Ibrahim Burak Özyurt

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This paper describes a genetically guided approach to optimizing the hard (J1) and fuzzy (Jm) c-means functionals used in cluster analysis. Our experiments show that a genetic algorithm ameliorates the di culty of choosing an initialization for the c-means clustering algorithms. Experiments use six data sets, including the Iris data, magnetic resonance and(More)
Organizing and annotating biomedical data in structured ways has gained much interest and focus in the last 30 years. Driven by decreases in digital storage costs and advances in genetics sequencing, imaging, electronic data collection, and microarray technologies, data is being collected at an ever increasing rate. The need to store and exchange data in(More)
The aggregation of imaging, clinical, and behavioral data from multiple independent institutions and researchers presents both a great opportunity for biomedical research as well as a formidable challenge. Many research groups have well-established data collection and analysis procedures, as well as data and metadata format requirements that are particular(More)
Investigators perform multi-site functional magnetic resonance imaging studies to increase statistical power, to enhance generalizability, and to improve the likelihood of sampling relevant subgroups. Yet undesired site variation in imaging methods could off-set these potential advantages. We used variance components analysis to investigate sources of(More)
Managing vast datasets collected throughout multiple clinical imaging communities has become critical with the ever increasing and diverse nature of datasets. Development of data management infrastructure is further complicated by technical and experimental advances that drive modifications to existing protocols and acquisition of new types of research data(More)
Increasing complexity of the chemical process industries (CPI) requires more reliable and efficient real time diagnostic tools. Here, a hybrid diagnostic methodology is introduced for fault diagnosis based on a hierarchical multilayer perceptron-elliptical neural network structure and a fuzzy expert system. The introduced hybrid system is noise tolerant,(More)
Ever increasing size of the biomedical literature makes tapping into implicit knowledge in scientific literature a necessity for knowledge discovery. In this paper, a semantic parser for recognizing semantic roles and named entities in individual sentences of schizophrenia related scientific abstracts is described. The named entity recognizer, CRFNER,(More)
A method for chemical process fault diagnosis using semiquantitative model generated behavior envelopes is described in this paper. The method generates a sequence of rules for each fault class, with any rule in a sequence valid within the bounds of its time interval. This can be viewed as a qualitative description of the trend of numerical sensor(More)
Arterial spin labeling (ASL) is a magnetic resonance imaging technique that provides a non-invasive and quantitative measure of cerebral blood flow (CBF). After more than a decade of active research, ASL is now emerging as a robust and reliable CBF measurement technique with increased availability and ease of use. There is a growing number of research and(More)
Arterial spin labeling (ASL) MRI provides an accurate and reliable measure of cerebral blood flow (CBF). A rapidly growing number of CBF measures are being collected both in clinical and research settings around the world, resulting in a large volume of data across a wide spectrum of study populations and health conditions. Here, we describe a central CBF(More)