WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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

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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.
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: 3
Citation - Scopus: 6
3t2fts: a Novel Feature Transform Strategy To Classify 3d Mri Voxels and Its Application on Hgg/Lgg Classification
(MDPI, 2023) Hajmohamad, Abdulsalam; Koyuncu, Hasan
The distinction between high-grade glioma (HGG) and low-grade glioma (LGG) is generally performed with two-dimensional (2D) image analyses that constitute semi-automated tumor classification. However, a fully automated computer-aided diagnosis (CAD) can only be realized using an adaptive classification framework based on three-dimensional (3D) segmented tumors. In this paper, we handle the classification section of a fully automated CAD related to the aforementioned requirement. For this purpose, a 3D to 2D feature transform strategy (3t2FTS) is presented operating first-order statistics (FOS) in order to form the input data by considering every phase (T1, T2, T1c, and FLAIR) of information on 3D magnetic resonance imaging (3D MRI). Herein, the main aim is the transformation of 3D data analyses into 2D data analyses so as to applicate the information to be fed to the efficient deep learning methods. In other words, 2D identification (2D-ID) of 3D voxels is produced. In our experiments, eight transfer learning models (DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet101, SqueezeNet, VGG19, and Xception) were evaluated to reveal the appropriate one for the output of 3t2FTS and to design the proposed framework categorizing the 210 HGG-75 LGG instances in the BraTS 2017/2018 challenge dataset. The hyperparameters of the models were examined in a comprehensive manner to reveal the highest performance of the models to be reached. In our trails, two-fold cross-validation was considered as the test method to assess system performance. Consequently, the highest performance was observed with the framework including the 3t2FTS and ResNet50 models by achieving 80% classification accuracy for the 3D-based classification of brain tumors.
A-Who: Stagnation-Based Adaptive Metaheuristic for Cloud Task Scheduling Resilient to DDoS Attacks
(MDPI, 2025) Kaplan, Fatih; Babalik, Ahmet
Task scheduling in cloud computing becomes significantly more challenging under Distributed Denial-of-Service (DDoS) attacks, as malicious workload injection disrupts resource availability and degrades Quality of Service (QoS). To address this issue, this study proposes an improved Wild Horse Optimizer (A-WHO) that incorporates a stagnation detection mechanism and a stagnation-driven adaptive leader perturbation strategy. The proposed mechanism dynamically applies a noise-guided perturbation into the stallion position only when no improvement is observed over a predefined threshold, enabling A-WHO to escape local optima without modifying the standard behavior of WHO in normal iterations. In addition, a DDoS-aware CloudSim environment is developed by generating attacker virtual machines and high-MI malicious cloudlets to emulate realistic resource exhaustion scenarios. A-WHO's performance is assessed using makespan, SLA violation rate, each of the QoS metrics, and energy consumption on normal and DDoS conditions. The experimental results indicate that A-WHO achieves the best absolute makespan and QoS metrics during an attack and competitive results under normal conditions. In comparison with the WHO, PSO, ABC, GA, SCA, and CSOA, the proposed approach demonstrates improved robustness and greater resilience to resource degradation attacks. These findings indicate that integrating stagnation-aware diversification into metaheuristic schedulers represents a promising direction for securing cloud task scheduling frameworks.
Citation - Scopus: 1
Aber Performance of Ofdm-Im Systems by Ris Design in the Presence of Iqi and Α-Μ Fading
(Elsevier - Division Reed Elsevier India Pvt Ltd, 2024) Karahan, Busra; Develi, Ibrahim; Canbilen, Ayse Elif; Alsalameh, Hussam
Index modulation (IM) techniques are among the competitive candidates for fifth-generation and beyond (5GB) systems, offering new ways of conveying information thanks to their advantages such as structure flexibility and hardware convenience. Meanwhile, research on orthogonal frequency division multiplexing (OFDM) performance improvements for next-generation wireless communication systems is still intensively ongoing. Accordingly, the IM system has been adapted to OFDM, which allows additional bits of information to be transmitted through the subcarrier indices of the OFDM. Nevertheless, hardware impairments (HWIs) limit the performance of the transceiver. In the literature, reconfigurable intelligent surface (RIS) technology controls the propagation environment and enhances the quality of the received signal by modifying the phase of the incoming signal. In this paper, we investigate the effects of in-phase (I) and quadrature-phase (Q) imbalance (IQI) on RIS-based OFDM-IM transceivers motivated by the benefits of the RISs. Firstly, we present an RIS-assisted OFDM-IM model subject to transmitter and receiver IQI effects. Next, the average bit error rate (ABER) performance of the RIS-assisted OFDM-IM is calculated by the provided mathematical expressions taking the effect of IQI into account. The simulation outputs show that the designed RIS-supported scheme achieves a performance improvement compared to the traditional OFDM-IM under the effect of IQI.
