Maraş, Hadi Hakan
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Maras, H. Hakan
Maras, Hadi Hakan
Maraş, H.H.
Maras, Hakan
Maras, Hadi Hakan
Maraş, H.H.
Maras, Hakan
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Prof. Dr.
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hhmaras@cankaya.edu.tr
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06.01. Bilgisayar Mühendisliği
Bilgisayar Mühendisliği
06. Mühendislik Fakültesi
01. Çankaya Üniversitesi
Bilgisayar Mühendisliği
06. Mühendislik Fakültesi
01. Çankaya Üniversitesi
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Now showing 1 - 10 of 31
Article Citation - WoS: 16Citation - Scopus: 24A Decision Support System for Locating Weapon and Radar Positions in Stationary Point Air Defence(Springer, 2012) Maras, Hakan; Gencer, Cevriye; Aygunes, Haluk; Tanerguclu, TurkerIn this study, a decision support system (DSS) based on the interactive use of location models and geographical information systems (GIS) was developed to determine the optimal positions for air defence weapons and radars. In the location model, the fire units are considered as the facilities to be located and the possible approach routes of air vehicles are treated as demand points. Considering the probability that fire by the units will miss the targets, the objective of the problem is to determine the positions that provide coverage of the approach routes of the maximum number of weapons while considering the military principles regarding the tactical use and deployment of units. In comparison with the conventional method, the proposed methodology presents a more reliable, faster, and more efficient solution. On the other hand, owing to the DSS, a battery commander who is responsible for air defence becomes capable of determining the optimal weapon and radar positions, among the alternative ones he has identified, that cover the possible approach routes maximally. Additionally, he attains the capability of making such decisions in a very short time without going to the field over which he will perform the defence and hence without being subject to enemy threats. In the decision support system, the digital elevation model is analysed using Map Objects 2.0, the mathematical model is solved using LINGO 4.0 optimization software, and the user interface and data transfer are supported by Visual Basic 6.0.Article Citation - WoS: 6Citation - Scopus: 6An Evaluation of the Relationship Between Physical/Mechanical Properties and Mineralogy of Landscape Rocks as Determined by Hyperspectral Reflectance(Springer Heidelberg, 2016) Caniberk, Mustafa; Odabas, Mehmet Serhat; Degerli, Burcu; Maras, Suleyman Sirri; Maras, Hadi Hakan; Maras, Erdem EminWe 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.Article Citation - WoS: 2Citation - Scopus: 2Risk Assessment of Sea Level Rise for Karasu Coastal Area, Turkey(Mdpi, 2023) Genc, Asli Numanoglu; Tora, Hakan; Maras, Hadi Hakan; Eliawa, AliSea 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.Article Citation - WoS: 3Citation - Scopus: 7Two Majority Voting Classifiers Applied To Heart Disease Prediction(Mdpi, 2023) Karadeniz, Talha; Maras, Hadi Hakan; Tokdemir, Gul; Ergezer, HalitTwo 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.Article A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images(Kaunas Univ Technology, 2024) Karadeniz, Talha; Tokdemir, Gul; Maras, H. HakanChronic venous insufficiency (CVI) is a serious disease characterised by the inability of the veins to effectively return blood from the legs back to the heart. This condition represents a significant public health issue due to its prevalence and impact on quality of life. In this work, we propose a tool to help doctors effectively diagnose CVI. Our research is based on extracting Visual Geometry Group network 16 (VGG-16) features and integrating a new classifier, which exploits mean absolute deviation (MAD) statistics to classify samples. Although simple in its core, it outperforms state-of-the-art method which is known as the CVI-classifier in the literature, and additionally it performs better than the methods such as multi-layer perceptron (MLP), Naive Bayes (NB), and gradient boosting machines (GBM) in the context of VGG-based classification of CVI. We had 0.931 accuracy, 0.888 Kappa score, and 0.916 F1-score on a publicly available CVI dataset which outperforms the state-of-the-art CVI-classifier having 0.909, 0.873, and 0.900 for accuracy, Kappa score, and F1-score, respectively. Additionally, we have shown that our classifier has a generalisation capacity comparable to support vector machines (SVM), by conducting experiments on eight different datasets. In these experiments, it was observed that our classifier took the lead on metrics such as F1-score, Kappa score, and receiver operating characteristic area under