the most important sources used in many sectors such as agriculture, forestry, environment, planning, related to the conservation of soil, are soil maps and soil reports. There is a need for proper and up-to-date soil maps produced with support of technology to achieve sustainable management of land and soil successfully. In order to facilitate the collection of soil data on site in a correct, fast and reliable way in soil survey and mapping studies, and therefore to produce soil maps with high accuracy, a mobile GIS application called “Soil Data System” was developed in this study to work in mobile devices which are driven by Android 5.0 and above. The developed application was tested in “Abadan Erosion Control Project” implementation area, which is located in the boundaries of Ankara, Turkey and was completed in 2016 within the General Directorate of Forestry of The Republic of Turkey, and various findings were obtained after comparing the application with classical soil survey and mapping studies. As a result of the analyzes, it was observed that Soil Data System contributed positively to soil mapping process in many aspects such as accuracy, transparency and time.
Digital Documentation of a Byzantine Rock-Carved Structure in Konya: Kiriakon Monastery, Sille
(Tubitak Scientific & Technological Research Council Turkey, 2025) Mimiroğlu, İlker; Karabörk, Hakan; Makineci, Hasan Bilgehan; Yaldız, Esra
Digital documentation methods enable the acquisition of viable data to preserve cultural heritage and its accurate transfer to future generations. Unmanned aerial vehicle (UAV) and terrestrial laser scanning (TLS) equipment are technical tools that have essential functions in the three-dimensional (3D) modeling of structures of historical importance. In the geographic referencing and visualization of historical structures with their surroundings, UAVs are often used, and for the visualization of the interior spaces of structures as a whole, TLS is popular. The Kiriakon Monastery, a rock-carved, cross-in-square, four-supports simple provincial type of church, and several different structures around it within the borders of Sille in Konya Province are investigated in this study. As one of the essential rock-carved monasteries in the Lycaonia region, the Kiriakon Monastery, which was designed around a courtyard and features spaces carved into the rock, is one of the most important Middle Byzantine period structures that have survived to the present day in Sille. Although both time and external factors have damaged the church section of the monastery, it reveals the richness of Byzantine period rock-carved church decoration designs with rich relief decorations. The Kiriakon Monastery, which is being increasingly damaged with time, does not have smooth geometric surfaces and consists of rock-carved spaces, and so documentation studies cannot be performed reliably with classical measurement methods. Instead, the Kiriakon Monastery can be evaluated as an important part of the historical cultural heritage of Anatolia, mainly containing rock-carved spaces from the Byzantine period, through documentation based on measurements obtained with the help of technological methods that are subsequently transferred to a digital environment. In this study, the digital documentation of Sille’s Kiriakon Monastery was georeferenced using UAV technology. Interior 3D models of the structure were created from TLS data and then integrated with the interior and exterior surface data.
Citation - WoS: 6
Citation - Scopus: 7
The Effect of Snowfall and Icing on the Sustainability of the Power Output of a Grid-Connected Photovoltaic System in Konya, Turkey
(2019) Anadol, Mehmet Ali; Erhan, Erman
When the module surface is covered with various factors such as snow and icing which prevent the solar irradiance from reaching the photovoltaic cells, the power production of the system and its performance decrease. The purpose of this study is to determine the energy production losses of a grid-connected photovoltaic plant due to snowfall and icing. The effect of snowfall and icing has been examined on a photovoltaic system consisting of a hybrid inverter with two separate maximum power point tracking inputs and 36 monocrystalline modules, which are mounted on the supporting system horizontally in the south direction and at a constant tilt angle of 30 ? . The plant area is located in a priority and snowy region (Konya,Turkey) where large-scale photovoltaic system installations are carried out. In order to evaluate the effect of snowfall, the minute resolution data of the hybrid inverter which provides connection to the grid is used. The change over time of the power generated by the two arrays of the plant was examined comparatively. For comparison, one of the arrays was continuously cleared. The recorded data was used to determine the expected energy output of the array covered with snow. Besides, the solar irradiance and ambient temperature data obtained from the meteorological station were used to accurately identify and evaluate the effects of snowfall with digital images recorded in the site area. The results showed that surface clearing of modules had a significant positive effect on the power output of the system. In the array entirely covered with snow, the daily energy loss exceeds 93%. In months of heavy snowfall, the monthly energy loss is 18% depending on time of being covered with snow of the modules. When the production data of 2017 and 2018 is evaluated, it is seen that the total energy loss of the plant varies between 1% and 2%.
