International Journal of Renewable Energy Development,
Journal Year:
2024,
Volume and Issue:
13(6), P. 1175 - 1190
Published: Oct. 27, 2024
In
the
ongoing
search
for
an
alternative
fuel
diesel
engines,
biogas
is
attractive
option.
Biogas
can
be
used
in
dual-fuel
mode
with
as
pilot
fuel.
This
work
investigates
modeling
of
injecting
strategies
a
waste-derived
biogas-powered
engine.
Engine
performance
and
emissions
were
projected
using
supervised
machine
learning
methods
including
random
forest,
lasso
regression,
support
vector
machines
(SVM).
Mean
Squared
Error
(MSE),
R-squared
(R²),
Absolute
Percentage
(MAPE)
among
criteria
evaluations
models.
Random
Forest
has
shown
better
Brake
Thermal
Efficiency
(BTE)
test
R²
0.9938
low
MAPE
3.0741%.
once
more
exceeded
other
models
0.9715
4.2242%
estimating
Specific
Energy
Consumption
(BSEC).
With
0.9821
2.5801%
emerged
most
accurate
model
according
to
carbon
dioxide
(CO₂)
emission
modeling.
Analogous
results
monoxide
(CO)
prediction
based
on
obtained
0.8339
3.6099%.
outperformed
Linear
Regression
0.9756%
7.2056%
case
nitrogen
oxide
(NOx)
emissions.
showed
constant
overall
criteria.
paper
emphasizes
how
well
especially
prognosticate
engines.
Journal of Industrial and Management Optimization,
Journal Year:
2024,
Volume and Issue:
20(12), P. 3816 - 3842
Published: Jan. 1, 2024
Data
analysis
is
very
important
in
many
fields
of
science.
The
most
preferred
methods
data
linear
regression
due
to
its
simplicity
interpret
and
ease
application.
One
the
assumptions
accepted
while
obtaining
that
there
no
correlation
between
independent
variables
model
which
refers
absence
multicollinearity.
As
a
result
multicollinearity,
variance
parameter
estimates
will
be
high
this
reduces
accuracy
reliability
models.
Shrinkage
aim
handle
multicollinearity
problem
by
minimizing
estimators
model.
Ridge
Regression,
Lasso,
Elastic-Net
are
applied
different
simulated
sets
with
characteristics
also
real
world
sets.
Based
on
performance
results,
compared
according
multi-criteria
decision
making
method
named
TOPSIS,
order
preference
determined
for
each
set.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(23), P. 4479 - 4479
Published: Nov. 29, 2024
Nitrogen
is
the
main
nutrient
element
in
growth
process
of
white
radish,
and
accurate
monitoring
radish
leaf
nitrogen
content
(LNC)
an
important
guide
for
precise
fertilization
decisions
field.
Using
LNC
as
object,
research
on
hyperspectral
estimation
methods
was
carried
out
based
field
sample
data
at
multiple
stages
using
feature
selection
integrated
learning
algorithm
models.
First,
Vegetation
Index
(VI)
constructed
from
data.
We
extracted
sensitive
features
VI
response
to
Pearson’s
feature-selection
approach.
Second,
a
stacking-integrated
approach
proposed
machine
algorithms
such
Support
Vector
Machine
(SVM),
Random
Forest
(RF),
Ridge
K-Nearest
Neighbor
(KNN)
base
model
first
layer
architecture,
Lasso
meta-model
second
realize
LNC.
The
analysis
results
show
following:
(1)
bands
are
mainly
centered
around
600–700
nm
1950
nm,
VIs
also
concentrated
this
band
range.
(2)
Stacking
with
spectral
inputs
achieved
good
prediction
accuracy
leaf,
R2
=
0.7,
MAE
0.16,
MSE
0.05
estimated
over
whole
stage
radish.
(3)
variable
filtering
function
chosen
meta-model,
which
has
redundant
model-selection
effect
helps
improve
quality
framework.
This
study
demonstrates
potential
method
stages.
