TY - GEN
T1 - Xiruxe
T2 - 2009 International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2009
AU - Bakir, Ayse
AU - Kocaguneli, Ekrem
AU - Tosun, Ayse
AU - Bener, Ayse
AU - Turhan, Burak
PY - 2009
Y1 - 2009
N2 - Fault localization in telecommunication sector is a major challenge. Most companies manually try to trace faults back to their origin. Such a process is expensive, time consuming and ineffective. Therefore in this study we automated manual fault localization process by designing and implementing an intelligent software tool (Xiruxe) for a local telecommunications company. Xiruxe has a learning-based engine which uses powerful AI algorithms, such as Naïve Bayes, Decision Tree and Multi Layer Perceptrons, to match keywords and patterns in the fault messages. The initial deployment results show that this intelligent engine can achieve a misclassification rate as low as 1.28%.
AB - Fault localization in telecommunication sector is a major challenge. Most companies manually try to trace faults back to their origin. Such a process is expensive, time consuming and ineffective. Therefore in this study we automated manual fault localization process by designing and implementing an intelligent software tool (Xiruxe) for a local telecommunications company. Xiruxe has a learning-based engine which uses powerful AI algorithms, such as Naïve Bayes, Decision Tree and Multi Layer Perceptrons, to match keywords and patterns in the fault messages. The initial deployment results show that this intelligent engine can achieve a misclassification rate as low as 1.28%.
UR - http://www.scopus.com/inward/record.url?scp=84876777111&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84876777111
SN - 9781615676576
T3 - International Conference on Artificial Intelligence and Pattern Recognition 2009, AIPR 2009
SP - 293
EP - 300
BT - International Conference on Artificial Intelligence and Pattern Recognition 2009, AIPR 2009
Y2 - 13 July 2009 through 16 July 2009
ER -