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Static Analysis Techniques for Predicting the Behavior of Active Database Rules
Static Analysis Techniques Predicting Behavior Active Database Rules
2016/5/24
Methods are given for statically analyzing sets of database production rules to determine if the rules are (1) guaranteed to terminate, (2) guaranteed to produce a unique nal database state, and (3) ...
Predicting and Characterising User Impact on Twitter
Predicting Characterising User Impact Twitter
2014/3/20
The open structure of online social networks and their uncurated nature give rise to problems of user credibility and influence. In this paper, we address the task of predicting the impact of Twitter ...
Predicting ERP User Satisfaction―an Adaptive Neuro Fuzzy Inference System (ANFIS) Approach
ANFIS ERP Implementation Outcome Prediction Failure Detection CSFs Causal Factors
2010/7/22
ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” ...
A New Fuzzy Modelling Approach For Predicting The Maximum Daily Temperature From A Time Series
Fuzzy rule-base modeling probabilistic time series transition matrix
2009/10/14
Classical time series analysis requires many assumptions such as the normality of data, linearity in the autocorelation coecient and statistical parameter estimations. It is almost impossible to find...
Predicting molecular formulas of fragment ions with isotope patterns in tandem mass spectra
Predicting molecular formulas fragment ions isotope patterns
2005/12/31
A number of different approaches have been proposed to predict elemental component formulas (or molecular formulas) of molecular ions in low and medium resolution mass spectra. Most of them rely on is...
Predicting Australian Stock Market Index Using Neural Networks Exploiting Dynamical Swings and Intermarket Influences
stock market prediction financial time series neural networks feature selection correlation variance reduction overtraining
2003/10/27
This paper presents a computational approach for predicting the Australian stock market index – AORD using multi-layer feed-forward neural networks from the time series data of AORD and various interr...