Classification of Patients According to Their Risks of Restenosis using Multi Criteria Classification Models and Regression Techniques

Halenur, Şahin
Duran, Serhan
Yakıcı, Ertan


Classification of electricity customers based on real consumption values using data mining and machine learning techniques and its corresponding applications
İşyapar, Muhammet Tuğberk; Alpaslan, Ferda Nur; Department of Computer Engineering (2013)
Classifying electricity customers based on real power consumptions has been particularly important in the last decade following the liberalization of the electricity markets in numerous countries and ubiquitous use of Automatic Meter Reading devices that collect consumption data at hourly intervals. Collection of vast amounts of consumption data has made it possible to identify customer classes by clustering. Classification of customer load profiles provides the basis of several applications offering soluti...
Classification of fNIRS Data Using Deep Learning for Bipolar Disorder Detection
Evgin, Haluk Barkin; Babacan, Oguzhan; Ulusoy, İlkay; Hosgoren, Yasemin; Kusman, Adnan; Sayar, Damla; Baskak, Bora; Ozguven, Halise Devrimci (2019-01-01)
With the use of ecologically validated tools more applicable measurements can be obtained, especially of individuals who have psychological disorders. Functional Near-Infrared Spectroscopy (fNIRS) is a neural imaging method that comes into prominence for imaging patients who have psychological disorders. It is a desired method because of its feasibility, high resolution in time and its partial resistance to head movements. Following the developments in the artificial intelligence, individuals' medical data ...
Classification of Hazelnut Kernels by Using Impact Acoustic Time-Frequency Patterns
Kalkan, Habil; Ince, Nuri Firat; Tewfik, Ahmed H.; Yardimci, Yasemin; Pearson, Tom (Springer Science and Business Media LLC, 2007-11-11)
Hazelnuts with damaged or cracked shells are more prone to infection with aflatoxin producing molds ( Aspergillus flavus). These molds can cause cancer. In this study, we introduce a new approach that separates damaged/cracked hazelnut kernels from good ones by using time-frequency features obtained from impact acoustic signals. The proposed technique requires no prior knowledge of the relevant time and frequency locations. In an offline step, the algorithm adaptively segments impact signals from a training...
Classification of fMRI Data by Using Clustering
Moğultay, Hazal; Yarman Vural, Fatoş Tunay (2015-05-19)
Recognition of the the cognitive states by using functional Magnetic Rezonans Imaging (fMRI) data is a challenging problem that has been a focus of scientific research for a long time. In this study the effectiveness of clustering and the ensemble learning techniques on fMRI dataset is investigated and different paramaters are compared. Moreover, the performance of these techniques are tested on both raw voxel intensity values and meshes formed by multiple voxels. Clusters are compared to the functional bra...
Classification of bridge damages and costs for optimal maintenance planing based on markov decision process
Aslzad, Reza; Akgül, Ferhat; Department of Engineering Sciences (2016)
Serviceability of a bridge is gradually reduced over time due to loads they carry andthe environmental effects. The effect of deterioration on bridges begin to appear when the bridges start aging and the materials are subjected to constant wear. Traffic loadings, weather and environmental factors, quality of construction, natural disasters, such as earthquakes and floods, are important factors that affect the degree and rate of deterioration of a bridge. Therefore, appropriate management is needed for bridg...
Citation Formats
Ş. Halenur, S. Duran, and E. Yakıcı, “Classification of Patients According to Their Risks of Restenosis using Multi Criteria Classification Models and Regression Techniques,” 2017, Accessed: 00, 2021. [Online]. Available: