WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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

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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.
Accurate Edge Detection With Support of Reflectance Transformation Imaging
(Institute of Electrical and Electronics Engineers Inc., 2022) Kaya, Burhan; Durdu, Akif
Reflectance Transformation Imaging (RTI) is a method of photographing an object that reveals details that are invisible to the naked eye. The input of RTI consists of a series of images captured by a fixed positioned camera and each illuminated from a known and different direction by lights. Reflection Transform Imaging is widely used to produce quality models from multi-light image data. It is frequently preferred for various studies in the field of cultural heritage. For the first time in this paper, the RTI photographing method has been used outside of its traditional using way. It is used to solve the well-known problem of edge detection. Reflection transform cannot be used actively, because it is difficult to create an RTI experimental environment in daily life. However, under certain conditions, the approaches mentioned in this paper will be used in daily life, from the analysis of images in every field. In this paper, the ideas that it can be applied in every partially controlled area that needs high resolution object detection are discussed. With the method mentioned in this statement, a new approach has been proposed and proven based on RTI basics for edge and corner detections. © 2022 IEEE.
Citation - WoS: 3
Citation - Scopus: 4
Achievable Rate Analysis for Two-Way Relay Non-Orthogonal Multiple Access Systems
(IEEE, 2021) Özdemir, Özgür
This paper investigates the performance of a non-orthogonal multiple access (NOMA) based two-way relaying system where the users want to exchange independent messages with the help of a decode-and-forward relay. We consider transmission over three phases where the first and second phases are allocated to the transmissions of the users and after detection the relay applies superposition coding and transmits the network encoded symbol to the users in the third phase. Exact analytical expressions are derived to characterize the achievable average rate of the system over independent Rayleigh fading channels. Computer simulations are also presented to confirm the theoretical analysis. Analytical and simulation results show that the proposed three-phase two-way relaying scheme with NOMA outperforms the two-phase and four-phase NOMA-based two-way relaying scenarios in terms of achievable average rate.
Citation - Scopus: 1
Achievable Rate of Noma-Based Cooperative Communication Systems With Best Relay Selection Over Cascaded Rayleigh Fading Channels
(IEEE, 2020) Özdemir, Özgür
In this paper, the achievable rate analysis of NOMA-based cooperative communication systems with best relay selection is studied. The cascaded Rayleigh fading channels are considered since investigations have shown that cascaded channel structure agree better with mobile network models such as inter-vehicular communication systems. A cooperative network where a source terminal communicates with a destination directly and through a selected relay among K relays is considered and the achievable average rate of this system using NOMA is found by computer simulations. The obtained results for cascaded Rayleigh fading channels in case of decode and forward protocol have shown that the average rate is decreased as the cascading degree increases. It has been also seen that for a given cascading degree the average rate performance of the system is increased when the number of total relays is increased.
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.
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.
Aligning Objects as Preprocessing Combined With Imitation Learning for Improved Generalization
(Institute of Electrical and Electronics Engineers Inc., 2024) Barstugan, Mucahid; Masuda, Shimpei; Sagawa, Ryusuke; Kanehiro, Fumio
Imitation learning method transfers human behavior to the robots or machines. This method aims to allow robots or machines to learn by observing tasks performed by human operators and imitating these tasks, rather than direct programming. ACT as an imitation learning method shows the high capability for automating dexterous manipulation tasks. From the viewpoint of industrial application, pose of the target object will be varied. However, even if only for the initial object pose variation, imitation learning method like ACT usually needs a lot of demonstration data that covers pose variation to train the policy that can generalize for. Collecting large demonstration dataset takes many efforts. This study created an object pick-and-place controller to eliminate pose variation as a preprocess step with YOLOv8, which is a recent object detection technique. The preprocess step automatically moves the object to a specific position and eliminates the pose variation. We show that our system effectiveness on the randomly placed bag opening task that requires both generalization for object pose variation and dexterous bimanual manipulation. The bag opening task was conducted with ACT and preprocess applied ACT methods, and the results were evaluated to examine the effect of the preprocess method to generalization process.
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.