Effect of Yucca Schidigera Spraying in Different Litter Materials on Some Litter Traits and Breast Burn of Broilers at the Fifth Week of Production
(2013) Erdem, Evren; Kocakaya, Afsin; Ünal, Necmettin; Onbaşılar, Esin Ebru; Torlak, Emrah
Bu çalışma farklı altlık materyallerine değişik dozlarda ilave edilen Yucca schidigeranın üretimin 5. haftasında bazı altlık özellikleri (nem, pH, amonyak, toplam koloni sayısı, Enterobakteri ve maya-küf düzeyleri) ile etçi piliçlerde göğüs yanıkları üzerine etkisini belirlemek amacıyla yapılmıştır. Bu çalışmada toplam 432 adet bir günlük yaşta erkek civciv (ROSS 308) kullanılmıştır. Çalışmada civcivler 170x94x90 cm (genişlik x uzunluk x yükseklik) boyutlarındaki bölmelerin her birinde 12 adet olacak şekilde yerleştirilmiştir. Altlık materyali olarak bölmelerin yarısında talaş diğer yarısında pirinç kavuzu kullanılmıştır. Her altlık grubu denemenin 2. haftasından itibaren her hafta altlığa 0, %4 ve %8 Yucca schidigera püskürtülecek şekilde 3 eşit alt gruba ayrılmıştır. Kullanılan altlık malzemelerinin ve altlığa değişik düzeylerde Yucca schidigera ilavesinin üretimin 5. haftasında incelenen altlık özellikleri ile etçi piliçlerde göğüs yanıklarını etkilemediği görülmüştür (P>0.05).
Citation - WoS: 4
Citation - Scopus: 5
Evaluating the Attributes of Remote Sensing Image Pixels for Fast K-Means Clustering
(2019) Sağlam, Ali; Baykan, Nurdan Akhan
Clustering process is an important stage for many data mining applications. In this process, data elements are grouped according to their similarities. One of the most known clustering algorithms is the k-means algorithm. The algorithm initially requires the number of clusters as a parameter and runs iteratively. Many remote sensing image processing applications usually need the clustering stage like many image processing applications. Remote sensing images provide more information about the environments with the development of the multispectral sensor and laser technologies. In the dataset used in this paper, the infrared (IR) and the digital surface maps (DSM) are also supplied besides the red (R), the green (G), and the blue (B) color values of the pixels. However, remote sensing images come with very large sizes (6000 × 6000 pixels for each image in the dataset used). Clustering these large-size images using their multiattributes consumes too much time if it is used directly. In the literature, some studies are available to accelerate the k-means algorithm. One of them is the normalized distance value (NDV)-based fast k-means algorithm that benefits from the speed of the histogram-based approach and uses the multiattributes of the pixels. In this paper, we evaluated the effects of these attributes on the correctness of the clustering process with different color space transformations and distance measurements. We give the success results as peak signal-to-noise ratio and structural similarity index values using two different types of reference data (the source images and the ground-truth images) separately. Finally, we give the results based on accuracy measurement for evaluating both the success of the clustering outputs and the reliability of the NDV-based measurement methods presented in this paper.
