WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Conference Object Citation - WoS: 1Citation - Scopus: 2Fisher's Linear Discriminant Analysis Based Prediction Using Transient Features of Seismic Events in Coal Mines(Institute of Electrical and Electronics Engineers Inc., 2016) Köktürk Güzel, Başak Esin; Karaçalı, BilgeIdentification of seismic activity levels in coal mines is important to avoid accidents such as rockburst. Creating an early warning system that can save lives requires an automated way of predicting. This study proposes a prediction algorithm for the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines that is based on transient activity features along with average indicators evaluated by a Fisher's linear discriminant analysis. Performance evaluation experiments on the training datasets revealed an accuracy level of around 0.9438 while the performance on the test dataset was at a level of 0.9297. These results suggest that the proposed approach achieves high accuracy in predicting danger seismic events while maintaining low complexity.Conference Object Citation - WoS: 4Citation - Scopus: 4Mining Frequent Patterns From Microarray Data(Institute of Electrical and Electronics Engineers Inc., 2011) Yıldız, Barış; Şelale, HaticeThe rapid development of computers and increasing amount of collected data made data mining a popular analysis tool. Data mining research is interrelated to many fields and one of the most important ones is bioinformatics. Among many techniques, mining association rules or frequent patterns is one of the most studied techniques in computer science and it is applicable to bioinformatics. Association analysis of gene expressions may be used as decision support mechanism for finding genes that are in same pathway. In this work, publicly available yeast microarray data has been analyzed using an efficient frequent pattern mining algorithm Matrix Apriori and frequently co-over-expressed genes have been identified. © 2011 IEEE.