Citation - WoS: 8
Citation - Scopus: 10
Analysis and Design of a Transimpedance Amplifier Based Front-End Circuit for Capacitance Measurements
(SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Demirtaş, Mehmet; Erişmiş, Mehmet Akif; Güneş, Salih
In this study, transimpedance amplifier based front-end circuits which can be employed to measure small capacitances were designed, analyzed and simulated using analog electronic circuit simulator. The front-end circuit converts the current flowing through the measured capacitance into a modulated voltage value which contains information regarding the desired capacitance. The frequency-domain, time-domain, stability and noise analyzes were carried out numerically and in simulation environment using a circuit simulator. The analytical, numerical and simulation results can be used to design optimized, precise and stable transimpedance amplifiers with low-noise value. The measured capacitance value was 10 pF which is low enough to simulate various real-world applications. Three commercially available, off-the-shelf operational amplifiers with different peripheral passive components were employed for computer based analysis. The designed transimpedance amplifiers are suitable to connect with capacitance extraction circuits which use analog or digital demodulation techniques.
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.
Citation - WoS: 27
Citation - Scopus: 28
Analysis of the Effect of Land Consolidation Projects in Terms of Land Fragmentation and Parcel Shapes: the Case of Konya, Turkey
(SPRINGER HEIDELBERG, 2020) Ertunç, Ela
Agricultural land fragmentation and irregularity of parcel shapes are a structural land management problem that prevents the development of modern agriculture. Januszewski (JI) and Simmons (SI) indices are widely used to determine agricultural land parcel fragmentation. Shape index (SHI) and fractal dimension (FD) are also commonly used to evaluate parcel shapes. In this study, it is aimed to evaluate the land consolidation project in terms of fragmentation and change of parcel shapes. The purpose of land consolidation projects is to ensure the optimum use of the land and to obtain maximum benefit. The success level of land consolidation projects should be evaluated. Therefore, changes of land parcel fragmentation and parcel shape changes before and after the LC were analyzed using candir District project data. In addition, the effects of LC project on parcel shape and size, the effect on parcel boundaries, and the effect of change of the distance between parcels on fuel saving and changes due to irrigation were investigated. As a result, according to Januszewski and Simmons indices, the ratio of farm enterprise whose index values were less than 0.40 was 1.17% and 3.7% before the LC, respectively, and decreased to 0.6% and 2.3% after the LC. The obtained values showed that the land parcel fragmentation decreased in candir District. In addition, the ratio of farm enterprises whose SHI were greater than 1.60, which implies non-uniform geometric parcel shapes, was 6.5% before the LC, but this ratio decreased to 5.8% after the LC. While the ratio of farm enterprises having parcels, which were non-uniform according to FD values, was 1.6% before the LC, this rate decreased to 0.9% after the LC. These results show that JI and SI indexes can be used in land consolidation projects. In addition, SHI and FD indices are generally parameters that can measure how close the parcel shapes are to the smooth geometric shapes, but they do not give successful results in each parcel. Finally, according to the results of the survey conducted with the farmers, the LC provided a significant economic gain to farm enterprise owners in this region.
Citation - WoS: 3
Citation - Scopus: 1
Analysis of the Effects of Adding Pyroclastic Rock To Red Mud for the Production of a Baked Building Material in Terms of Its Resistance To Frost Actions
(SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Dereli, Mustafa; Tosun, Mustafa
In the aluminum sector, approximately 130 million tons of waste red mud was produced in the last year. Such a high amount of wastes causes their storage areas to become a threat to the environment. Numerous studies have been conducted in the literature to eliminate this environmental threat. However, it is observed that these studies are mostly conducted on only a part of the waste, and there are few studies on the whole consumption of waste. Due to the said lack in the literature, it is thought that this waste can be utilized as a baked building material and consumed systematically. However, according to the literature and previous studies, it is observed that the use of the waste alone will not produce a quality building material. Therefore, otiose pyroclastic rocks were included within the scope of the study to be used together with the waste material. Accordingly, micronized pyroclastic rocks obtained from different regions were added to red mud at the proportions of 10, 20, 30, 40, and 50% by weight. Bentonite of 3% was added to mixtures to prevent capillary cracks. As a result of the preliminary experiments conducted on the baked building material samples obtained with the above-mentioned mixture ratios, two mixture types from each region with the highest compressive strength were selected. Physical and mechanical experiments were conducted on the samples to determine the resistance to frost actions-as an outer ambient condition with the most destructive effect-of the baked building materials (especially brick, etc. with the widest area of usage in outer walls and surface cover materials such as ceramic, clinker pavement, etc.) which will be obtained with these mixtures. As a result of the study, the optimum mixture type was determined to be the sample formed by adding 10% volcanic tuff around campus to red mud and baking it at a temperature of 1050 degrees C. Furthermore, different mixture types formed by adding the micronized pyroclastic material from other regions could reach sufficient values in terms of both compressive strength and frost actions. According to the results of the study, this material will fulfill the need for raw material as a building material resistant to frost actions and used in outer masonry. In this study, a process was obtained to eliminate a potential environmental problem, and a contemporary building material intended to be used as a sustainable building material was produced.
