Browsing by Author "Maras, Hadi Hakan"

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An evaluation of the relationship between physical/mechanical properties and mineralogy of landscape rocks as determined by hyperspectral reflectance
(Springer Heidelberg, 2016) Maras, Erdem Emin; Maraş, Hadi Hakan; Caniberk, Mustafa; Odabas, Mehmet Serhat; Degerli, Burcu; Maras, Suleyman Sirri; Maras, Hadi Hakan; 34410
We investigated the relationships between mineral content and the physical and mechanical properties of landscape rock using a non-destructive remote sensing method applied in the laboratory. Using this technique, the spectral properties of the landscape rock could be collected at different wavelengths without harming the samples. Differences in spectral reflectance were compared with the physical and mechanical properties of the stone. Significant correlations were observed between reflectance values and the rocks' mechanical and physical properties, with correlation coefficients of 95 to 99 %. However, establishing a correlation between two variables is not a sufficient condition to establish a causal relationship. Mineral densities and mineral content are characteristics used for the classification of landscape rock. We have concluded that although spectral signatures from landscape rock can be used for predicting which stones might have similar features when comparing two batches of stone, the high correlations we discovered cannot confirm a cause and effect relationship that would allow for the prediction of a rock's physical and mechanical properties. Although this conclusion is disappointing, the mineral content and the significant correlations discovered by hyperspectral reflectance scanning can be used as supplementary information when comparing two samples of landscape rock.
Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods
(Springer, 2022) Ureten, Kemal; Maraş, Hadi Hakan; Maras, Hadi Hakan; 34410
Rheumatoid arthritis and hand osteoarthritis are two different arthritis that causes pain, function limitation, and permanent joint damage in the hands. Plain hand radiographs are the most commonly used imaging methods for the diagnosis, differential diagnosis, and monitoring of rheumatoid arthritis and osteoarthritis. In this retrospective study, the You Only Look Once (YOLO) algorithm was used to obtain hand images from original radiographs without data loss, and classification was made by applying transfer learning with a pre-trained VGG-16 network. The data augmentation method was applied during training. The results of the study were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated from the confusion matrix, and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. In the classification of rheumatoid arthritis and normal hand radiographs, 90.7%, 92.6%, 88.7%, 89.3%, and 0.97 accuracy, sensitivity, specificity, precision, and AUC results, respectively, and in the classification of osteoarthritis and normal hand radiographs, 90.8%, 91.4%, 90.2%, 91.4%, and 0.96 accuracy, sensitivity, specificity, precision, and AUC results were obtained, respectively. In the classification of rheumatoid arthritis, osteoarthritis, and normal hand radiographs, an 80.6% accuracy result was obtained. In this study, to develop an end-to-end computerized method, the YOLOv4 algorithm was used for object detection, and a pre-trained VGG-16 network was used for the classification of hand radiographs. This computer-aided diagnosis method can assist clinicians in interpreting hand radiographs, especially in rheumatoid arthritis and osteoarthritis.
Automatic Coastline Detection Using Image Enhancement and Segmentation Algorithms
(Hard, 2016) Maras, Erdem Emin; Maraş, Hadi Hakan; Caniberk, Mustafa; Maras, Hadi Hakan; 34410
Coastlines have hosted numerous civilizations since the earliest times of mankind due to the advantages they offer such as natural resources, transportation, arable areas, seafood, trade, and biodiversity. Coastal regions should be monitored vigilantly by planners and control mechanisms, and any changes in these regions should be detected with its human or natural origin, and future plans and possible interventions should be formed in these aspects to maintain ecological balance, sustainable development, and planned urbanization. Integrated coastal zone management (ICZM) provides an important tool to reach that goal. One of the important elements of ICZM is the detection of coastlines. While there are several methods to detect coastlines, remote sensing methods provide the fastest and the most efficient solutions. In this study, color infrared, grayscale, RGB, and fake infrared images were processed with the median filtering and segmentation software developed within the study, and coastal lines were detected by the edge detection method. The results show that segmentation with fake infrared images derived from RGB images give the best results.
Detection of Rheumatoid Arthritis From Hand Radiographs Using A Convolutional Neural Network
(Springer London Ltd, 2020) Ureten, Kemal; Erbay, Hasan; Maras, Hadi Hakan
Introduction Plain hand radiographs are the first-line and most commonly used imaging methods for diagnosis or differential diagnosis of rheumatoid arthritis (RA) and for monitoring disease activity. In this study, we used plain hand radiographs and tried to develop an automated diagnostic method using the convolutional neural networks to help physicians while diagnosing rheumatoid arthritis. Methods A convolutional neural network (CNN) is a deep learning method based on a multilayer neural network structure. The network was trained on a dataset containing 135 radiographs of the right hands, of which 61 were normal and 74 RA, and tested it on 45 radiographs, of which 20 were normal and 25 RA. Results The accuracy of the network was 73.33% and the error rate 0.0167. The sensitivity of the network was 0.6818; the specificity was 0.7826 and the precision 0.7500. Conclusion Using only pixel information on hand radiographs, a multi-layer CNN architecture with online data augmentation was designed. The performance metrics such as accuracy, error rate, sensitivity, specificity, and precision state shows that the network is promising in diagnosing rheumatoid arthritis.
