Journal of King Saud University - Computer and Information Sciences,
Год журнала:
2023,
Номер
36(1), С. 101905 - 101905
Опубликована: Дек. 31, 2023
In
this
paper,
the
main
objective
is
to
estimate
percentage
of
glycosylated
hemoglobin
through
an
easily
accessible
computational
platform
risk
generating
type
2
diabetes
mellitus
in
Mexican
population.
The
estimation
tool
developed
artificial
neural
network
model,
which
was
trained
and
validated
according
a
population
sample
1120
people
between
18
59
years
old.
model
inputs
were
gender,
age,
body
mass
index,
waist
circumference,
weekly
food
consumption,
family
history,
whether
person
suffers
from
any
chronic
degenerative
disease
other
than
T2DM.
We
used
as
output,
estimated
dynamic
glucose
model.
results
present
coefficient
determination
99%,
demonstrating
acceptable
performance
aid
for
health
personnel,
seeks
generate
first
approximation
glycemic
status
those
communities
with
high
marginalization
index
prevention
strategies.
Applied Computational Intelligence and Soft Computing,
Год журнала:
2024,
Номер
2024(1)
Опубликована: Янв. 1, 2024
Artificial
neural
networks
(ANNs)
are
widely
used
machine
learning
techniques
with
applications
in
various
fields.
Heuristic
search
optimization
methods
typically
to
minimize
the
loss
function
ANNs.
However,
these
can
lead
network
become
stuck
local
optima,
limiting
performance.
To
overcome
this
challenge,
study
introduces
an
improved
approach,
improvement
of
reinforcement
artificial
bee
colony
(improved
R‐ABC)
algorithm,
enhance
process
for
The
proposed
method
aims
limitations
heuristic
and
improve
efficiency
weight
adjustment
This
new
approach
enhances
discovery
phase
traditional
R‐ABC
by
including
parameters
neighboring
food
sources,
augmenting
capabilities
finding
optimal
solution.
performance
was
compared
ANNs
utilizing
backpropagation
stochastic
gradient
descent
(SGD)
Adam
optimizers,
as
well
other
swarm
intelligence
(SI)
such
particle
(PSO)
R‐ABC.
results
showed
that
both
PSO
continuously
solutions
across
all
benchmark
datasets.
In
iris
dataset,
SI
approaches
consistently
achieved
F
1‐scores
exceeding
0.94,
outperforming
SGD
Adam.
For
datasets,
generally
outperformed
methods.
indicate
when
is
applied
ANNs,
it
outperforms
optimization,
especially
size
expands.
Although
faster
execution
times
TensorFlow,
suggests
using
model
accuracy
efficiency.
Advanced
increase
ability
obtain
solutions.
Enhanced
algorithms
significantly
ANN
training
efficiency,
complex
high‐dimensional
Indonesian Journal of Electrical Engineering and Computer Science,
Год журнала:
2024,
Номер
33(3), С. 1829 - 1829
Опубликована: Фев. 16, 2024
<div>Cloud
computing
(CC)
is
a
rapidly
developing
IT
approach
with
intrusion
detection
system
being
crucial
tool
for
safeguarding
virtual
networks
and
machines
from
potential
threats,
thereby
mitigating
security
concerns
in
the
cloud
environment.
The
(IDS)
demands
significant
improvements,
primarily
based
on
optimizing
performance
bolstering
measures.
This
research
aims
to
implement
an
IDS
utilizing
deep
learning
(DL)
method.
DL
model
promising
technique
widely
used
detect
intrusions.
implemented
hierarchical
long
short-term
memory
(HLSTM)
method’s
evaluated
feature
selection
through
variance
threshold-based
regression
(VTR)
two
network
datasets:
Bot-IoT
lab-knowledge
discovery
data
mining
(NSL-KDD).
paper
concludes
use
of
resulting
high
performance.
Moreover,
method
NSL-KDD
datasets
obtains
respective
accuracies
99.50%
0.995.
It
compared
existing
methods
namely,
ensemble
ID
CC
DL,
LeNet,
fuzzy
neural
Honey
Bader
algorithm
privacy-preserving
ID,
improved
metaheuristics
logic-based
security,
beluga
whale-tasmanian
devil
optimization
convolutional
(CNN)
TL,
chronological
slap
swarm
algorithm-based
belief
(DBN),
dragonfly
invasive
weed
optimization-based
Shepard
CNN.</div>
Applied Computational Intelligence and Soft Computing,
Год журнала:
2024,
Номер
2024(1)
Опубликована: Янв. 1, 2024
COVID‐19
has
significantly
impacted
peoples’
mental
health
because
of
isolation
and
social
distancing
measures.
It
practically
impacts
every
segment
people’s
daily
lives
causes
a
medical
problem
that
spreads
throughout
the
entire
world.
This
pandemic
caused
an
increased
emotional
distress.
Since
everyone
been
affected
by
epidemic
physically,
emotionally,
financially,
it
is
crucial
to
examine
comprehend
reactions
as
crisis
affects
health.
study
uses
Twitter
data
understand
what
people
feel
during
pandemic.
We
collected
about
isolation,
preprocessed
text,
then
classified
tweets
into
various
emotion
classes.
The
are
using
twarc
library
academic
researcher
account
labeled
Vader
analyzer
after
preprocessing.
trained
five
machine
learning
models,
namely,
support
vector
(SVM),
Naïve
Bayes,
KNN,
decision
tree,
logistic
regression
find
patterns
trends
in
emotions.
individuals
analyzed.
applied
precision,
recall,
F
1‐score,
accuracy
evaluation
metrics,
which
shows
SVM
performed
best
among
other
models.
Our
results
show
isolated
felt
emotions,
out
which,
fear,
sadness,
surprise
were
most
common.
gives
insights
impact
power
understanding
outcomes.
findings
can
be
used
develop
targeted
interventions
strategies
address
toll