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.
Adaptation and Validation of the Post-Pandemic Health Promotion Behavior of Young Adults in the Digital Age (ps-Sgd) Scale in the Turkish Population
(MDPI, 2024) Koç, Mustafa Can; Yıldırım, Elif; Özdurak Singin, Rabia Hürrem; Talaghir, Laurentiu-Gabriel; Iconomescu, Teodora Mihaela; Karakaş, Neşe
Background: Young adulthood is a critical developmental period in which individuals establish life-long health behaviors and take responsibility for their own health care. Health promotion strategies tailored to young adults, leveraging digital tools, and addressing challenges exacerbated by events like the COVID-19 pandemic are needed. The aim of this study was to adapt the post-pandemic health promotion behavior of young adults in the digital age (PS-SGD) scale to the Turkish population in order to assess and compare the health behavior of young adults after the pandemic. Methods: A total of 312 participants, aged between 19 and 29 years, were included in the study via non-probabilistic criterion sampling, while the Turkish adaptation process started with translation and back translation methods performed with three language and two health science experts. For statistical analysis, EFA and CFA were conducted to evaluate internal consistency and structural validity. Confirmatory factor analysis was utilized to confirm the structure of the six sub-dimensions. Additionally, measurement invariance was examined regarding participants' gender to determine if the scale accurately captured similar traits across diverse groups. The relationship between the test-retest data was tested by Pearson correlation to measure consistency and its invariance over time. Results: The gender distribution of the sample was found to be 61.3% female and 38.7% male. According to the results of EFA, items 8 and 18 were removed from the Turkish-adapted version. As a result of the reliability analysis conducted with the Turkish version of the scale, the Cronbach alpha coefficient was obtained as 0.851 for the post-pandemic health promotion behavior. Additionally, the scale was rated as reliable with the following Cronbach alpha values: 0.79 for the personal hygiene, 0.78 for dietary habits, 0.72 for using mobile devices, 0.70 for emotional health, 0.68 for health care and physical activity, and 0.51 for social health sub-dimensions. To examine the six sub-dimension factor structures of the scale, fit indices were calculated as chi 2/df (1.722), GFI (0.894), IFI (0.908), TLI (0.892), CFI (0.907), RMSEA (0.048), and SRMR (0.057) and were within acceptable limits. Findings of the multi-group confirmatory factor analysis for measurement invariance were less than or equal to 0.01 for the triangle CFI and triangle RMSEA values across all indices. Consequently, it was observed that the item-factor structure, factor loadings, variances, covariances, and error variances of the scale were equivalent for both male and female young adults, while test-retest results showed a high positive correlation. Conclusions: The Turkish version of the post-pandemic health promotion behavior scale of young adults in the digital age scale, consisting of 25 items and six subscales, was proven to be a valid and reliable tool to measure health promotion behavior in young adults aged 19-29 years.
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: 24
Citation - Scopus: 40
An Adaptive Method for Traffic Signal Control Based on Fuzzy Logic With Webster and Modified Webster Formula Using Sumo Traffic Simulator
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Ali, Muzamil Eltejani Mohammed; Durdu, Akif; Çeltek, Seyit Alperen; Yılmaz, Alper
In the past, the Webster optimal cycle time formula was limited to calculate the optimal cycle from historical data for fixed-time traffic signal control. This paper focuses on the design of an adaptive traffic signal control based on fuzzy logic with Webster and modified Webster's formula. These formulas are used to calculate the optimal cycle time depending on the current traffic situation which applying in the next cycle. The alternation of the traffic condition between two successive cycles is monitored and handled through the fuzzy logic system to compensate the fluctuation. The obtained optimal cycle time is used to determine adaptively the effective phase green times i.e. is used to determine adaptively the maximum allowable extension limit of the green phase in the next cycle. The SUMO traffic simulator is used to compare the results of the proposed adaptive control methods with fuzzy logic-based traffic control, and fixed-time Webster and modified Webster-based traffic control methods. The proposed methods are tested on an isolated intersection. In this study, real field-collected data obtained from three, four, and five approaches intersections in Kilis/Turkey are used to test the performance of the proposed methods. In addition, to examine the efficiency of the proposed techniques at heavy demands, the arbitrary demands are generated by SUMO for a four approaches intersection. The obtained simulation results indicate that the proposed methods overperform the fixed time and fuzzy logic-based traffic control methods in terms of average vehicular delay, speed, and travel time.