Did satellite imagery supersede aerial imagery? A perspective from 3D geopositioning accuracy
(Springer Heidelberg, 2016) Yilmaz, Altan; Maraş, Hadi Hakan; Erdogan, Mustafa; Maras, Hadi Hakan; Aktug, Bahadir; Maras, Suleyman Sirri; 34410
In this study, the geometric accuracy comparison of aerial photos and WorldView-2 satellite stereo image data is evaluated with the different number and the distribution of the ground control points (GCPs) on the basis of large scale map production. Also, the current situation of rivalry between airborne and satelliteborne imagery was mentioned. The geometric accuracy of Microsoft UltraCam X 45 cm ground sampling distance (GSD) aerial imagery and WorldView-2 data both with and without GCPs are also separately analyzed. The aerial photos without any GCP by only using global navigation satellite system (GNSS) and inertial measurement unit (IMU) data with tie points give an accuracy of +/- 1.17 m in planimetry and +/- 0.71 m in vertical that means nearly two times better accuracy than the rational polynomial coefficient (RPC) of stereo WorldView-2. Using one GCP affects the accuracies of aerial photos and WorldView-2 in different ways. While this situation distorts the aerial photo block, it corrects the shift effect of RPC in WorldView-2 and increases the accuracy. By using four or more GCPs, 1/2 pixel (similar to 0.23 m) accuracy in aerial photos and 1 pixel (similar to 0.50 m) accuracy in WorldView-2 can be achieved in horizontal. In vertical, aerial photos have 1 pixel (similar to 0.55 m) and WorldView-2 has 1.5 pixels (similar to 0.85 m) accuracy. These results show that Worldview-2 imagery can be used in the production of class I 1: 5000 scale maps according to the ASPRS Accuracy Standards for Digital Geospatial Data in terms of geometric accuracy. It is concluded that the rivalry between aerial and satellite imagery will continue for some time in the future.
Risk Assessment of Sea Level Rise for Karasu Coastal Area, Turkey
(Mdpi, 2023) Eliawa, Ali; Maraş, Hadi Hakan; Genc, Asli Numanoglu; Tora, Hakan; Maras, Hadi Hakan; 34410
Sea Level Rise (SLR) due to global warming is becoming a more pressing issue for coastal zones. This paper presents an overall analysis to assess the risk of a low-lying coastal area in Karasu, Turkey. For SLR scenarios of 1 m, 2 m, and 3 m by 2100, inundation levels were visualized using Digital Elevation Model (DEM). The eight-side rule is applied as an algorithm through Geographic Information System (GIS) using ArcMap software with high-resolution DEM data generated by eleven 1:5000 scale topographic maps. The outcomes of GIS-based inundation maps indicated 1.40%, 6.02%, and 29.27% of the total land area by 1 m, 2 m, and 3 m SLR scenarios, respectively. Risk maps have shown that water bodies, low-lying urban areas, arable land, and beach areas have a higher risk at 1 m. In a 2 m scenario, along with the risk of the 1 m scenario, forests become at risk as well. For the 3 m scenario, almost all the territorial features of the Karasu coast are found to be inundated. The effect of SLR scenarios based on population and Gross Domestic Product (GDP) is also analyzed. It is found that the 2 and 3 m scenarios lead to a much higher risk compared to the 1 m scenario. The combined hazard-vulnerability data shows that estuarine areas on the west and east of the Karasu region have a medium vulnerability. These results provide primary assessment data for the Karasu region for the decision-makers to enhance land use policies and coastal management plans.
Two Majority Voting Classifiers Applied to Heart Disease Prediction
(Mdpi, 2023) Karadeniz, Talha; Maraş, Hadi Hakan; Karadeniz, Talha; Maras, Hadi Hakan; Tokdemir, Gül; Tokdemir, Gul; Ergezer, Halit; Ergezer, Halit; 34410; 293396
Two novel methods for heart disease prediction, which use the kurtosis of the features and the Maxwell-Boltzmann distribution, are presented. A Majority Voting approach is applied, and two base classifiers are derived through statistical weight calculation. First, exploitation of attribute kurtosis and attribute Kolmogorov-Smirnov test (KS test) result is done by plugging the base categorizer into a Bagging Classifier. Second, fitting Maxwell random variables to the components and summating KS statistics are used for weight assignment. We have compared state-of-the-art methods to the proposed classifiers and reported the results. According to the findings, our Gaussian distribution and kurtosis-based Majority Voting Bagging Classifier (GKMVB) and Maxwell Distribution-based Majority Voting Bagging Classifier (MKMVB) outperform SVM, ANN, and Naive Bayes algorithms. In this context, which also indicates, especially when we consider that the KS test and kurtosis hack is intuitive, that the proposed routine is promising. Following the state-of-the-art, the experiments were conducted on two well-known datasets of Heart Disease Prediction, namely Statlog, and Spectf. A comparison of Optimized Precision is made to prove the effectiveness of the methods: the newly proposed methods attained 85.6 and 81.0 for Statlog and Spectf, respectively (while the state of the heart attained 83.5 and 71.6, respectively). We claim that the Majority Voting family of classifiers is still open to new developments through appropriate weight assignment. This claim is obvious, especially when its simple structure is fused with the Ensemble Methods' generalization ability and success.