Diagnosis of discrete-event systems (DESs) may be improved by knowledge-compilation techniques, where a large amount of model-based reasoning is anticipated off-line, by simulating the behavior of the system and generating suitable data structures (compiled knowledge) embedding diagnostic information.
As a consequence, operators are investing a large amount of money to investigate traffic modeling and classification through packet inspection in order to better understand the characteristic of today's cellular data traffic.
The advantage of this methodology is that it allows to test a large amount of alternative model topologies using a limited amount of data, or semi-quantitative data such as promoter activities in genetic knockout strains or immunoblotting data [ 12, 13].
This Web-delivered application makes use of the Ingenuity Pathways Knowledge Base (IPKB) containing a large amount of individually modeled relationships between gene objects (e.g., genes, mRNAs, and proteins) in order to dynamically generate significant regulatory and signaling networks or pathways.
Through modeling a large amount of cases with a validated model, the factors, e.g. temperature difference, outdoor wind velocity, effective air leakage gaps in the envelopes, the area of the air leakage and the room, were analyzed.
However, there exists a large amount of data in the model on anaesthesia and different types of shock favouring their use for evaluation of complex situations such as the anaesthesia of OP poisoned patients and combined injuries.
The artificial neural network technique has been widely investigated, but it requires a large amount of historical data for model training and suffers from local optima and overfitting issues.
Because our research focused on individual learning styles related to collective learning processes and a large amount of learning activities a model from Family 5 seems appropriate to study preferences in the context of quality improvement work [ 33, 34].
Although different experimental methods [ 1, 2] have already generated a large amount of PPI for many model species in recent years [ 3], these existing PPI data are incomplete and contain many false positive interactions.
The terrain analysis variables did not explain a large amount of variance within the models.