the curve (ROC AUC).Conference Object Multimodal interaction flow representation for ubiquitous environments - MIF: A case study in surgical navigation interface design(2015) Tokdemir, Gül; Altun, Gamze; Çağıltay, Nergiz E.; Maras, H. Hakan; Börcek, Alp ÖzgünWith the advent of technology, new interaction modalities became available which augmented the system interaction. Even though there are vast amount of applications for the ubiquitous devices like mobile agents, smart glasses and wearable technologies, many of them are hardly preferred by users. The success of those systems is highly dependent on the quality of the interaction design. Moreover, domain specific applications developed for these ubiquitous devices involve detailed domain knowledge which normally IT professionals do not have, which may involve a substantial lack of quality in the services provided. Hence, effective and high quality domain specific applications developed for these ubiquitous devices require significant collaboration of domain experts and IT professionals during the development process. Accordingly, tools to provide common communication medium between domain experts and IT professionals would provide necessary medium for communication. In this study, a new modelling tool for interaction design of ubiquitous devices like mobile agents, wearable devices is proposed which includes different interaction modalities. In order to better understand the effectiveness of this newly proposed design tool, an experimental study is conducted with 11 undergraduate students (novices) and 15 graduate students (experienced) of Computer Engineering Department for evaluating defect detection performance for the defects seeded into the interface design of a neuronavigation device. Results show that the defects were realized as more difficult for the novices and their performance was lower compared to experienced ones. Considering the defect types, wrong information and wrong button type of defects were recognized as more difficult. The results of this study aimed to provide insights for the system designers to better represent the interaction design details and to improve the communication level of IT professionals and the domain experts. © Springer International Publishing Switzerland 2015.Conference Object Covariance Features for Trajectory Analysis(IEEE, 2016) Karadeniz, Talha; Maras, Hadi HakanIn this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results.Article Covariance Features for Trajectory Analysis(Kaunas Univ Technology, 2018) Karadeniz, Talha; Maraş, Hadi HakanIn this work, it is demonstrated that covariance estimator methods can be used for trajectory classification. It is shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. Compared to Dynamic Time Warping, application of explained technique is faster and yields more accurate results. An improvement of Dynamic Time Warping based on counting statistical comparison of base distance measures is also achieved. Results on Australian Sign Language and Character Trajectories datasets are reported. Experiment realizations imply feasibility through covariance attributes on time series.Conference Object Citation - WoS: 11Citation - Scopus: 12A New Robust Binary Image Embedding Algorithm in Discrete Wavelet Domain(Institute of Electrical and Electronics Engineers Inc., 2014) Mohammed, A.; Maraş, H.H.; Elbasi, E.Digital watermarks have recently emerged as a possible solution for protecting the copyright of digital materials, the work presented in this paper is concerned with the Discrete Wavelet Transform (DWT) based non-blind digital watermarking, and how the DWT is an efficient transform in the field of digital watermarking. In this work we used an optimum criteria that embeds four watermarks in more than one level of DWT in the same algorithm. The aim of this work is to keep the Correlation Coefficient (CC) between the original and the extracted watermark around the value of 0.9.Article Citation - WoS: 11Citation - Scopus: 11Diagnosis of Osteoarthritic Changes, Loss of Cervical Lordosis, and Disc Space Narrowing on Cervical Radiographs With Deep Learning Methods(Turkish Joint Diseases Foundation, 2022) Tokdemir, Gul; Ureten, Kemal; Atalar, Ebru; Duran, Semra; Maras, Hakan; Maras, YukselObjectives: In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology. Materials and methods: In this retrospective study, the convolutional neural networks were used and transfer learning method was applied with the pre-trained VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks. Our data set consisted of 161 normal lateral cervical radiographs and 170 lateral cervical radiographs with osteoarthritis and cervical degenerative disc disease. Results: We compared the performances of the classification models in terms of performance metrics such as accuracy,
