IEEE Transactions on Engineering Management,
Год журнала:
2024,
Номер
71, С. 3566 - 3579
Опубликована: Янв. 1, 2024
Digital
marketing
refers
to
the
process
of
promoting,
selling,
and
delivering
products
or
services
through
online
platforms
channels
using
internet
electronic
devices
in
a
digital
environment.
Its
aim
is
attract
engage
target
audiences
various
strategies
methods,
driving
brand
promotion
sales
growth.
The
primary
objective
this
scholarly
study
seamlessly
integrate
advanced
big
data
analytics
artificial
intelligence
(AI)
technology
into
realm
marketing,
thereby
fostering
progression
optimization
sustainable
practices.
First,
characteristics
applications
involving
vast,
diverse,
complex
datasets
are
analyzed.
Understanding
their
attributes
scope
application
essential.
Subsequently,
comprehensive
investigation
AI-driven
learning
mechanisms
conducted,
culminating
development
an
AI
random
forest
model
(RFM)
tailored
for
marketing.
Subsequent
this,
leveraging
real-world
case
enterprise
X,
fundamental
customer
collected
subjected
meticulous
analysis.
RFM
model,
ingeniously
crafted
study,
then
deployed
prognosticate
anticipated
count
prospective
customers
said
enterprise.
empirical
findings
spotlight
pronounced
prevalence
university-affiliated
individuals
across
diverse
age
cohorts.
In
terms
occupational
distribution
within
base,
categories
workers
educators
emerge
as
dominant,
constituting
41%
31%
demographic,
respectively.
Furthermore,
price
patrons
exhibits
skewed
pattern,
whereby
bracket
0–150
encompasses
17%
population,
whereas
range
150–300
captures
notable
52%.
These
delineated
bands
collectively
constitute
substantial
proportion,
exceeding
450
embodies
minority,
accounting
less
than
20%.
Notably,
devised
endeavor
demonstrates
remarkable
proficiency
accurately
projecting
forthcoming
passenger
volumes
over
seven-day
horizon,
significantly
surpassing
predictive
capability
logistic
regression.
Evidently,
proffered
herein
excels
precise
anticipation
counts,
furnishing
pragmatic
foundation
intelligent
evolution
strategies.
Geosciences,
Год журнала:
2025,
Номер
15(2), С. 47 - 47
Опубликована: Фев. 2, 2025
The
accurate
prediction
of
rock
mass
quality
ahead
the
tunnel
face
is
crucial
for
optimizing
construction
strategies,
enhancing
safety,
and
reducing
geological
risks.
This
study
developed
three
hybrid
models
using
random
forest
(RF)
optimized
by
moth-flame
optimization
(MFO),
gray
wolf
optimizer
(GWO),
Bayesian
(BO)
algorithms
to
classify
surrounding
in
real
time
during
boring
machine
(TBM)
operations.
A
dataset
with
544
TBM
tunneling
samples
included
key
parameters
such
as
thrust
force
per
cutter
(TFC),
revolutions
minute
(RPM),
penetration
rate
(PR),
advance
(AR),
revolution
(PRev),
field
index
(FPI),
classification
based
on
Rock
Mass
Rating
(RMR)
method.
To
address
class
imbalance,
Borderline
Synthetic
Minority
Over-Sampling
Technique
was
applied.
Performance
assessments
revealed
MFO-RF
model’s
superior
performance,
training
testing
accuracies
0.992
0.927,
respectively,
predictors
identified
PR,
AR,
RPM.
Additional
validation
91
data
sets
confirmed
reliability
model
unseen
data,
achieving
an
accuracy
0.879.
graphical
user
interface
also
developed,
enabling
engineers
technicians
make
instant
reliable
predictions,
greatly
supporting
safe
operational
efficiency.
These
contribute
valuable
tools
real-time,
data-driven
decision-making
projects.
Applied Sciences,
Год журнала:
2023,
Номер
13(3), С. 1345 - 1345
Опубликована: Янв. 19, 2023
Blasting
operations
involve
some
undesirable
environmental
issues
that
may
cause
damage
to
equipment
and
surrounding
areas.
One
of
them,
probably
the
most
important
one,
is
flyrock
induced
by
blasting,
where
its
accurate
estimation
before
operation
essential
identify
blasting
zone’s
safety
zone.
This
study
introduces
several
tree-based
solutions
for
an
prediction
flyrock.
has
been
done
using
four
techniques,
i.e.,
decision
tree
(DT),
random
forest
(RF),
extreme
gradient
boosting
(XGBoost),
adaptive
(AdaBoost).
The
modelling
techniques
was
conducted
with
in-depth
knowledge
understanding
their
influential
factors.
mentioned
factors
were
designed
through
use
parametric
investigations,
which
can
also
be
utilized
in
other
engineering
fields.
As
a
result,
all
models
are
capable
enough
blasting-induced
prediction.
However,
predicted
values
obtained
AdaBoost
technique.
