Maraş, Hadi Hakan
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Name Variants
Maras, H. Hakan & Maras, Hadi Hakan & Maraş, H.H. & Maras, Hakan
Job Title
Prof. Dr.
Email Address
hhmaras@cankaya.edu.tr
Main Affiliation
06.01. Bilgisayar Mühendisliği
Bilgisayar Mühendisliği
06. Mühendislik Fakültesi
01. Çankaya Üniversitesi
Bilgisayar Mühendisliği
06. Mühendislik Fakültesi
01. Çankaya Üniversitesi
Status
Current Staff
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ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
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WoS Researcher ID
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Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
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3GOOD HEALTH AND WELL-BEING
1
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4QUALITY EDUCATION
1
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5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
1
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8DECENT WORK AND ECONOMIC GROWTH
1
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
2
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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13CLIMATE ACTION
1
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14LIFE BELOW WATER
2
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15LIFE ON LAND
0
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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17PARTNERSHIPS FOR THE GOALS
0
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Scholarly Output
32
Articles
19
Views / Downloads
2095/894
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
204
Scopus Citation Count
274
Patents
0
Projects
0
WoS Citations per Publication
6.38
Scopus Citations per Publication
8.56
Open Access Source
10
Supervised Theses
0
| Journal | Count |
|---|---|
| Arabian Journal of Geosciences | 2 |
| Elektronika ir Elektrotechnika | 2 |
| Turkish Journal of Electrical Engineering and Computer Sciences | 2 |
| 4th International Conference on Control, Decision and Information Technologies (CoDIT) -- APR 05-07, 2017 -- Barcelona, SPAIN | 1 |
| 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) -- OCT 02-05, 2016 -- Seattle, WA | 1 |
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32 results
Scholarly Output Search Results
Now showing 1 - 10 of 32
Article Citation - WoS: 11Citation - Scopus: 12Diagnosis 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,Article Citation - WoS: 7Citation - Scopus: 7An 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: 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: 2Citation - Scopus: 2Risk Assessment of Sea Level Rise for Karasu Coastal Area, Turkey(Mdpi, 2023) Genc, Asli Numanoglu; Tora, Hakan; Maras, Hadi Hakan; Eliawa, Ali; Numanoğlu Genç, AslıSea 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: 9Two 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 Radyografik Olarak Tanı Konulabilen Diz Röntgenlerinin Derin Öğrenme ve Makine Öğrenmesi Yöntemleri ile Sınıflandırılması(2025) Duran, Semra; Orhan, Kevser; Maras, Hadi Hakan; Atalar, Ebru; Maraş, Yüksel; Üreten, KemalBu çalışmanın amacı, düz diz röntgenleriyle tanısı konulabilen diz osteoartriti, sinovyal kondromatozis, Osgood-Schlatter hastalığı, os fabella patolojileri ve normal diz radyografilerini derin öğrenme ve makine öğrenmesi yöntemleriyle sınıflandırmaktır. Bu çalışma 540 diz osteoartriti, 151 Osgood_Schlatter hastalığı, 191 diz kondromatozisi, 152 os fabella ve 523 normal diz röntgen görüntüsü üzerinde gerçekleştirildi. Öncelikle önceden eğitilmiş derin öğrenme modeli olan VGG-16 ağı ile sınıflandırma yapıldı. Daha sonra VGG-16 evrişim katmanı ile çıkarılan özellikler, rastgele orman, destek vektör makineleri, lojistik regresyon ve karar ağacı makine öğrenmesi algoritmalarıyla sınıflandırıldı. VGG-16 modeli ile %95,3 doğruluk, %95,1 duyarlılık, %98.7 özgüllük, %96,8 kesinlik ve %95,9 F1 skoru sonuçları elde edildi. VGG-16 evrişim katmanından çıkarılan özelliklerin makine öğrenmesi algoritmaları ile sınıflandırılmasında lojistik regresyon sınıflandırıcısı ile %98,2 doğruluk, %99,0 duyarlılık, %98.9 özgüllük, %98,2 kesinlik ve %98,5 F1 skoru sonuçları elde edilmiştir. Radyografik olarak tanısı konulabilen diz patolojilerinin sınıflandırılması amacıyla yapılan bu çalışmada, VGG-16 ağı ile başarılı sonuçlar elde edilmiştir. VGG-16 modeli evrişim katmanı üzerinden çıkarılan özellikler makine öğrenmesi algoritmaları ile yeniden sınıflandırılmış, lojistik regresyon, destek vektör makineleri ve rastgele orman sınıflandırıcıları ile VGG-16 modeline kıyasla performans metriklerinde iyileşmeler elde edilmiştir. Önerilen bu yöntemle, derin öğrenme modellerinin performansı daha da iyileştirilebilir.Article Citation - WoS: 65Citation - Scopus: 74Detection of Rheumatoid Arthritis From Hand Radiographs Using a Convolutional Neural Network(Springer London Ltd, 2020) Ureten, Kemal; Erbay, Hasan; Maras, Hadi HakanIntroduction 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.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.Conference Object Problems of Gaining Neuronavigation Skills on Surgical Education Programs: A case study in Turkey(2016) Cağıltay, Nergiz Ercil; Borcek, Alp Özgün; Tokdemir, Gül; Maraş, Hadi Hakan; Topallı, DamlaNeuronavigation technology is one of the important technologies that supports and guides the surgeons during the surgical procedures. Availability of this technology for the surgical education programs to gain the skills of using this technology effectively and efficiently during the surgical operations as well as availability of this technology in the operating room is critical for improving the surgical operations’ performance. In this study, the availability of this technology for the neurosurgical operations is researched through a quantitative research conducted by 220 neuro surgeons in Turkey. Based on the results of this study, suggestions for the education technology developers for creating technology enhanced education programs to increase the navigation skill levels of the candidate surgeons are developed. The results of this study aimed to improve the quality of the surgical education programs and the performance of the surgical procedures.Conference Object Citation - WoS: 1Neuronavigation Systems and Passive Usage Problem(Ieee, 2015) Tonbul, Gokcen; Aydin, Elif; Cagiltay, Nergiz; Topalli, Damla; Borcek, Alp Ozgun; Tokdemir, Gul; Maras, HakanNowadays, neuronavigation systems are used in brain surgery procedures, known as a technology to help the surgeon during the operational period. However, the surgeons have faced several problems with the existing systems. Some of these problems are related to the systems software and user interfaces. In this study, such problems are examined and the "Passive Usage" term is added to the literature by establishing a connection between the problems of endoscopic surgical procedures and similar issues occurred in other domains. The passive usage problem is generalized on different domains for the first time with this study. The results of the study expected to gather up the similar passive usage problems experienced in different domains. Accordingly, the methodologies and studies that are conducted in different research areas may lead to eliminate the Passive Usage problems efficiently.