655 citations · 670 across the 4 of their papers we have counts for
5 papers
Hierarchical Multi-resolution Mesh Networks for Brain Decoding
Itir Onal Ertugrul, Mete Ozay, Fatos Tunay Yarman Vural
We propose a new framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs), which establishes a set of brain networks at multiple time resolutions of fMRI signal to rep…
Machine Learning Methods for Attack Detection in the Smart Grid
Mete Ozay, Inaki Esnaola, Fatos T. Yarman Vural +2
Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online sett…
Fusion of Image Segmentation Algorithms using Consensus Clustering
Mete Ozay, Fatos T. Yarman Vural, Sanjeev R. Kulkarni +1
A new segmentation fusion method is proposed that ensembles the output of several segmentation algorithms applied on a remotely sensed image. The candidate segmentation sets are pr…
Learning Deep Temporal Representations for Brain Decoding
Orhan Firat, Emre Aksan, Ilke Oztekin +1
Functional magnetic resonance imaging produces high dimensional data, with a less then ideal number of labelled samples for brain decoding tasks (predicting brain states). In this…
Discriminative Functional Connectivity Measures for Brain Decoding
Orhan Firat, Mete Ozay, Ilke Oztekin +1
We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resona…