Citation - WoS: 6
Citation - Scopus: 6
Hplc-Uv Analysis of Phenolic Compounds and Biological Activities of Padina Pavonica and Zanardinia Typus Marine Macroalgae Species
(Scientific And Technological Research Council Turkey, 2023) Keskinkaya, Hatice Banu; Deveci, Ebru; Yılmaz Altınok, Bahar; Gümüş, Numan Emre; Okudan Aslan, Emine Şükran; Akköz, Cengiz; Karakurt, Serdar
The marine macroalgae species are recognized as the food of the future with excellent bioactive properties. This study aimed to investigate phenolic compounds; cytotoxic, antibacterial, and antioxidant activities; total phenolic (TPC) and flavonoid (TFC) contents of the methanol, ethanol, and water extracts of Padina pavonica (PP) and Zanardinia typus (ZT). To the best of our knowledge, this is the first report in which ZT was evaluated in terms of phenolic content, antioxidant, antibacterial, and cytotoxic activities. The HPLC analysis allowed the identification of five phenolic compounds containing rutin (0.31 +/- 0.04 - 3.00 +/- 0.21 ppm) in all extracts and trans- p-coumaric acid (0.15 +/- 0.02 - 3.95 +/- 0.02 ppm) in MPP, EPP, MZT, EZT, WZT as the major compounds. TPC and TFC of the extracts were calculated as 11.78 +/- 0.75 - 76.78 +/- 0.54 mu g GAEs/mg extract and 6.78 +/- 0.17 - 29.50 +/- 2.23 mu g QEs/mg extract, respectively. The highest cytotoxicity was observed in EZT (CC50: 132.3 +/- 22.4 mu g/mL) against MCF-7 and MZT (CC50: 91.4 +/- 20.9 mu g/mL) against MIA PaCa-2. Among the studied extracts, EPP showed the best antibacterial activity against all test pathogens. Also, EPP indicated superior antibacterial activity against Plesiomonas shigelloides (MIC: 1.25 mg/mL) and Staphylococcus aureus (MIC: 1.25 mg/mL). EZT displayed the highest antioxidant activity in DPPH center dot (IC50: 49.03 +/- 0.28 mu g/mL), CUPRAC (A0.50: 15.20 +/- 0.14 mu g/mL), and ABTS center dot+ (IC50: 18.86 +/- 0.74 mu g/mL) assays. The results approved that P. pavonica and Z. typus marine macroalgae species could be valued as natural sources of bioactive agents for food and pharmacology applications.
Citation - WoS: 3
Citation - Scopus: 3
Key Drivers of Volatility in BISTt100 Firms Using Machine Learning Segmentation
(Ramazan YAMAN, 2025) Yildirim, Hasan Hüseyin; Akusta, Ahmet
This study conducts a comprehensive volatility analysis among firms listed on the BIST100 index using machine learning techniques and panel regression models. Focusing on the period from 2006 to 2023, the study excludes financial firms, resulting in a dataset of 46 companies. The methodology follows a two-step process: First, firms are clustered into low and high-volatility groups using Principal Component Analysis (PCA) and the K-means algorithm; second, panel regression models are applied to determine the financial ratios influencing stock price volatility. The Parkinson Volatility measure is used as the dependent variable, while independent variables include Return on Assets (ROA), Return on Equity (ROE), liquidity ratios, firm beta, and leverage ratios. Results indicate that firm beta has a statistically significant positive impact on volatility across all models, while the current ratio negatively affects volatility in the model 1. These findings provide valuable insights for investors and policymakers regarding risk management in the Turkish stock market. Applying machine learning and advanced econometric techniques adds to the literature on volatility forecasting and financial decision-making. © 2025 Elsevier B.V., All rights reserved.
Citation - WoS: 4
Citation - Scopus: 4
Medical Image Fusion With Convolutional Neural Network in Multiscale Transform Domain
(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2021) Abas, Asan İhsan; Koçer, Hasan Erdinç; Baykan, Nurdan Akhan
Multimodal medical image fusion approaches have been commonly used to diagnose diseases and involve merging multiple images of different modes to achieve superior image quality and to reduce uncertainty and redundancy in order to increase the clinical applicability. In this paper, we proposed a new medical image fusion algorithm based on a convolutional neural network (CNN) to obtain a weight map for multiscale transform (curvelet/ non-subsampled shearlet transform) domains that enhance the textual and edge property. The aim of the method is achieving the best visualization and highest details in a single fused image without losing spectral and anatomical details. In the proposed method, firstly, non-subsampled shearlet transform (NSST) and curvelet transform (CvT) were used to decompose the source image into low-frequency and high-frequency coefficients. Secondly, the low-frequency and high-frequency coefficients were fused by the weight map generated by Siamese Convolutional Neural Network (SCNN), where the weight map get by a series of feature maps and fuses the pixel activity information from different sources. Finally, the fused image was reconstructed by inverse multi-scale transform (MST). For testing of proposed method, standard gray-scaled magnetic resonance (MR) images and colored positron emission tomography (PET) images taken from Brain Atlas Datasets were used. The proposed method can effectively preserve the detailed structure information and performs well in terms of both visual quality and objective assessment. The fusion experimental results were evaluated (according to quality metrics) with quantitative and qualitative criteria.
