Under
the
background
of
global
energy
transformation
and
environmental
protection,
application
artificial
intelligence
technology
has
become
an
important
trend
in
oil
gas
field
development
industry.
However,
how
to
effectively
utilize
improve
efficiency
safety
development,
while
addressing
economic
issues
it
brings,
is
a
major
question
that
researchers
need
consider.
Based
on
actual
needs
exploitation,
basic
principles
methods
deep
learning
are
studied,
main
models
training
introduced.
The
process
described
detail,
realization
steps
depth
optimization
model
for
studied.
challenges
including
data
security,
complexity,
computing
resource
demand
so
on.
results
show
as
powerful
tool,
great
potential
security
but
still
faces
some
challenges.
Therefore,
future
research
should
pay
more
attention
these
problems
promote
development.
SSRN Electronic Journal,
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 1, 2024
The
rapid
expansion
of
data
generation
poses
significant
challenges
and
opportunities
for
data-driven
innovation.
This
review
explores
the
utilization
machine
learning
(ML)
deep
(DL)
methodologies
in
big
analytics,
emphasizing
current
advancements,
techniques,
practical
implementations.
We
provide
an
in-depth
examination
ML
methods
large-scale
data,
encompassing
supervised,
unsupervised,
reinforcement
strategies.
Additionally,
we
analyse
various
DL
architectures
such
as
convolutional
neural
networks
(CNNs),
recurrent
(RNNs),
transformers,
which
are
adept
at
identifying
complex
patterns
high-dimensional
datasets.
Data
pre-processing
feature
engineering
crucial
enhancing
quality
utility;
this
discusses
techniques
managing
noise,
handling
missing
extracting
relevant
features.
also
highlight
applications
diverse
fields
healthcare,
finance,
retail,
demonstrating
their
transformative
impact.
addresses
scalability
performance
optimization
essential
effective
deployment
models
contexts.
Emerging
trends
automated
ML,
edge
computing,
potential
integration
quantum
computing
with
discussed,
offering
a
glimpse
into
future
trajectory
analytics.
Ethical
considerations,
including
issues
privacy,
bias,
model
interpretability,
critically
examined
to
ensure
responsible
application
these
technologies.
paper
aims
be
comprehensive
resource
researchers
practitioners
aiming
harness
advanced
Applied Sciences,
Год журнала:
2025,
Номер
15(2), С. 490 - 490
Опубликована: Янв. 7, 2025
The
aging
of
power
plant
pipelines
has
led
to
significant
leaks
worldwide,
causing
environmental
damage,
human
safety
risks,
and
economic
losses.
Rapid
leak
detection
is
critical
for
mitigating
these
issues,
but
challenges
such
as
varying
characteristics,
ambient
noise,
limited
real-world
data
complicate
their
accurate
model
development.
To
address
we
propose
a
that
integrates
stepwise
transfer
learning
an
attention
mechanism.
proposed
utilizes
two-stage
deep
process.
In
Stage
1,
one-dimensional
convolutional
neural
networks
(1D
CNNs)
are
pre-trained
extract
root
mean
square
(RMS)
frequency-domain
features
from
acoustic
signals.
2,
the
classifier
layers
models
removed,
extracted
fused
processed
using
bidirectional
long
short-term
memory
(LSTM)
network.
An
mechanism
incorporated
within
LSTM
prioritize
features,
enhancing
ability
distinguish
signals
noise.
achieved
accuracy
99.99%,
significantly
outperforming
traditional
methods
considered
in
this
study.
By
effectively
addressing
noise
interference
scarcity,
robust
approach
demonstrates
its
potential
enhance
safety,
reduce
improve
cost
efficiency
industrial
infrastructure.
Journal of Applied Data Sciences,
Год журнала:
2024,
Номер
5(2), С. 455 - 473
Опубликована: Май 15, 2024
Integrating
Artificial
Intelligence
(AI)
within
Industry
4.0
has
propelled
the
evolution
of
fault
diagnosis
and
predictive
maintenance
(PdM)
strategies,
marking
a
significant
shift
towards
smarter
paradigms
in
mechatronics
sector.
With
advent
4.0,
mechatronic
systems
have
become
increasingly
sophisticated,
highlighting
critical
need
for
advanced
methodologies
that
are
both
efficient
effective.
This
paper
delves
into
confluence
cutting-edge
AI
techniques,
including
machine
learning
(ML)
deep
(DL),
with
multi-agent
(MAS)
to
enhance
precision
facilitate
PdM
context
4.0.