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: 5
Addendum: Measurement and Qcd Analysis of Double-Differential Inclusive Jet Cross Sections in Proton-Proton Collisions at Root S=13 Tev
(Springer, 2022) Tumasyan, A.; Adam, W.; Andrejkovic, J. W.; Bergauer, T.; Chatterjee, S.; Dragicevic, M.; Del Valle, A. Escalante; Gürpınar Güler, Emine; Güler, Yalçın
The QCD analysis at NNLO is repeated by using the NNLO interpolation grids for the double-differential inclusive jet cross section [1], which were released after the journal publication of the original analysis. The NNLOJET calculation used to derive these grids is based on the leading-colour and leading-flavour-number approximation and does not include the most recent subleading colour contributions. However, these contributions were reported in ref. [2] to be very small in inclusive jet production, in particular for a jet size of R = 0.7. The grids also contain an estimate of the numerical integration uncertainty of around 1% or less. To account for point-to-point fluctuations, this uncertainty, after consultation with the authors of NNLOJET, has been increased by a factor of two; however, its impact in the fit is negligible. A comparison of the measurement with predictions using various PDFs is shown in figure 1. Although the PDF parametrisation remains identical, higher precision in PDF and QCD parameters is expected by using NNLO grids consistently in the QCD analysis. These new results supersede those obtained by using the k-factor technique.
Citation - Scopus: 1
Adrenal Tumor Segmentation on U-Net: a Study About Effect of Different Parameters in Deep Learning
(World Scientific Publ Co Pte Ltd, 2023) Solak, Ahmet; Ceylan, Rahime; Bozkurt, Mustafa Alper; Cebeci, Hakan; Koplay, Mustafa
Adrenal lesions refer to abnormalities or growths that occur in the adrenal glands, which are located on top of each kidney. These lesions can be benign or malignant and can affect the function of the adrenal glands. This paper presents a study on adrenal tumor segmentation using a modified U-Net model with various parameter selection strategies. The study investigates the effect of fine-tuning parameters, including k-fold values and batch sizes, on segmentation performance. Additionally, the study evaluates the effectiveness of different preprocessing techniques, such as Discrete Wavelet Transform (DWT), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Image Fusion, in enhancing segmentation accuracy. The results show that the proposed model outperforms the original U-Net model, achieving the highest scores for Dice, Jaccard, sensitivity, and specificity scores of 0.631, 0.533, 0.579, and 0.998, respectively, on the T1-weighted dataset with DWT applied. These results highlight the importance of parameter selection and preprocessing techniques in improving the accuracy of adrenal tumor segmentation using deep learning.
Citation - WoS: 18
Citation - Scopus: 24
Adsorption of Cr(vi) Onto Cross-Linked Chitosan-Almond Shell Biochars: Equilibrium, Kinetic, and Thermodynamic Studies
(SPRINGER INTERNATIONAL PUBLISHING AG, 2021) Altun, Türkan; Ecevit, Hüseyin; Kar, Yakup; Çiftçi, Birsen
In this study, to remove Cr(VI) from the solution environment by adsorption, the almond shell was pyrolyzed at 400 and 500 degrees C and turned into biochar (ASC400 and ASC500) and composite adsorbents were obtained by coating these biochars with chitosan (Ch-ASC400 and Ch-ASC500). The resulting biochars and composite adsorbents were characterized using Fourier transform infrared (FTIR) spectroscopy; Brunauer, Emmett, and Teller (BET) surface area; scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDX); and the point of zero charge pH (pH(pzc)) analyses. The parameters affecting the adsorption were examined with batch adsorption experiments and the optimum parameters for the efficient adsorption of Cr(VI) in 55 mg L-1 solution were determined as follows; adsorbent dosages: 5 g L-1 for biochars, 1.5 g L-1 for composite adsorbents, contact time: 120 min, pH: 1.5. It was seen that the temperature did not affect the adsorption much. Under optimum conditions, Cr(VI) adsorption capacities of ASC400, ASC500, Ch-ASC400, and Ch-ASC500 adsorbents are 11.33, 11.58, 37.48, and 36.65 mg g(-1), respectively, and their adsorption percentages are 95.2%, 97.5%, 94.3%, and 94.0%, respectively. Adsorption data were applied to Langmuir, Freundlich, Scatchard, Dubinin-Radushkevic, and Temkin isotherms and pseudo-first-order kinetic model, pseudo-second-order kinetic model, intra-particle diffusion model, and film diffusion model. The adsorption data fitted well to the Langmuir isotherm and pseudo-second-order kinetic models. From these results, it was determined that chemical adsorption is the dominant mechanism. Also, both intra-particle diffusion and film diffusion is effective in the adsorption rate. For all adsorbents, the Langmuir isotherm proved to be the most appropriate model for adsorption. The maximum monolayer adsorption capacities calculated from this model are 24.15 mg g(-1), 27.38 mg g(-1), 54.95 mg g(-1), and 87.86 mg g(-1) for ASC400, ASC500, Ch-ASC400, and Ch-ASC500, respectively. The enthalpy change, entropy change, and free energy changes during the adsorption process were calculated and the adsorption was also examined thermodynamically. As a result, adsorption occurs spontaneously for all adsorbents.
