Transactions on Emerging Telecommunications Technologies,
Journal Year:
2025,
Volume and Issue:
36(3)
Published: March 1, 2025
ABSTRACT
Workload
prediction
is
the
necessary
factor
in
cloud
data
center
for
maintaining
elasticity
and
scalability
of
resources.
However,
accuracy
workload
very
low,
because
redundancy,
noise,
low
center.
In
this
manuscript,
Prediction
Cloud
Data
Centers
using
Complex‐Valued
Spatio‐Temporal
Graph
Convolutional
Neural
Network
Optimized
with
Gazelle
Optimization
Algorithm
(CVSTGCN‐WLP‐CDC)
proposed.
Initially,
input
collected
from
two
standard
datasets
such
as
NASA
Saskatchewan
HTTP
traces
dataset.
Then,
preprocessing
Multi‐Window
Savitzky–Golay
Filter
(MWSGF)
used
to
remove
noise
redundant
data.
The
preprocessed
fed
CVSTGCN
a
dynamic
environment.
work,
proposed
Approach
(GOA)
enhance
weight
bias
parameters.
CVSTGCN‐WLP‐CDC
technique
executed
efficacy
based
on
structure
evaluated
several
performances
metrics
accuracy,
recall,
precision,
energy
consumption
correlation
coefficient,
sum
index
(SEI),
root
mean
square
error
(RMSE),
squared
(MPE),
percentage
(PER).
provides
23.32%,
28.53%
24.65%
higher
accuracy;
22.34%,
25.62%,
22.84%
lower
when
comparing
existing
methods
Artificial
Intelligence
augmented
evolutionary
approach
espoused
centres
architecture
(TCNN‐CDC‐WLP),
Performance
analysis
machine
learning
centered
techniques
(PA‐BPNN‐CWPC),
Machine
effectual
utilization
centers
(ARNN‐EU‐CDC)
respectively.
Transactions on Emerging Telecommunications Technologies,
Journal Year:
2023,
Volume and Issue:
34(12)
Published: Sept. 14, 2023
Abstract
With
the
rapid
development
of
telecommunication
networks,
predictability
network
traffic
is
significant
interest
in
analysis
and
optimization,
bandwidth
allocation,
load
balancing
adjustment.
Consequently,
recent
years,
research
attention
has
been
paid
to
forecasting
traffic.
Telecommunication
problems
can
be
considered
a
time‐series
problem,
wherein
periodic
historical
data
fed
as
input
model.
Time‐series
approaches
are
broadly
categorized
statistical
machine
learning
(ML)
methods
their
combinations.
Statistical
forecast
linear
characteristics
data,
unable
capture
nonlinear
complex
patterns.
ML‐based
model
data.
In
hybrid
combining
have
widely
used
characteristics.
However,
performance
these
highly
depends
on
feature
selection
techniques
hyper‐parameter
tuning
ML
methods.
A
novel
method
proposed
for
short‐term
based
hyperparameter
optimization
address
this
problem.
It
combines
components
First,
technique,
modified
mutual
information
combination
targets,
find
candidate
variables.
Next,
vector
auto
regressive
moving
average
(VARMA),
long
memory
(LSTM),
multilayer
perceptron
(MLP),
called
VARMA‐LSTM‐MLP
forecaster,
suggested
metaheuristic
algorithm,
composed
firefly
BAT,
employed
optimal
set
values.
The
assessed
by
real‐world
dataset
containing
Tehran
city's
daily
IRAN.
evaluation
results
demonstrate
that
outperforms
existing
terms
mean
squared
error
absolute
error.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 10, 2025
Here,
we
present
a
remarkable
methodology
for
unveiling
subsurface
structures
with
the
potential
to
transform
exploration
of
mineral
and
ores
resources,
as
well
study
volcanic
activity.
By
incorporating
Metaheuristic
Bat
algorithm
(MBA)
second
horizontal
gravity
gradient
(SHG)
employing
variable
window
lengths,
aim
eliminate
regional
effect
in
data,
thereby
improving
precision
structure
parameter
estimation.
Through
rigorous
evaluation
on
synthetic
cases,
have
demonstrated
robustness
our
approach
its
ability
handle
diverse
geological
complexities
noise
levels.
Furthermore,
method
has
been
applied
actual
data
from
three
distinct
locations:
Canada,
India,
Cuba,
yielding
excellent
results
that
confirm
reliability
applicability
real-world
settings.
We
are
confident
use
lengths
SHG
computation,
coupled
optimization
global
optimal
solution
via
Algorithm,
can
significantly
contribute
enhanced
structural
hope
research
will
inspire
others
explore
this
groundbreaking
continue
advancing
field
optimization.