Observed
forecasted
training
testing
phases
received
coefficients
determination
(R2)
0.99
0.99,
respectively,
confirm
power
this
technique
estimating
Additionally,
according
results
input
parameters,
powder
factor
had
highest
influence
on
flyrock,
whereas
burden
spacing
lowest
impact
Mechanics of Advanced Materials and Structures,
Год журнала:
2023,
Номер
31(23), С. 5999 - 6014
Опубликована: Июнь 22, 2023
Concrete
production
contributes
significantly
to
global
greenhouse
gas
emissions,
and
its
manufacture
requires
substantial
natural
resources.
These
concerns
can
be
partly
mitigated
by
recycling
construction
demolition
waste
as
aggregates
produce
Recycled
Aggregate
(RAC).
RAC
has
gained
momentum
due
lower
environmental
impact,
costs,
increased
sustainability.
The
aim
of
this
study
was
advance
the
reasonable
use
recycled
aggregate
in
concrete
achieve
optimal
mixture
ratio
design.
Four
advanced
machine
learning
algorithms,
Support
Vector
Machine
(SVR),
Light
Gradient
Boosting
(LGBM),
Random
Forest
(RF),
Multi-Layer
Perceptron
(MLP),
were
employed,
novel
optimization
biogeography-based
(BBO),
Multi-Verse
Optimizer
(MVO)
Gravitational
Search
Algorithm
(GSA),
integrated
predict
compressive
strength
RAC.
Six
potential
influential
factors
for
considered
models.
employed
four
evaluation
metrics,
Taylor
diagrams
Regression
Error
Characteristic
plots
compare
model
performance.
result
shows
LGBM-based
hybrid
outperformed
other
methods,
demonstrating
high
accuracy
predicting
strength.
Shapley
Additive
Explanation
(SHAP)
results
emphasize
importance
understanding
interactions
between
various
their
effects
on
mechanical
properties
findings
inform
development
more
sustainable
environmentally
friendly
building
materials.
Mathematics,
Год журнала:
2023,
Номер
11(10), С. 2358 - 2358
Опубликована: Май 18, 2023
One
of
the
most
undesirable
consequences
induced
by
blasting
in
open-pit
mines
and
civil
activities
is
flyrock.
Furthermore,
production
oversize
boulders
creates
many
problems
for
continuation
work
usually
imposes
additional
costs
on
project.
In
this
way,
breakage
associated
with
throwing
small
fragments
particles
at
high
speed,
which
can
lead
to
serious
risks
human
resources
infrastructures.
Hence,
accurate
prediction
flyrock
boulder
crucial
avoid
possible
its’
environmental
side
effects.
This
study
attempts
develop
an
optimized
artificial
neural
network
(ANN)
particle
swarm
optimization
(PSO)
jellyfish
search
algorithm
(JSA)
construct
hybrid
models
anticipating
distance
resulting
a
quarry
mine.
The
PSO
JSA
algorithms
were
used
determine
optimum
values
neurons’
weight
biases
connected
neurons.
regard,
database
involving
65
monitored
recording
was
collected
that
comprises
six
influential
parameters
distance,
i.e.,
hole
depth,
burden,
angle,
charge
weight,
stemming,
powder
factor
one
target
parameter,
distance.
ten
various
ANN,
PSO–ANN,
JSA–ANN
established
estimating
their
results
investigated
applying
three
evaluation
indices
coefficient
determination
(R2),
root
mean
square
error
(RMSE)
value
accounted
(VAF).
calculation
indicators
revealed
R2,
(0.957,
0.972
0.995)
(0.945,
0.954
0.989)
determined
train
test
proposed
predictive
models,
respectively.
yielded
denoted
although
ANN
model
capable
PSO–ANN
anticipate
more
accuracy.
performance
accuracy
level
estimate
better
compared
models.
Therefore,
identified
as
superior
from
blasting.
final,
sensitivity
analysis
conducted
showed
factor,
angle
have
impact
changes.
Mathematics,
Год журнала:
2023,
Номер
11(14), С. 3064 - 3064
Опубликована: Июль 11, 2023
The
criteria
for
measuring
soil
compaction
parameters,
such
as
optimum
moisture
content
and
maximum
dry
density,
play
an
important
role
in
construction
projects.
On
sites,
base/sub-base
soils
are
compacted
at
the
optimal
to
achieve
desirable
level
of
compaction,
generally
between
95%
98%
density.
present
technique
determining
parameters
laboratory
is
a
time-consuming
task.
This
study
proposes
improved
hybrid
intelligence
paradigm
alternative
tool
method
estimating
density
soils.
For
this
purpose,
advanced
version
grey
wolf
optimiser
(GWO)
called
GWO
(IGWO)
was
integrated
with
adaptive
neuro-fuzzy
inference
system
(ANFIS),
which
resulted
high-performance
model
named
ANFIS-IGWO.
Overall,
results
indicate
that
proposed
ANFIS-IGWO
achieved
most
precise
prediction
(degree
correlation
=
0.9203
root
mean
square
error
0.0635)
0.9050
0.0709)
outcomes
suggested
noticeably
superior
those
attained
by
other
ANFIS
models,
built
standard
GWO,
Moth-flame
optimisation,
slime
mould
algorithm,
marine
predators
algorithm.
geotechnical
engineers
can
benefit
from
newly
developed
during
design
stage
civil
engineering
MATLAB
models
also
included
parameters.
