In
this
paper,
we
conducted
an
in-depth
study
on
the
diagnosis
and
prognosis
prediction
of
hematoma
dilatation
peripheral
edema
after
hemorrhagic
stroke.
First,
using
data
extracted
from
table
such
as
flow
number
imaging
examination,
a
discriminative
model
expansion
was
established,
which
discriminated
against
based
explicit
conditions
successfully
realized
probability
with
accuracy
rate
nearly
74%.
Secondly,
paper
analyzed
relationship
between
volume
time,
used
K-means
clustering
to
classify
volume,
explored
effect
different
treatment
methods
progression
volume.
Finally,
LSTM
neural
network
construct
for
MRS
score,
further
optimized
treatment.
Applied Mathematics and Nonlinear Sciences,
Год журнала:
2023,
Номер
9(1)
Опубликована: Сен. 25, 2023
Abstract
In
this
paper,
a
talent
training
model
based
on
big
data
analysis
is
designed
for
the
background
of
construction
Hainan
Free
Trade
Port.
A
learning
behavior
method
using
K-Means
clustering
algorithm
and
particle
swarm
optimization
algorithm,
which
can
accurately
mine
valuable
information
from
large
amount
user
provide
reference
exploration
Russian
model.
The
accuracy
rate
in
experimental
validation
reach
91.99%,
outstanding
important
support
establishing
systematic
context
2021 IEEE International Conference on Unmanned Systems (ICUS),
Год журнала:
2023,
Номер
unknown, С. 1225 - 1230
Опубликована: Окт. 13, 2023
With
the
rapid
development
of
artificial
intelligence
technology,
unmanned
aerial
vehicles
and
ground
have
become
increasingly
popular
as
substitutes
for
humans
to
perform
various
search
reconnaissance
other
tasks.
However
one
vehicle
or
is
difficult
meet
multi-dimensional
requirements
task
due
its
limited
load.
This
paper
conducts
research
on
construction
problem
multi-group
platforms
(CPMGUP)
based
Firstly,
tasks
are
grouped
using
k-means
clustering
algorithm.
Then,
an
ability-centered
algorithm
(ACCA)
was
proposed
solve
CPMGUP
groups.
Finally,
numerical
experiments
were
conducted
verify
effectiveness
ACCA
compare
it
with
improved
genetic
Journal of Education and Learning (EduLearn),
Год журнала:
2023,
Номер
18(1), С. 154 - 164
Опубликована: Дек. 21, 2023
Assessment
for
learning
(AFL)
is
a
pedagogical
approach
that
enhances
student
outcomes
through
high-quality
feedback.
This
study
investigates
the
effectiveness
of
integrating
feedback
loop
model
(FLM)
with
AFL
to
improve
students'
engagement
and
understanding
physics,
specifically
in
kinematics
motion
dynamics.
The
employs
mixed-methods
research
design,
combining
quantitative
qualitative
data
assess
impact
FLM-based
approach.
A
one-group
pretest-posttest
design
was
used,
supported
by
instruments
measured
their
conceptual
grasp
physics.
findings
indicate
FLM
into
led
significant
improvements,
evidenced
Cohen’s
effect
size
1.91,
highlighting
substantial
on
learning.
These
results
affirm
positively
affects
contributes
existing
effective
assessment
methods,
providing
valuable
insights
educators
policymakers
developing
enhanced
teaching
strategies.
emphasizes
potential
benefits
incorporating
diverse
educational
settings
elevate
experiences
outcomes.
In
this
paper,
we
conducted
an
in-depth
study
on
the
diagnosis
and
prognosis
prediction
of
hematoma
dilatation
peripheral
edema
after
hemorrhagic
stroke.
First,
using
data
extracted
from
table
such
as
flow
number
imaging
examination,
a
discriminative
model
expansion
was
established,
which
discriminated
against
based
explicit
conditions
successfully
realized
probability
with
accuracy
rate
nearly
74%.
Secondly,
paper
analyzed
relationship
between
volume
time,
used
K-means
clustering
to
classify
volume,
explored
effect
different
treatment
methods
progression
volume.
Finally,
LSTM
neural
network
construct
for
MRS
score,
further
optimized
treatment.