Specifically,
we
explore
use
various
ML
models,
Support
Vector
Machines
(SVMs)
Random
Forests
(RFs),
DL
architectures
like
Convolutional
Neural
Networks
(CNNs)
Recurrent
(RNNs),
which
been
effectively
oriented
analyses
complex
industrial
data.
Initially,
study
examines
progress
algorithms
accelerate
identification
by
leveraging
data
from
system
operations,
sensors,
historical
trends.
AI-enabled
rapidly
detects
irregularities
discerns
fundamental
causes,
thereby
minimizing
downtime
enhancing
reliability
efficiency.
Furthermore,
this
underscores
adoption
AI-driven
approaches,
emphasizing
prognostics
predict
Remaining
Useful
Life
(RUL)
machinery.
capability
allows
strategic
scheduling
activities,
optimizing
resource
use,
prolonging
lifespan
expensive
assets,
refining
management
spare
parts
inventory.
The
tangible
advantages
employing
showcased
through
case
authentic
implementations.
highlights
successful
implementations,
documenting
real-world
challenges
such
as
integration
issues
interoperability,
elaborates
on
strategies
deployed
navigate
these
obstacles.
results
demonstrate
improved
operational
cost
savings
shed
light
pragmatic
considerations
solutions
MAS
applications.
also
navigates
prospective
research
avenues
applying
domain
setting
stage
ongoing
innovation
exploration
transformative
domain.
Sustainability,
Год журнала:
2024,
Номер
16(11), С. 4432 - 4432
Опубликована: Май 23, 2024
Consumer
decision-making
behaviors
play
a
pivotal
role
in
the
realm
of
purchasing
sustainable
products.
It
is
crucial
for
businesses
to
understand
key
factors
that
influence
consumers’
choices
this
context,
especially
if
they
aim
align
with
eco-friendly
trends.
Conventional
methods
are
inadequate
accurately
and
successfully
identifying
importance
products
stem
from
lack
holistic
consideration.
methods,
like
AHP,
surveys,
questionnaires,
interviews,
focus
groups,
often
do
not
fully
consider
many
aspects
consumer
behavior
related
sustainability.
To
address
gap,
our
study
aims
(1)
employ
hybrid
approach,
integrating
conventional
cutting-edge
machine-learning
technology
predicting
consumer’s
products;
(2)
demonstrate
practical
application
approach
through
example
green
furniture;
(3)
provide
guide
influencing
This
will
map
out
implications
future
The
studying
decision
making
product
purchases,
combining
quantitative
AI
methods.
methodology
provides
comprehensive
analysis
environmentally
friendly
choices,
fostering
awareness
informed
making.
Businesses
can
use
these
insights
tailor
strategies,
enhance
offerings,
meet
rising
demand
products,
contributing
responsible
promoting
economies
scale
innovation.
understanding
creating
socially
marketplace.
Electronics,
Год журнала:
2024,
Номер
13(14), С. 2883 - 2883
Опубликована: Июль 22, 2024
In
this
study,
we
present
a
novel
approach
leveraging
the
segment
anything
model
(SAM)
for
efficient
detection
and
tracking
of
vehicles
in
urban
traffic
surveillance
systems
by
utilizing
uncalibrated
low-resolution
highway
cameras.
This
research
addresses
critical
need
accurate
vehicle
monitoring
intelligent
transportation
(ITS)
smart
city
infrastructure.
Traditional
methods
often
struggle
with
variability
complexity
environments,
leading
to
suboptimal
performance.
Our
harnesses
power
SAM,
an
advanced
deep
learning-based
image
segmentation
algorithm,
significantly
enhance
accuracy
robustness.
Through
extensive
testing
evaluation
on
two
datasets
511
cameras
from
Quebec,
Canada
NVIDIA
AI
City
Challenge
Track
1,
our
algorithm
achieved
exceptional
performance
metrics
including
precision
89.68%,
recall
97.87%,
F1-score
93.60%.
These
results
represent
substantial
improvement
over
existing
state-of-the-art
such
as
YOLO
version
8
single
shot
detector
(SSD),
region-based
convolutional
neural
network
(RCNN).
advancement
not
only
highlights
potential
SAM
real-time
applications,
but
also
underscores
its
capability
handle
diverse
dynamic
conditions
scenes.
The
implementation
technology
can
lead
improved
management,
reduced
congestion,
enhanced
mobility,
making
it
valuable
tool
modern
cities.
outcomes
pave
way
future
advancements
remote
sensing
photogrammetry,
particularly
realm
management.