Alternative Approach for Learning and Improving the MCDA Method PROAFTN

In the paper, the authors took an attempt to briefly describe their experience in organizing work with data analysis using neural networks and machine

The problem is the lack of an effective information technology for processing and analyzing data that would enable the medical analyst to identify hidden patterns

In order to improve the reconstruction of the normal deriva- tives, reduce the extension of the boundary part where over- specified data are given and deblur noisy normal

The purpose of F irs t L ink is to increase unders tand ing and awareness among ind iv idua ls w i th demen t ia and the ir fam i ly members /careg ivers of A lzhe imer’s

The conservative sector of the two-body problem is presented in chapter III , and illustrated by the high precision (next-to-next-to-next-to-leading order) computation of the

We apply this hybrid verification method to Ethernet TSN networks and obtain, for instance in the case of priority scheduling with 8 traffic classes, a 99% prediction accuracy with

As described above, the classical penalty method penalizes the arcs along the shortest path by adding a fraction of the arcs’ original length.. These changes to the metric

Traditionally, Hidden Markov Models (HMMs) would be used to address this problem, involving a first step of hand- crafting to build a dialogue model (e.g. defining potential hid-