AEWS: an integrated knowledge-based system with neural networks for reliability prediction

1998 ◽  
Vol 35 (2) ◽  
pp. 101-108 ◽  
Author(s):  
Young B Moon ◽  
C.Kenneth Divers ◽  
Hyune-Ju Kim
Keyword(s):  
Neural Networks ◽  
Reliability Prediction ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Integrated Knowledge

APX: An Integrated Knowledge-Based System to Support Audit Planning

1994 ◽  
Vol 3 (3) ◽  
pp. 149-164 ◽  
Author(s):  
Anandhi Bharadwaj ◽  
Vijay Karan ◽  
Radha K. Mahapatra ◽  
Uday S. Murthy ◽  
Ajay S. Vinze
Keyword(s):  
Knowledge Based System ◽  
Audit Planning ◽  
Knowledge Based ◽  
Integrated Knowledge

scholarly journals Integrated Knowledge-Based System for Machine Design

2013 ◽  
Vol 5 ◽  
pp. 702590 ◽  
Author(s):  
Durmuş Karayel ◽  
S. Serdar Ozkan ◽  
Fahri Vatansever
Keyword(s):  
Machine Design ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Integrated Knowledge

An integrated knowledge-based system for grasslands ecosystems

2001 ◽  
Vol 14 (5-6) ◽  
pp. 271-280 ◽  
Author(s):  
Li Da Xu ◽  
Ning Liang ◽  
Qiong Gao
Keyword(s):  
Knowledge Based System ◽  
Knowledge Based ◽  
Integrated Knowledge

RECOGNITION OF ARABIC PHONETIC FEATURES USING NEURAL NETWORKS AND KNOWLEDGE-BASED SYSTEM: A COMPARATIVE STUDY

1999 ◽  
Vol 08 (01) ◽  
pp. 73-103 ◽  
Author(s):  
S. A. SELOUANI ◽  
J. CAELEN
Keyword(s):  
Neural Networks ◽  
Arabic Language ◽  
Vowel Duration ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Auditory Models ◽  
System A ◽  
Acoustical Analysis ◽  
Phonetic Features ◽  
Segmentation Task

In this paper, we are concerned with the automatic recognition of Arabic phonetic macro-classes and complex phonemes by multi-layer sub-neural-networks (SNN) and knowledge-based system (SARPH). Our interest goes to the particularities of the Arabic language such as geminate and emphatic consonants and the vowel duration. These particularities are unanimously considered as the main root of failure of Automatic Speech Recognition (ASR) systems dedicated to standard Arabic. The purely automatic method constituted by the SNNs is confronted to an approach based on the user phonetic knowledge expressed by SARPH rules. For the acoustical analysis of speech as well as for the segmentation task, auditory models have been used. The ability of systems has been tested in experiments using stimuli uttered by 6 native Algerian speakers. The results show that SNNs achieved well in pure identification while in the case of semantically relevant duration the knowledge-based system performs better.

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