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The Fermi Large Area Telescope (LAT) is currently the most important facilityfor investigating the GeV $\gamma$-ray sky. With Fermi LAT more than threethousand $\gamma$-ray sources have been discovered so far. 1144 ($\sim40\%$) ofthe sources are active galaxies of the blazar class, and 573 ($\sim20\%$) arelisted as Blazar Candidate of Uncertain type (BCU), or sources without aconclusive classification. We use the Empirical Cumulative DistributionFunctions (ECDF) and the Artificial Neural Networks (ANN) for a fast method ofscreening and classification for BCUs based on data collected at $\gamma$-rayenergies only, when rigorous multiwavelength analysis is not available. Basedon our method, we classify 342 BCUs as BL Lacs and 154 as FSRQs, while 77objects remain uncertain. Moreover, radio analysis and direct observations inground-based optical observatories are used as counterparts to the statisticalclassifications to validate the method. This approach is of interest because ofthe increasing number of unclassified sources in Fermi catalogs and becauseblazars and in particular their subclass High Synchrotron Peak (HSP) objectsare the main targets of atmospheric Cherenkov telescopes.

Blazar Flaring Patterns (B-FlaP): Classifying Blazar Candidates of Uncertain type in the third Fermi-LAT catalog by Artificial Neural Networks