In order to address some of the challenges we propose a complexity framework that allows the derivation of a priori estimates of mental load that go beyond CLT's notion of element interactivity.
The subdivision of the complete modeling process in QSAR modeling workflow architecture provides several advantages including (a) it reduces the complexity of modeling framework (b) improves the understanding of the implemented machine learning procedure and (c) increases the flexibility for future modification of the workflow.
In this paper, we design features that can be used for efficient target classification and analyze the time complexity of our framework.
Including additional underlying constructs inevitably adds to the complexity of the framework [ 8].
Although there would have been potential value in using the full PK profile as driver for efficacy, the complexity of the framework and the resultant run times severely limited the practicalities of such an approach.
(Interview 1) Furthermore, citing the complexity of the framework, one participant suggested " not one person can cover it all, so you have to work as a team don't you to be able to work holistically.
In "Complexity-Aware Quantization and Lightweight VLSI Implementation of FIR Filters", a complexity-aware quantization framework for FIR filters is presented.
However existing energy modelling tools fail to meet many of the needs to manage this process, due to the excessive complexity of modelling frameworks.
These challenges were traced to three root causes: the ambiguity of the EA concept, difficult EA terminology and the complexity of EA frameworks.
In particular, we show that our recently proposed framework, Context-dependent Learning from Ordered Answer Sets, is more general than brave induction, induction of stable models, and cautious induction, and maintains the same complexity as cautious induction, which has the highest complexity of these frameworks.
