Research Square (Research Square),
Год журнала:
2023,
Номер
unknown
Опубликована: Июнь 23, 2023
Abstract
Advancements
in
computing
and
storage
technologies
have
significantly
contributed
to
the
adoption
of
deep
learning
(DL)-based
models
among
machinelearning
(ML)
experts.
Although
a
generic
model
can
be
used
search
fora
near-optimal
solution
any
problem
domain,
what
makes
these
DL
modelscontext-sensitive
is
combination
training
data
hyperparameters.
Due
lack
inherent
explainability
HyperparameterOptimization
(HPO)
or
tuning
specific
each
art,science,
experience.
In
this
article,
we
explored
various
existing
methods
ways
identify
optimal
set
values
for
hyperparameters
specificto
along
with
techniques
realize
those
real-lifesituations.
The
article
also
includes
detailed
comparative
study
variousstate-of-the-art
HPO
using
Keras
Tuner
toolkit
highlights
observations
describing
how
performance
improvedby
applying
techniques.
Bioengineering,
Год журнала:
2024,
Номер
11(1), С. 77 - 77
Опубликована: Янв. 13, 2024
Accurate
classification
of
electromyographic
(EMG)
signals
is
vital
in
biomedical
applications.
This
study
evaluates
different
architectures
recurrent
neural
networks
for
the
EMG
associated
with
five
movements
right
upper
extremity.
A
Butterworth
filter
was
implemented
signal
preprocessing,
followed
by
segmentation
into
250
ms
windows,
an
overlap
190
ms.
The
resulting
dataset
divided
training,
validation,
and
testing
subsets.
Grey
Wolf
Optimization
algorithm
applied
to
gated
unit
(GRU),
long
short-term
memory
(LSTM)
architectures,
bidirectional
networks.
In
parallel,
a
performance
comparison
support
vector
machines
(SVMs)
performed.
results
obtained
first
experimental
phase
revealed
that
all
RNN
evaluated
reached
100%
accuracy,
standing
above
93%
achieved
SVM.
Regarding
speed,
LSTM
ranked
as
fastest
architecture,
recording
time
0.12
ms,
GRU
0.134
Bidirectional
showed
response
0.2
while
SVM
had
longest
at
2.7
second
phase,
slight
decrease
accuracy
models
observed,
98.46%
LSTM,
96.38%
GRU,
97.63%
network.
findings
this
highlight
effectiveness
speed
task.
Atmosphere,
Год журнала:
2025,
Номер
16(4), С. 419 - 419
Опубликована: Апрель 4, 2025
Accurate
drought
prediction
is
crucial
for
optimizing
water
resource
allocation,
safeguarding
agricultural
productivity,
and
maintaining
ecosystem
stability.
This
study
develops
a
methodological
framework
short-term
forecasting
using
SPEI
time
series
(1979–2020)
evaluates
three
predictive
models:
(1)
baseline
XGBoost
model
(XGBoost1),
(2)
feature-optimized
variant
incorporating
Pearson
correlation
analysis
(XGBoost2),
(3)
an
enhanced
CPSO-XGBoost
integrating
hybrid
particle
swarm
optimization
with
dual
mechanisms
of
binary
feature
selection
parameter
tuning.
Key
findings
reveal
spatiotemporal
patterns:
temporal-scale
dependencies
show
all
models
exhibit
limited
capability
at
SPEI-1
(R2:
0.32–0.41,
RMSE:
0.68–0.79)
but
achieve
progressive
accuracy
improvement,
peaking
SPEI-12
where
attains
optimal
performance
0.85–0.90,
0.33–0.43)
18.7–23.4%
error
reduction
versus
baselines.
Regionally,
humid
zones
(South
China/Central-Southern)
demonstrate
peak
(R2
≈
0.90,
RMSE
<
0.35),
while
arid
regions
(Northwest
Desert/Qinghai-Tibet
Plateau)
dramatic
improvement
from
0.35,
>
1.0)
to
0.85,
52%).
Multivariate
probability
density
confirms
the
model’s
robustness
through
capture
nonlinear
atmospheric-land
interactions
reduced
parameterization
uncertainties
via
intelligence
optimization.
The
CPSO-XGBoost’s
superiority
stems
synergistic
optimization:
enhances
input
relevance
adaptive
tuning
improves
computational
efficiency,
collectively
addressing
climate
variability
challenges
across
diverse
terrains.
These
establish
advanced
early
warning
systems,
providing
critical
support
climate-resilient
management
risk
mitigation
spatiotemporally
predictions.
Applied Sciences,
Год журнала:
2025,
Номер
15(9), С. 5053 - 5053
Опубликована: Май 1, 2025
The
accurate
prediction
of
concrete
temperature
during
arch
dam
construction
is
essential
for
crack
prevention.
internal
the
poured
blocks
influenced
by
dynamic
factors
such
as
material
properties,
age,
heat
dissipation
conditions,
and
control
measures,
which
are
highly
time-varying.
Conventional
models,
rely
on
offline
data
training,
struggle
to
capture
these
time-varying
dynamics,
resulting
in
insufficient
accuracy.
To
overcome
limitations,
this
study
constructed
a
sparrow
search
algorithm–incremental
support
vector
regression
(SSA-ISVR)
model
online
prediction.
First,
SSA
was
employed
optimize
penalty
kernel
coefficients
ISVR
algorithm,
minimizing
errors
between
predicted
measured
temperatures
establish
pretrained
initial
model.
Second,
untrained
samples
were
dynamically
monitored
incorporated
using
Karush–Kuhn–Tucker
(KKT)
conditions
identify
unlearned
information,
prompting
updates.
Additionally,
redundant
removed
based
sample
similarity
error-driven
criteria
enhance
training
efficiency.
Finally,
model’s
accuracy
reliability
validated
through
actual
case
studies
compared
LSTM,
BP,
models.
results
indicate
that
SSA-ISVR
outperforms
aforementioned
effectively
capturing
changes
accurately
predicting
variations,
with
mean
absolute
error
0.14
°C.