Maraş, Hadi Hakan
Loading...
Profile URL
Name Variants
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
SDG data could not be loaded because of an error. Please refresh the page or try again later.
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Bibliometrics data could not be loaded because of an error. Please refresh the page or try again later.
Google Analytics Visitor Traffic
Google Analytics Visitor Traffic could not be loaded because of an error. Please refresh the page or try again later.
Scholarly output chart could not be loaded because of an error. Please refresh the page or try again later.
Journals could not be loaded because of an error. Please refresh the page or try again later.
Scopus Quartile Distribution
Quartile distribution chart could not be loaded because of an error. Please refresh the page or try again later.
Competency Cloud
31 results
Scholarly Output Search Results
Now showing 1 - 10 of 31
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, Yuksel; 17411; 34410Objectives: 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: 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 Emin; 34410We 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, Turker; 7671; 57149In 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 Analysing Iraqi Railways Network by Applying Specific Criteria Using the Gis Techniques(Coll Science Women, Univ Baghdad, 2019) Naji, Hayder Fans; Maras, H. Hakan; 34410The railways network is one of the huge infrastructure projects. Therefore, dealing with these projects such as analyzing and developing should be done using appropriate tools, i.e. GIS tools. Because, traditional methods will consume resources, time, money and the results maybe not accurate. In this research, the train stations in all of Iraq's provinces were studied and analyzed using network analysis, which is one of the most powerful techniques within GIS. A free trial copy of ArcGIS (R) 10.2 software was used in this research in order to achieve the aim of this study. The analysis of current train stations has been done depending on the road network, because people used roads to reach those train stations. The data layers for this study were collected and prepared to meet the requirements of network analyses within GIS. In this study, the current train stations in Iraq were analyzed and studied depending on accessibility value for those stations. Also, to know the numbers of people who can reach those stations within a walking time of 20 minutes. So, this study aims to analyze the current train stations according to multiple criteria by using network analysis in order to find the serviced areas around those stations. Results will be presented as digital maps layers with their attribute tables that show the beneficiaries from those train stations and serviced areas around those stations depending on specific criteria, with a view to determine the size of this problem and to support the decision makers in case of locating new train stations within the best locations for it.Conference Object Neuronavigation Skill Training Through Simulation: Insights From Eye Data(Iated-int Assoc Technology Education A& development, 2016) Çağıltay, Nergiz; Tokdemir, Gül; Cagiltay, N. E.; Topalli, D.; Maraş, Hadi Hakan; Borcek, A. O.; Tokdemir, G.; Aydın, Elif; Maras, H. H.; Tonbul, G.; Aydin, E.; 17411; Yazılım Mühendisliği; Bilgisayar Mühendisliği; Elektrik-Elektronik MühendisliğiNeuronavigation systems are developed to support the brain surgery operations. Because of its complex anatomical structure, the neurosurgery is a risky and critical operation. The surgeon is required to perform the operation in a very small area with very restricted movements. The neuronavigation systems are developed to help the surgeon during the operation to show the current position of the surgery with respect to the 3D virtual model of the patient. In these systems, the 3D virtual model of the patient is created according to the medical data (MRI/BT) of the patient. Hence these systems work like navigations systems that are used in driving a car. The surgeon uses this system by controlling the system through a software interface and its user interface and correlates the current position of the operation with the 3D patient virtual model. In this way the surgeon checks the critical anatomical structures through this system and eliminates possible risks. Hence surgeons who will perform such operations are required to develop several skills to manage this very complicated environment. They are required to perform the operation according to the information coming from the navigation display. Additionally, in order to reach relevant information from the navigation display they have to control the navigation panel. In order to prepare surgeons to manage this very complicated environment, their required skills need to be improved during the training period. In this study, to better understand the surgeons' behaviours while managing the tasks related to the surgical navigation procedures, a simulation based environment is developed and an experimental study is conducted with 10 people. Their eye data and their performance data is recorded based on the simulated tasks. The results of the study is analysed statistically and descriptively. The results show that it is possible to control a neuronavigation display through eye movements which could be an alternative human-computer interaction option for designing the neuronavigation systems' user interfaces. Secondly, it is shown that performing a task according to the results of a second information source (neuronavigation system) lowers the general performance in terms of travelled distance with the operation tool and camera (endoscope). However the success level while performing each task and the time spent values are similar in both cases. On the other hand the number of errors is higher in the first scenario. Hence, the surgical education programs need to provide appropriate solutions to better understand and measure the skill levels of trainees on such tasks and to improve their skills through virtual practice systems.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, Hakan; 17411Nowadays, 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.Article Citation - WoS: 62Citation - Scopus: 70Detection 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.Article Citation - WoS: 38Citation - Scopus: 51Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs With Deep Learning Methods(Springer, 2022) Maras, Hadi Hakan; Ureten, Kemal; 34410Rheumatoid 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.Article Citation - WoS: 10Citation - Scopus: 15Detection of Hand Osteoarthritis From Hand Radiographs Using Convolutional Neural Networks With Transfer Learning(Tubitak Scientific & Technological Research Council Turkey, 2020) Erbay, Hasan; Maras, Hadi Hakan; Ureten, Kemal; 34410Osteoarthritis is the most common type of arthritis. Hand osteoarthritis leads to specific structural changes in the joints, such as asymmetric joint space narrowing and osteophytes (bone spurs). Conventional radiography has traditionally been the primary method of visualizing these structural changes and diagnosing osteoarthritis. We aimed to develop a computerized method that is capable of determining the structural changes seen in radiography of the hand and to assist practitioners in interpreting radiographic changes and diagnosing the disease. In this retrospective study, transfer-learning-based convolutional neural networks were trained on a randomly selected dataset containing 332 radiography images of hands from an original set of 420 and were validated with the remaining 88. Multilayer convolutional neural network models were designed based on a transfer learning method using pretrained AlexNet, GoogLeNet, and VGG-19 networks. The accuracies of the models were 93.2% for AlexNet, 94.3% for GoogLeNet, and 96.6% for VGG-19. The sensitivities of these models were 0.9167 for AlexNet, 0.9184 for GoogLeNet, and 0.9574 for VGG-19, while the specificity values were 0.9500, 0.9744, and 0.9756, respectively. The performance metrics, including accuracy, sensitivity, specificity, and precision, of our newly developed automated diagnosis methods are promising in the diagnosis of hand osteoarthritis. Our computer-aided detection systems may help physicians in interpreting hand radiography images, diagnosing osteoarthritis, and saving time.Conference Object Citation - WoS: 10Citation - 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.; 34410Digital 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.
