General Classification of the Authenticated Encryption Schemes for the CAESAR CompetitionFarzaneh abedChristian ForlerStefan Lucks
General Classification SchemesNo Abstract available for this article.doi:10.1038/202956e0NoneSpringer NatureNature
In addition, it can be combined with some of the mathematical programming techniques for feature selection (Bertsimas and Mazumder, 2014), with classification schemes (Bertsimas and Shioda, 2007), or with constraints on the coefficients of the linear manifold. This unified framework is also able ...
, or type II error, aka power) levels for some statistical test of choice, when designing ML/AI modeling, two additional factors come into play the first relevant to predictive mod- eling, and the second related to causal modeling: (a) The learning curve of the used learning algorithm....
According to the classification accuracies, oGT2FC, as a general type-2 fuzzy classifier, could perform better than its competitors in all datasets, except Car+. Table 5. Classification accuracy and CPU time (ms) of compared methods. DatasetAlgorithm Up to 20 expertsoGT2FCST2ClasseT2Classp...
There are two general approaches to developing artificial general intelligence (AGI)1: computer-science-oriented and neuroscience-oriented. Because of the fundamental differences in their formulations and coding schemes, these two approaches rely on distinct and incompatible platforms2,3,4,5,6,7,8, ...
We show that traditional vector graphics can be quickly converted into cell-specialized streams using a novel lattice-clipping algo- rithm that is simpler and asymptotically faster than hierarchical clipping schemes. A unique aspect of the algorithm is that it requires a single traversal of the ...
Improving hybrid-layer convolutional neural network system for lung cancer nodule classification using enhanced weight optimisation algorithm by Vikul Pawar, P. Premchand Abstract: In recent times, lung cancer is evolving as a highly life-threatening disease for human beings. According to the WHO, ...
The UMD LC was obtained through supervised classification with a decision tree algorithm of imagery captured by the AVHRR sensor. Urban and built-up areas were not mapped, nor were water covers. Instead, they were extracted from auxiliary sources. The classification obtained in this way was then...
Our framework, called L-GreCo, is based on an efficient adaptive algorithm, which automatically picks the optimal compression parameters for model layers guaranteeing the best compression ratio while respecting a theoretically-justified error constraint. Our extensive experimental study over image ...