Journal of King Saud University - Computer and Information Sciences,
Journal Year:
2023,
Volume and Issue:
36(1), P. 101905 - 101905
Published: Dec. 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.
PLoS ONE,
Journal Year:
2024,
Volume and Issue:
19(7), P. e0306699 - e0306699
Published: July 10, 2024
In
order
to
optimize
the
spectrum
allocation
strategy
of
existing
wireless
communication
networks
and
improve
information
transmission
efficiency
data
security,
this
study
uses
independent
correlation
characteristics
chaotic
time
series
simulate
collection
control
bees,
proposes
an
artificial
bee
colony
algorithm
based
on
uniform
mapping
collaborative
control.
Furthermore,
it
The
method
begins
by
establishing
a
composite
system
uniformly
distributed
Chebyshev
maps.
neighborhood
intervals
where
nectar
sources
are
firmly
connected
relatively
independent,
then
conducts
traversal
search.
research
results
demonstrated
great
performance
suggested
in
each
test
function
as
well
positive
effects
optimization
network
throughput
rate
was
over
300
kbps,
quantity
security
service
eavesdropping
below
0.1,
utilization
algorithm-based
could
be
enhanced
0.8
at
most.
Overall,
proposed
outperformed
comparison
algorithm,
with
high
accuracy
significant
amount
optimization.
This
is
favorable
for
efficient
use
resources
secure
data,
encourages
development
technology
networks.
Applied Computational Intelligence and Soft Computing,
Journal Year:
2024,
Volume and Issue:
2024(1)
Published: Jan. 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
Indonesian Journal of Electrical Engineering and Computer Science,
Journal Year:
2024,
Volume and Issue:
33(3), P. 1829 - 1829
Published: Feb. 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>
Journal of King Saud University - Computer and Information Sciences,
Journal Year:
2023,
Volume and Issue:
36(1), P. 101905 - 101905
Published: Dec. 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.
