Diaz Cruz, J.A. Edelen, A.L. Edstrom, D.R. Jacobson, B.T. Lumpkin, A.H. Sikora, J.P. Thurman-Keup, R.M. JACoW Publishing Geneva, Switzerland 2673-5490 978-3-95450-227-1 10.18429/JACoW-IPAC2022-MOPOPT058 English 400-403 Contribution to a conference proceedings 2022 2022-07 https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT058 https://jacow.org/ipac2022/papers/mopopt058.pdf Low emittance electron beams are of high importance at facilities like the Linac Coherent Light Source II (LCLS-II) at SLAC. Emittance dilution effects due to off-axis beam transport for a TESLA-type cryomodule (CM) have been shown at the Fermilab Accelerator Science and Technology (FAST) facility. The results showed the correlation between the electron beam-induced cavity high-order modes (HOMs) and the Beam Position Monitor (BPM) measurements downstream the CM. Mitigation of emittance dilution can be achieved by reducing the HOM signals. Here, we present a couple of Neural Networks (NN) for bunch-by-bunch mean prediction and standard deviation prediction for BPMs located downstream the CM.