  The discovery of neutrino oscillation, proving that neutrinos do have masses,
reveals the misfits of particles in the current Standard Model (SM) theory. In
theory, neutrinos having masses could result in lepton flavour not being a
symmetry called Lepton Flavour Violation (LFV). While SM theory extensions
allowed LFV processes, their branching fractions are too small, making them
unobservable even with the strongest equipment up-to-date. With that,
scientists in recent years have generated LFV-like processes from the combined
LHCb and Monte-Carlo-Simulated data in an attempt to identify LFV using
Artificial Intelligence (AI), specifically Machine Learning (ML) and Deep
Learning (DL). In this paper, the performance of several algorithms in AI has
been presented, such as XGBoost, LightGBM, custom 1-D Dense Block Neural
Networks (DBNNs), and custom 1-D Convolutional Neural Networks (CNNs) in
identifying LFV signals, specifically $\tau^{-} \rightarrow
\mu^{-}\mu^{-}\mu^{+}$ decay from the combined LHCb and Monte-Carlo-Simulated
data that imitates the signatures of the said decay. Kolmogorov-Smirnov (KS)
and Cramer-von Mises (CvM) tests were also conducted to verify the validity of
predictions for each of the trained algorithms. The result shows decent
performances among algorithms, except for the LightGBM, for failing the CvM
test, and a 20-layered CNN for having recorded a considerably low AUC.
Meanwhile, XGBoost and a 10-layered DBNN recorded the highest AUC of 0.88. The
main contribution of this paper is the extensive experiment involving custom
DBNN and CNN algorithms in different layers, all of which have been rarely used
in the past years in identifying LFV-like signatures, unlike GBMs and
tree-based algorithms, which have been more popular in the said task.
