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Title: Fisher's linear discriminant analysis based prediction using transient features of seismic events in coal mines
Authors: Köktürk Güzel, Başak Esin
Karaçalı, Bilge
Keywords: Coal mines
Data mining
Discriminant analysis
Information systems
Seismic activity
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Köktürk Güzel, B. E., and Karaçalı, B. (2016, September). Fisher's linear discriminant analysis based prediction using transient features of Seismic Events in Coal Mines. In M. Ganzha, L. Maciaszek, M. Paprzycki (Eds.), Paper presented at the Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, Gdansk,, Poland (pp. 231-234). New York City : Institute of Electrical and Electronics Engineers.
Abstract: Identification 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.
Description: 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016; Gdansk; Poland; 11 September 2016 through 14 September 2016
ISBN: 9788360810903
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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