Applied Mathematics and Nonlinear Sciences,
Год журнала:
2025,
Номер
10(1)
Опубликована: Янв. 1, 2025
Abstract
The
rapid
expansion
of
cross-national
e-commerce
has
brought
significant
opportunities
and
challenges
in
understanding
diverse
consumer
behavior.
This
study
introduces
an
innovative
framework
combining
the
XLSTM
(Extended
Long
Short-Term
Memory)
model
with
K-means
clustering
to
analyze
user
behavior
optimize
conversion
rates
on
global
platforms.
extends
traditional
LSTM
models
by
incorporating
multi-dimensional
cell
states,
attention
mechanisms,
improved
memory
capabilities,
enabling
it
effectively
capture
complex
temporal
cross-cultural
patterns.
integration
enhances
process
providing
high-quality
embeddings
that
lead
well-defined
stable
clusters.
Through
comprehensive
evaluations,
combined
approach
demonstrates
superior
performance
across
key
metrics,
including
Silhouette
Score,
Davies-Bouldin
Index
(DBI),
Adjusted
Rand
(ARI),
compared
standalone
algorithms
LSTM-based
methods.
Feature
importance
analysis
further
identifies
coupon
usage,
visit
frequency,
product
category
interest
as
most
influential
factors
purchase
decisions.
findings
highlight
potential
this
methodology
improve
engagement
marketing
strategies
for
Extracellular Vesicles and Circulating Nucleic Acids,
Год журнала:
2025,
Номер
6(1), С. 128 - 40
Опубликована: Фев. 28, 2025
Artificial
intelligence
(AI)
is
revolutionizing
scientific
research
by
facilitating
a
paradigm
shift
in
data
analysis
and
discovery.
This
transformation
characterized
fundamental
change
methods
concepts
due
to
AI’s
ability
process
vast
datasets
with
unprecedented
speed
accuracy.
In
breast
cancer
research,
AI
aids
early
detection,
prognosis,
personalized
treatment
strategies.
Liquid
biopsy,
noninvasive
tool
for
detecting
circulating
tumor
traits,
could
ideally
benefit
from
analytical
capabilities,
enhancing
the
detection
of
minimal
residual
disease
improving
monitoring.
Extracellular
vesicles
(EVs),
which
are
key
elements
cell
communication
progression,
be
analyzed
identify
disease-specific
biomarkers.
combined
EV
promises
an
enhancement
diagnosis
precision,
aiding
Studies
show
that
can
differentiate
types
predict
drug
efficacy,
exemplifying
its
potential
medicine.
Overall,
integration
biomedical
clinical
practice
significant
changes
advancements
diagnostics,
medicine-based
approaches,
our
understanding
complex
diseases
like
cancer.
Computational and Structural Biotechnology Journal,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 1, 2025
The
Transformer
is
a
deep
neural
network
based
on
the
self-attention
mechanism,
designed
to
handle
sequential
data.
Given
its
tremendous
advantages
in
natural
language
processing,
it
has
gained
traction
for
other
applications.
As
primary
structure
of
RNA
sequence
nucleotides,
researchers
have
applied
Transformers
predict
secondary
and
tertiary
structures
from
sequences.
number
Transformer-based
models
prediction
tasks
rapidly
increasing
as
they
performed
par
or
better
than
learning
networks,
such
Convolutional
Recurrent
Neural
Networks.
This
article
thoroughly
examines
models.
Through
an
in-depth
analysis
models,
we
aim
explain
how
their
architectural
innovations
improve
performances
what
still
lack.
techniques
continue
evolve,
this
review
serves
both
record
past
achievements
guide
future
avenues.
SHS Web of Conferences,
Год журнала:
2025,
Номер
214, С. 01006 - 01006
Опубликована: Янв. 1, 2025
Artificial
intelligence
(AI)
continues
to
advance
nuclear
medicine
in
all
areas,
including
treatment
planning,
resource
allocation,
and
precision.
The
imaging
techniques
powered
by
AI
enable
faster
more
accurate
diagnosis
of
diseases
machine
learning
models
improve
individual-specific
dosimetry.
Additionally,
increases
operational
efficiency,
reduces
costs,
lower
radiation
exposure
for
patients.
Despite
these
improvements,
issues
such
as
ethical
concerns,
bias
data,
clinical
integration
difficulties
still
exist.
This
review
paper
discusses
the
role
changing
practice,
emphasizing
pros
cons,
anticipated
future.
As
field
proves
its
further
value,
multidisciplinary
collaborations
are
invited
help
ensure
AI’s
value
future
treatment.
Applied Mathematics and Nonlinear Sciences,
Год журнала:
2025,
Номер
10(1)
Опубликована: Янв. 1, 2025
Abstract
The
rapid
expansion
of
cross-national
e-commerce
has
brought
significant
opportunities
and
challenges
in
understanding
diverse
consumer
behavior.
This
study
introduces
an
innovative
framework
combining
the
XLSTM
(Extended
Long
Short-Term
Memory)
model
with
K-means
clustering
to
analyze
user
behavior
optimize
conversion
rates
on
global
platforms.
extends
traditional
LSTM
models
by
incorporating
multi-dimensional
cell
states,
attention
mechanisms,
improved
memory
capabilities,
enabling
it
effectively
capture
complex
temporal
cross-cultural
patterns.
integration
enhances
process
providing
high-quality
embeddings
that
lead
well-defined
stable
clusters.
Through
comprehensive
evaluations,
combined
approach
demonstrates
superior
performance
across
key
metrics,
including
Silhouette
Score,
Davies-Bouldin
Index
(DBI),
Adjusted
Rand
(ARI),
compared
standalone
algorithms
LSTM-based
methods.
Feature
importance
analysis
further
identifies
coupon
usage,
visit
frequency,
product
category
interest
as
most
influential
factors
purchase
decisions.
findings
highlight
potential
this
methodology
improve
engagement
marketing
strategies
for
