Approximation of Algebraic Curves in Function Spaces of Topological Sequences Connected to Specific Simple Graph Families
Journal of Function Spaces,
Journal Year:
2025,
Volume and Issue:
2025(1)
Published: Jan. 1, 2025
Algebraic
curves
and
topological
sequences
play
a
crucial
role
in
mathematics
graph
theory,
serving
as
bridge
between
geometry,
algebra,
number
theory.
They
facilitate
structural
analysis
various
applications,
including
chemistry,
network
analysis,
computer
science.
In
this
research,
we
introduce
the
concept
of
estimated
algebraic
S
T
,
explore
development
linear
exponential
that
emerge
from
associated
with
collections
simple
graphs.
By
analyzing
invariants
their
corresponding
sequences,
aim
to
estimate
model
these
through
curves,
thereby
shedding
light
on
dynamics
growth
trends.
Our
study
centers
some
families
graphs,
such
snake
pan
which
obtain
closed‐form
expressions
asymptotic
approximations
for
sequences.
constructing
characterise
mathematical
interactions
regulating
evolution
explain
how
qualities
graphs
affect
properties.
These
provide
new
approaches
comprehend
complicated
networks
help
address
graph‐theoretic
problems
combinatorics,
computational
geometry.
Language: Английский
Native language identification from text using a fine-tuned GPT-2 model
PeerJ Computer Science,
Journal Year:
2025,
Volume and Issue:
11, P. e2909 - e2909
Published: May 28, 2025
Native
language
identification
(NLI)
is
a
critical
task
in
computational
linguistics,
supporting
applications
such
as
personalized
learning,
forensic
analysis,
and
machine
translation.
This
study
investigates
the
use
of
fine-tuned
GPT-2
model
to
enhance
NLI
accuracy.
Using
NLI-PT
dataset,
we
preprocess
fine-tune
classify
native
learners
based
on
their
Portuguese-written
texts.
Our
approach
leverages
deep
learning
techniques,
including
tokenization,
embedding
extraction,
multi-layer
transformer-based
classification.
Experimental
results
show
that
our
significantly
outperforms
traditional
methods
(
e.g
.,
SVM,
Random
Forest)
other
pre-trained
models
BERT,
RoBERTa,
BioBERT),
achieving
weighted
F1
score
0.9419
an
accuracy
94.65%.
These
large
transformer
work
well
for
can
help
guide
future
research
tools
artificial
intelligence
(AI)-based
education.
Language: Английский