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.
Citation - WoS: 2
Apneic Events Detection Using Different Features of Airflow Signals
(MEHRAN UNIV ENGINEERING & TECHNOLOGY, 2019) Göğüş, Fatma Zehra; Tezel, Gülay
Apneic-event based sleep disorders are very common and affect greatly the daily life of people. However, diagnosis of these disorders by detecting apneic events are very difficult. Studies show that analyzes of airflow signals are effective in diagnosis of apneic-event based sleep disorders. According to these studies, diagnosis can be performed by detecting the apneic episodes of the airflow signals. This work deals with detection of apneic episodes on airflow signals belonging to Apnea-ECG (Electrocardiogram) and MIT (Massachusetts Institute of Technology) BIH (Bastons's Beth Isreal Hospital) databases. In order to accomplish this task, three representative feature sets namely classic feature set, amplitude feature set and descriptive model feature set were created. The performance of these feature sets were evaluated individually and in combination with the aid of the random forest classifier to detect apneic episodes. Moreover, effective features were selected by OneR Attribute Eval Feature Selection Algorithm to obtain higher performance. Selected 28 features for Apnea-ECG database and 31 features for MIT-BIH database from 54 features were applied to classifier to compare achievements. As a result, the highest classification accuracies were obtained with the usage of effective features as 96.21% for Apnea-ECG database and 92.23% for MIT-BIH database. Kappa values are also quite good (91.80 and 81.96%) and support the classification accuracies for both databases, too. The results of the study are quite promising for determining apneic events on a minute-by-minute basis.
Citation - WoS: 3
Application of Abm To Spectral Features for Emotion Recognition
(MEHRAN UNIV ENGINEERING & TECHNOLOGY, 2018) Demircan, Semiye; Örnek, Humar Kahramanlı
ER (Emotion Recognition) from speech signals has been among the attractive subjects lately. As known feature extraction and feature selection are most important process steps in ER from speech signals. The aim of present study is to select the most relevant spectral feature subset. The proposed method is based on feature selection with optimization algorithm among the features obtained from speech signals. Firstly, MFCC (Mel-Frequency Cepstrum Coefficients) were extracted from the EmoDB. Several statistical values as maximum, minimum, mean, standard deviation, skewness, kurtosis and median were obtained from MFCC. The next process of study was feature selection which was performed in two stages: In the first stage ABM (Agent-Based Modelling) that is hardly applied to this area was applied to actual features. In the second stageOpt-aiNET optimization algorithm was applied in order to choose the agent group giving the best classification success. The last process of the study is classification. ANN (Artificial Neural Network) and 10 cross-validations were used for classification and evaluation. A narrow comprehension with three emotions was performed in the application. As a result, it was seen that the classification accuracy was rising after applying proposed method. The method was shown promising performance with spectral features.
Citation - WoS: 2
Application of Digital Urban Memory Transmission Model for Sustainability of Cultural Heritage
(2023) Ata, İlknur Acar; Başar, Mehmet Emin
In this study, a model for preserving and maintaining the memory value of cultural heritage was put forward and the application of the model was made in a designed digital environment. The model can be applied in teaching, transferring and keeping the memory values carried by tangible and intangible cultural heritage values of the world. Digital Cultural Heritage Memory Model (DCHMM) aims to transfer and interpret the urban memories of the settlements to a wide audience on the web and thus raising awareness on the protection and maintenance of these values. The cultural heritage values of the sample villages selected for the application of the model are aimed to be realized with the participation of the interactive user in the transfer of verbal, written and architectural memory values, the interpretation in the asking questions and giving ideas section, and the success of the application in the questioning section. Nine historical buildings that must be preserved and transferred to memory in three settlements of Niğde with a common historical past and that stand out with a variety of building types with significant urban memory value were selected for the field study. To provide data for the digital environment that evaluates DCHMM’s applicability, the urban memory values (written-verbal-architectural memory elements) collected during the field study in the selected sample (three exchanged villages in Niğde- Yeşilburç Village, Uluağaç Village, Hançerli Village) were digitized according to the model’s information, participation and questioning sections. 452 users from different age and occupation groups made the application of the model in the web environment in a six-month period. The digitized values obtained as a result of the study were interpreted in line with the targets in the sections of the model, and the model was brought to the literature with its application.

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