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
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
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Files
Sustainable Development Goals
11
SUSTAINABLE CITIES AND COMMUNITIES
2
Research Products
3
GOOD HEALTH AND WELL-BEING
1
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
14
LIFE BELOW WATER
2
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
1
Research Products
1
NO POVERTY
0
Research Products
4
QUALITY EDUCATION
1
Research Products
5
GENDER EQUALITY
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
15
LIFE ON LAND
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
1
Research Products
13
CLIMATE ACTION
1
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
2
ZERO HUNGER
0
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
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
WoS h-index
7
Scopus h-index
9
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: 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: 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: 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 Citation - Scopus: 2Automatic Detection of Spina Bifida Occulta With Deep Learning Methods From Plain Pelvic Radiographs(Springer Science and Business Media Deutschland GmbH, 2023) Üreten, K.; Maraş, Y.; Maraş, H.H.; Gök, K.; Atalar, E.; Çayhan, V.; Duran, S.Purpose: Spina bifida occulta (SBO), which is the most common congenital spinal deformity, is often seen in the lower lumbar spine and sacrum. In this study, it is aimed to develop a computer-aided diagnosis method that will help clinicians in the diagnosis of spina bifida occulta from plain pelvic radiographs with deep learning methods and transfer learning method. Materials and methods: The You Only Look Once (YOLO) algorithm was used for object detection, and classification was made by applying transfer learning with a pre-trained VGG-19, ResNet-101, MobileNetV2, and GoogLeNet networks. Our dataset consisted of 206 normal lumbosacral radiographs and 160 SBO lumbosacral radiographs. The performance of the models was evaluated by metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the ROC curve (AUC) results. Results: In the detection of SBO, 85.5%, 80.8%, 89.7%, 87.5%, 84%, and 0.92 accuracy, sensitivity, specificity, precision, F1 score, and AUC results were obtained with the pre-trained VGG-19 model, respectively. The pre-trained VGG-19 model performed better than the others. Conclusion: Successful results were obtained in this study performed to the diagnosis of SBO with deep learning methods. A model that will assist physicians in the diagnosis of SBO can be developed with new studies to be conducted with a large number of spinal radiographs. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.Conference Object Citation - Scopus: 11Measurement of Edge Detection Algorithms in Clean and Noisy Environment(Ieee, 2014) Mahmood, Alaa Mohammed; Maras, Hadi Hakan; Elbasi, ErsinThe subject of identification edge in images has a wide application in various fields for that it's considered one of the important topics in a digital image processing. There are many algorithms to detect the edge in images, but the performance of these algorithms depends on the type of image, the environment of the image and the threshold value of the edge algorithm. The objective of this paper is to evaluate five algorithms of edge detection which are Roberts, Sobel, Prewitt, LOG, and Canny in multi environments clean and noisy by using several types of original images (binary image, graphic image, high frequency image, low frequency image, median frequency image, and texture image) and then determine the best algorithm. In noisy environment the following noises was used Gaussian, salt and pepper and speckle. It's known that each edge detection algorithm has a threshold value, if the current pixel value is less than the defined threshold in strength, it will be considered an edge pixel. The change rate of the threshold value in all environments is also explained through this study.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,Conference Object Spam Detection With Fasttext Based Features(Institute of Electrical and Electronics Engineers Inc., 2024) Karadeniz, T.; Tokdemir, G.; Maraş, H.H.Fasttext is a powerful word representation method that creates word representations based on vectors of character n-grams. In this work, we propose a method that utilizes fasttext features for a novel feature engineering model for the spam detection problem. In the feature engineering method, the combination of average, mean of second derivative; mean peak and standard deviation of fasttext features are computed. Finally, tf-idf features are also considered for the modeling process. The success of each feature engineering technique is measured and reported. The combination of the five feature extraction methods, tested on two spam detection datasets, yielded promising results with an accuracy of 0.978 on e-mail spam detection and an accuracy of 0.986 on sms spam classification. © 2024 IEEE.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.Conference Object