Citation - WoS: 7
Citation - Scopus: 4
Multivariate Statistical Analysis Application To Determine Factors Affecting the Parcel Value To Be Used Mass Real Estate Valuation Approaches
(SELCUK UNIV PRESS, 2022) Yalpır, Şükran; Ünel, Fatma Bünyan
Real estate is a form of immovable asset that enables individuals to exert their property rights and provides a form of material guarantee through its economic value. The economic value of real estate, which is reflected in the price, is an aspect that all countries emphasize today by identifying purchase and sale values in market conditions that are removed from large project rumors and speculations. The more the real estate market value is removed from reality, the more negative its effect will be on the cost-benefit of real estate management. This study aimed to identify the main criteria that affect parcel value, which constitutes a basis for real estate, narrow them down to the optimum level using questionnaires and standardize them. A total of 559 experts working in real estate valuation and 1,915 members of the public that play a role in real estate purchases and sales were contacted in Ankara, Konya and Kayseri, all of which are located in the Central Anatolia Region of Turkey. The factor analysis method was applied to the survey data. Grouping was carried out with 10 factors and the results were interpreted.
Citation - WoS: 10
Citation - Scopus: 11
A New Color Distance Measure Formulated From the Cooperation of the Euclidean and the Vector Angular Differences for Lidar Point Cloud Segmentation
(2021) Sağlam, Ali; Baykan, Nurdan Akhan
Two important features of the points in the LiDAR point clouds are the spatial and the color features. The spatial feature is mostly used in the point cloud processing field due to its 3D informative and distinctive characteristic. The local geometric difference derived from the spatial features of the points is usually benefited by graph-based point cloud segmentation methods, because the geometric features of the local point groups are highly distinctive. In this paper, we use both the geometric and color differences of the adjacent local point groups at the impact rates 0.3, 0.5, and 0.7 and cooperate the Euclidean and the vector color differences within several averaging techniques for the color difference. The difference forms have been tested within a graph-based segmentation method on four point cloud segmentation datasets, two indoor and two outdoor, using their spatial and color information. The geometric mean as an averaging techniques increases the segmentation success for the all datasets except one outdoor when the color differences are used in the segmentation at the impact rate 0.3, while the harmonic mean increases the success for the all datasets the successes except the other outdoor at the same impact rate. According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.
Citation - WoS: 10
Citation - Scopus: 12
A Novel Map-Merging Technique for Occupancy Grid-Based Maps Using Multiple Robots: a Semantic Approach
(2019) Durdu, Akif; Korkmaz, Mehmet
Map merging is a noteworthy phenomenon for cases such as search and rescue and disaster areas in which the duration is quite significant when gathering information about an environment. It is obvious that the total mapping time decreases if the number of agents (robots) increases. However, the use of multiple agents leads to problems such as task allocation schemes and the fusing of local maps. Examining the present methods, it is generally observed that the common features of local maps have been found and the global map is formed by obtaining related transformation between local maps. However, such implementations may be risky when local maps have symmetrical areas. Hence, a novel and semantic approach has been developed to solve this problem. The developed method counts on the reliability level of feature points. If relevant feature points are trusted, local maps are merged according to the best point or points. The simulation results from a robot operating system and a real-time experiment support the proposed method’s efficiency, and mapping can be performed even for environments that have symmetrical similar parts and the task time can thus be reduced.

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