Citation - WoS: 3
Citation - Scopus: 3
Adsorption-Assisted Photocatalytic Degradation of Anionic Direct Yellow-50 and Cationic Methylene Blue Dyes by Chemically Synthesized Poly(1,5-Diaminoanthraquinone
(Springer, 2025) Akıllı, A.; Özler, A.; Taymaz, B.H.; Hancı, A.; Eskizeybek, V.; Kamış, H.
Conducting polymers renowned for their exceptional photocatalytic activity, conductivity, and visible-light absorption capabilities present a compelling alternative for advanced photocatalytic applications. In this regard, the creation of conductive polymers of the next generation has enormous promise for improving energy efficiency as well as solving environmental issues. In this study, the conductive polymer poly(1,5-diaminoanthraquinone) (PDAAQ) with a band gap of 1.28 eV and an electrical conductivity of 1.23 S/cm was successfully synthesized via chemical oxidative polymerization using ammonium peroxydisulfate as an oxidant and perchloric acid as an initiator in an acetonitrile polymerization medium. The adsorption-assisted photocatalytic performance of PDAAQ has been investigated in cationic methylene blue (MB) and an anionic direct yellow (DY) dye under visible irradiation. The effect of polymerization medium, oxidant type, polymerization time, and monomer oxidant ratio on adsorption-assisted photocatalytic degradation of MB was investigated. The synthesized PDAAQ polymer demonstrates exceptional photocatalytic performance, completely degrading MB and DYE dyes under visible light illumination in 6 and 8 min through an adsorption-assisted photocatalysis mechanism. Besides, the photocatalytic dye degradation performance of PDAAQ was investigated for the degradation of synthetic wastewater (SWW) under visible light. The PDAAQ polymer proves to be an effective photocatalyst for photocatalytic applications, showcasing exceptional potential in degrading model dyes and treating synthetic wastewater. © The Author(s) 2025.
Advances in UAV Operations for Valley-Type Mapping with Different Duration Period PPP-AR Methods in GCP
(MDPI, 2025) Bilgen, Burhaneddin; Makineci, Hasan Bilgehan; Bulbul, Sercan
This study evaluates the performance of the Precise Point Positioning with Ambiguity Resolution (PPP-AR) method under varying durations and software platforms in determining the optimal placement of Ground Control Points (GCPs) for use in photogrammetric products generated by Unmanned Aerial Vehicles (UAVs) over valley-type rugged terrain. In the field experiment, six GCPs and twenty checkpoints were established, and GNSS measurements with 5-s intervals were collected for 2 h at the GCPs. The collected GNSS data were segmented into 3-min and 10-min intervals, and PPP-AR-based solutions were generated for the complete datasets as well as for the 3- and 10-min subsets. The software tools used for PPP-AR processing included CSRS-PPP, Pride PPP-AR, PPP Arisen, and raPPPid. Eleven photogrammetric models were constructed using the coordinates obtained, and their accuracies were assessed using the checkpoints. The results indicate that, in terms of horizontal accuracy, the best performance was achieved using CSRS-PPP and Pride PPP-AR with 10-min observation durations. The static GNSS method yielded the most precise results for vertical accuracy, while among PPP-AR solutions the 10-min CSRS-PPP application demonstrated superior performance. Additionally, models were generated using only three GCPs placed according to different strategies, revealing that satisfactory levels of accuracy can be achieved when GCPs are strategically positioned. This study demonstrates that the PPP-AR method can be reliably utilized for high-accuracy GCP acquisition within short durations, even in challenging terrain conditions.
