Browsing by Author "Tasdemir, Sena Busra Yengec"

Now showing 1 - 3 of 3

Citation - WoS: 11
Citation - Scopus: 20
ROI Detection in Mammogram Images Using Wavelet-Based Haralick and Hog Features
(IEEE, 2018) Tasdemir, Sena Busra Yengec; Tasdemir, Kasim; Aydin, Zafer
Digital mammography is a widespread medical imaging technique that is used for early detection and diagnosis of breast cancer. Detecting the region of interest (ROI) helps to locate the abnormal areas, which may be analyzed further by a radiologist or a CAD system. In this paper, a new classification method is proposed for ROI detection in mammography images. Features are extracted using Wavelet transform, Haralick and HOG descriptors. To reduce the number of dimensions and eliminate irrelevant features, a wrapper-based feature selection method is implemented. Several feature extraction methods and machine learning classifiers are compared by performing a leave-one-image-out cross-validation experiment on a difficult dataset. The proposed feature extraction method provides the best accuracy of 87.5% and the second-best area under curve (AUC) score of 84% when employed in a random forest classifier.
Citation - WoS: 7
Citation - Scopus: 8
Evaluation of Dominant and Non-Dominant Hand Movements for Volleyball Action Modelling
(Assoc Computing Machinery, 2019) Haider, Fasih; Salim, Fahim A.; Tasdemir, Sena Busra Yengec; Naghashi, Vahid; Tengiz, Izem; Cengiz, Kubra; Luz, Saturnino
In this paper, we assess the use of Inertial Measurement Units (IMU) in recognising different volleyball-specific actions. Analysis of the results suggests that all sensors in the IMU (i.e. magnetometer, accelerometer, barometer and gyroscope) contribute unique information in the classification of volleyball-specific actions. We demonstrate that while the accelerometer feature set provides the best Unweighted Average Recall (UAR) overall, "decision fusion" of the accelerometer with the magnetometer improves UAR slightly from 85.86% to 86.9%. Interestingly, it is also demonstrated that the non-dominant hand provides better UAR than the dominant hand. These results are even more marked with "decision fusion".
Citation - WoS: 24
Citation - Scopus: 31
Volume Fraction, Thickness, and Permeability of the Sealing Layer in Microbial Self-Healing Concrete Containing Biogranules
(Frontiers Media S.A., 2018) Ersan, Yusuf Cagatay; Palin, Damian; Tasdemir, Sena Busra Yengec; Tasdemir, Kasim; Jonkers, Henk M.; Boon, Nico; De Belie, Nele
Autonomous repair systems in construction materials have become a promising alternative to current unsustainable and labor-intensive maintenance methods. Biomineralization is a popular route that has been applied to enhance the self-healing capacity of concrete. Various axenic microbial cultures were coupled with protective carriers, and their combination appears to be useful for the development of healing agents for realizing self-healing concrete. The advantageous traits of non-axenic cultures, such as economic feasibility, self-protection, and high specific activity have been neglected so far, and thus the number of studies investigating their performance as healing agents is scarce. Here we present the self-healing performance of a mortar containing a healing agent consisting of non-axenic biogranules with a denitrifying core. Mortar specimens with a defined crack width of 400 mu m were used in the experiments and treated with tap water for 28 days. Self-healing was quantified in terms of the crack volume reduction, the thickness of the sealing layer along the crack depth and water permeability under 0.1 bar pressure. Complete visual crack closure was achieved in the bio-based specimens in 28 days, the thickness of the calcite layer was recorded as 10 mm and the healed crack volume was detected as 6%. Upon self-sealing of the specimens, the water permeability decreased by 83%. Overall, non-axenic biogranules with a denitrifying core shows great potential for development of self-healing bioconcrete.