Intelligent evaluation system for new energy vehicles based on sentiment analysis: An MG-PL-3WD method
Engineering Applications of Artificial Intelligence,
Год журнала:
2024,
Номер
133, С. 108485 - 108485
Опубликована: Апрель 25, 2024
Язык: Английский
Unstructured Text Data Security Attribute Mining Method Based on Multi‐Model Collaboration
Concurrency and Computation Practice and Experience,
Год журнала:
2025,
Номер
37(3)
Опубликована: Янв. 20, 2025
ABSTRACT
Access
control
is
a
critical
security
measure
to
ensure
that
sensitive
information
and
resources
are
accessed
only
by
authorized
users.
However,
attribute‐based
access
in
the
big
data
environment
faces
challenges
such
as
large
number
of
entity
attributes,
poor
availability,
difficulty
manual
labeling.
In
this
paper,
we
focus
on
problem
mining
optimizing
attributes
unstructured
propose
method
for
textual
based
multi‐model
collaboration.
First,
utilize
unsupervised
methods
extract
candidate
from
resources,
then
weight
results
multiple
using
rough
set
theory
obtain
optimal
result.
Second,
considering
various
factors
including
text
itself
construct
feature
vector
consisting
45
categories
represent
attributes.
Third,
employ
voting
collaboratively
train
attribute
model
resources.
Finally,
HowNet,
optimize
achieve
automated
intelligent
resource
providing
an
foundation
precise
control.
The
experiments
indicate
precision
rate
proposed
paper
can
reach
up
92.36%,
F1‐score
82.51%.
scale
be
compressed
69.59%
its
original
size
after
optimization.
This
has
greater
advantage
over
other
provide
support
Язык: Английский
A local multi-granularity fuzzy rough set method for multi-attribute decision making based on MOSSO-LSTM and its application in stock market
Applied Intelligence,
Год журнала:
2024,
Номер
54(7), С. 5728 - 5747
Опубликована: Апрель 1, 2024
Язык: Английский
Modelling customer requirement for mobile games based on online reviews using BW-CNN and S-Kano models
Expert Systems with Applications,
Год журнала:
2024,
Номер
258, С. 125142 - 125142
Опубликована: Авг. 24, 2024
Язык: Английский
Personalized movie recommendations based on probabilistic linguistic sentiment and integrated DEMATEL-TODIM methods
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Авг. 27, 2024
With
the
development
of
society,
online
reviews
are
increasingly
becoming
a
crucial
factor
in
decision-making.
Especially
for
entertainment
products
such
as
movies,
they
preferred
their
affordability
and
high
factor.
Therefore,
this
paper
proposes
movie
recommendation
model
that
considers
user
personalization
using
probabilistic
linguistic
approach
based
on
reviews.
Firstly,
method
constructs
quantitative
sentiment
framework
transforms
comments
into
multi-granular
language.
Secondly,
we
build
decision-making
trial
evaluation
laboratory
(DEMATEL)
environments
to
explore
interrelationships
between
product
attributes,
improve
distance
measure
score
function
better
integrate
information
DEMATEL
weight
calculations.
Furthermore,
account
risk
preferences,
employs
extended
TODIM
(an
acronym
Portuguese
interactive
multicriteria
decision
making)
methodology
determine
ranking
alternatives.
Finally,
design
Douban
experiments
demonstrate
validity
model.
Compared
with
other
methods,
incorporates
emotional
tendency
attributes
preference
process
leading
more
reasonable
results.
Язык: Английский