Advancing Remote Sensing with Few-Shot Learning: A Comprehensive Review of Methods, Challenges, and Future Directions
(Wiley, 2025) Aslan, Muhammet Fatih; Sabanci, Kadir; Durdu, Akif; Kaousar, Rehana
In this review, the details and developments of few-shot learning (FSL) techniques in different remote sensing (RS) studies including change monitoring, disaster management, urban monitoring, and agriculture are discussed in detail. Furthermore, a categorization is made by dividing FSL methods into three categories (metric-based, optimization-based, and transfer learning approaches) and considering hybrid approaches. Special attention is given to episodic training and meta-learning approaches that provide rapid adaptation to new classes with minimal examples. Furthermore, the integration of explainable artificial intelligence (XAI) and its real-time application capabilities are discussed. Important issues such as domain shift, class imbalance, and high dimensionality are discussed. Recent refinements such as task-level learning, data augmentation, and multimodal integration are examined. Finally, a coherent framework is suggested for further studies and practical FSL applications in the context of RS. As a result, it provides a more comprehensive perspective than previous reviews. This review aimed to guide future research in the integration of FSL with RS applications by analyzing the existing literature and pointing out important research gaps.
Citation - WoS: 6
Citation - Scopus: 9
Ağaç-tohum Algoritmasının Cuda Destekli Grafik İşlem Birimi Üzerinde Paralel Uygulaması
(2018) Çınar, Ahmet Cevahir; Kıran, Mustafa Servet
Son yıllarda toplanan verinin artmasıyla birlikte verimli hesaplama yöntemlerinin de geliştirilmesi ihtiyacı artmaktadır. Çoğunlukla gerçek dünya problemlerinin zor olması sebebiyle optimal çözümü garanti etmese dahi makul zamanda yakın optimal çözümü garanti edebilen sürü zekâsı veya evrimsel hesaplama yöntemlerine olan ilgi de artmaktadır. Diğer bir açıdan seri hesaplama yöntemlerinde verinin veya işlemin paralelleştirilebileceği durumlarda paralel algoritmaların da geliştirilmesi ihtiyacı ortaya çıkmıştır. Bu çalışmada literatüre son yıllarda kazandırılmış olan popülasyon tabanlı ağaç-tohum algoritması ele alınmış ve CUDA platformu içerisinde paralel versiyonu geliştirilmiştir. Algoritmanın paralel versiyonunun performansı kıyas fonksiyonları üzerinde analiz edilmiş ve seri versiyonunun performansı ile karşılaştırılmıştır. Kıyas fonksiyonlarında problem boyutluluğu 10 olarak alınmış ve farklı popülasyon ve blok sayıları altında performans analizi yapılmıştır. Deneysel çalışmalar algoritmanın paralel versiyonunun algoritmanın seri sürümüne göre bazı problemler için 184,65 kata performans artışı sağladığı görülmüştür.
Air Quality of Karaman City, Turkey
(Hysen MANKOLLI, 2020) Mankolli, H.; Toros, H.; Dursun, S.
Karaman is a new and developing city in Turkey's economy is developing and the industry. Geographically, the location of Karaman is located in the south of the Central Anatolia region, in the north of the Taurus Mountains. With its fertile lands, the economy and industry based on Karaman agriculture are developing. Karaman city center has modern industrial facilities open for employment. It is known to have an important industrial potential in recent years. The total surface area of 887 thousand ha of Karaman province; 229 thousand hectares (26%) are flat areas and 654 thousand hectares (74%) are mountainous. The population size is around 250 thousand. Turkey is ahead in the production of bakery products, 35% and 20% of total wheat production is produced by Karaman biscuit manufacturing industry. Turkey, as in general in Karaman in fossil fuel consumption for heating in the cold winter air pollution as it is used in many developed cities is also observed. Traffic vehicles vehicle exhausts and fossil fuels used in industry are other important sources of pollution in the city center. In Turkey, the year 2020 at the beginning of March with the gorilla-19 Covidien epidemic, there has been a significant improvement in air quality. The field dust event that occurred after pandemic virus measures, especially PM pollution increase was observed. © 2020, Hysen MANKOLLI. All rights reserved.

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