Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Oct. 28, 2024
Optimizing
agricultural
water
resource
management
is
crucial
for
food
production,
as
effective
can
significantly
improve
irrigation
efficiency
and
crop
yields.
Currently,
precise
demand
forecasting
have
become
key
research
focuses;
however,
existing
methods
often
fail
to
capture
complex
spatial
temporal
dependencies.
To
address
this,
we
propose
a
novel
deep
learning
framework
that
combines
remote
sensing
technology
with
the
UNet-ConvLSTM
(UCL)
model
effectively
integrate
features
from
MODIS
GLDAS
datasets.
Our
leverages
high-resolution
data
UNet
dependencies
captured
by
ConvLSTM
prediction
accuracy.
Experimental
results
demonstrate
our
UCL
achieves
best
$$R^2$$
compared
methods,
reaching
0.927
on
dataset
0.935
dataset.
This
approach
highlights
potential
of
AI
technologies
in
addressing
critical
challenges
management,
contributing
more
sustainable
efficient
production
systems.
Electronics,
Journal Year:
2023,
Volume and Issue:
12(7), P. 1643 - 1643
Published: March 30, 2023
The
financial
market
has
been
developing
rapidly
in
recent
years,
and
the
issue
of
credit
risk
concerning
listed
companies
become
increasingly
prominent.
Therefore,
predicting
is
an
urgent
concern
for
banks,
regulators
investors.
commonly
used
models
are
Z-score,
Logit
(logistic
regression
model),
kernel-based
virtual
machine
(KVM)
neural
network
models.
However,
results
achieved
could
be
more
satisfactory.
This
paper
proposes
a
credit-risk-prediction
model
based
on
CNN-LSTM
attention
mechanism,
Our
approach
benefits
long
short-term
memory
(LSTM)
long-term
time-series
prediction
combined
with
convolutional
(CNN)
model.
Furthermore,
advantages
being
integrated
into
include
reducing
complexity
data,
improving
calculation
speed
training
solving
possible
lack
historical
data
sequence
LSTM
model,
resulting
accuracy.
To
reduce
problems,
we
introduced
mechanism
to
assign
weights
independently
optimize
show
that
our
distinct
compared
other
CNNs,
LSTMs,
CNN-LSTMs
research
credit-risk
listing
formula
significant
meaning.
Sensors,
Journal Year:
2023,
Volume and Issue:
23(8), P. 3852 - 3852
Published: April 10, 2023
Multi-Objective
Multi-Camera
Tracking
(MOMCT)
is
aimed
at
locating
and
identifying
multiple
objects
from
video
captured
by
cameras.
With
the
advancement
of
technology
in
recent
years,
it
has
received
a
lot
attention
researchers
applications
such
as
intelligent
transportation,
public
safety
self-driving
driving
technology.
As
result,
large
number
excellent
research
results
have
emerged
field
MOMCT.
To
facilitate
rapid
development
need
to
keep
abreast
latest
current
challenges
related
field.
Therefore,
this
paper
provide
comprehensive
review
multi-object
multi-camera
tracking
based
on
deep
learning
for
transportation.
Specifically,
we
first
introduce
main
object
detectors
MOMCT
detail.
Secondly,
give
an
in-depth
analysis
evaluate
advanced
methods
through
visualisation.
Thirdly,
summarize
popular
benchmark
data
sets
metrics
quantitative
comparisons.
Finally,
point
out
faced
transportation
present
practical
suggestions
future
direction.
Frontiers in Energy Research,
Journal Year:
2023,
Volume and Issue:
11
Published: May 5, 2023
A
smart
grid
is
a
new
type
of
power
system
based
on
modern
information
technology,
which
utilises
advanced
communication,
computing
and
control
technologies
employs
sensors,
measurement,
communication
devices
that
can
monitor
the
status
operation
various
in
real-time
optimise
dispatch
through
intelligent
algorithms
to
achieve
efficient
system.
However,
due
its
complexity
uncertainty,
how
effectively
perform
prediction
an
important
challenge.
This
paper
proposes
model
attention
mechanism
convolutional
neural
network
(CNN)
combined
with
bi-directional
long
short-term
memory
BiLSTM.The
has
stronger
spatiotemporal
feature
extraction
capability,
more
accurate
capability
better
adaptability
than
ARMA
decision
trees.
The
traditional
models
tree
often
only
use
simple
statistical
methods
for
prediction,
cannot
meet
requirements
high
accuracy
efficiency
load
so
CNN-BiLSTM
Bayesian
optimisation
following
advantages
suitable
compared
tree.
CNN
hierarchical
structure
containing
several
layers
such
as
layer,
pooling
layer
fully
connected
layer.
mainly
used
extracting
features
from
data
images,
dimensionality
reduction
features,
classification
recognition.
core
operation,
locally
weighted
summation
input
extract
data.
In
convolution
different
be
extracted
by
setting
kernels
BiLSTM
capture
semantic
dependencies
both
directions.
consists
two
LSTM
process
sequence
forward
backward
directions
combine
obtain
comprehensive
contextual
information.
access
front
back
inputs
at
each
time
step
results.
It
prevents
gradient
explosion
disappearance
while
capturing
longer-distance
dependencies.
extracts
then
optimises
them
Bayes.
By
collecting
system,
including
power,
load,
weather
other
factors,
our
uses
deeply
learn
grids
key
future
prediction.
Meanwhile,
algorithm
model’s
hyperparameters,
thus
improving
performance.
provide
reference
help
energy
utilisation
Frontiers in Energy Research,
Journal Year:
2023,
Volume and Issue:
11
Published: May 16, 2023
Introduction:
Smart
grid
financial
market
forecasting
is
an
important
topic
in
deep
learning.
The
traditional
LSTM
network
widely
used
time
series
because
of
its
ability
to
model
and
forecast
data.
However,
long-term
forecasting,
the
lack
historical
data
may
lead
a
decline
performance.
This
difficult
problem
for
networks
overcome.
Methods:
In
this
paper,
we
propose
new
deep-learning
address
problem.
WOA-CNN-BiLSTM
combines
bidirectional
long
short-term
memory
BiLSTM
convolution
Advantages
Neural
Network
CNN.
We
replace
with
network,
BiLSTM,
exploit
capturing
dependencies.
It
can
capture
dependencies
modelling.
At
same
time,
use
convolutional
neural
(CNN)
extract
features
better
represent
patterns
regularity
method
combining
CNN
learn
characteristics
more
comprehensively,
thus
improving
accuracy
prediction.
Then,to
further
improve
performance
CNN-BiLSTM
model,
optimize
using
whale
algorithm
WOA.
optimization
algorithm,
which
has
good
global
search
convergence
speed,
complete
short
time.
Results:
Optimizing
through
WOA
reduce
calculation
training
prediction
smart
market,
market.
Experimental
results
show
that
our
proposed
than
other
models
effectively
deal
missing
sequence
forecasting.
Discussion:
provides
necessary
help
development
markets
risk
management
services,
promote
growth
industry.
Our
research
are
great
significance
learning,
provide
effective
idea
solving
grid.
Scientific Reports,
Journal Year:
2023,
Volume and Issue:
13(1)
Published: July 10, 2023
Abstract
Flue-cured
tobacco
grading
plays
a
crucial
role
in
leaf
purchase
and
the
formulation
of
groups.
However,
traditional
flue-cured
mode
is
usually
manual,
which
time-consuming,
laborious,
subjective.
Hence,
it
essential
to
research
more
efficient
intelligent
methods.
Most
existing
methods
suffer
from
classes
less
accuracy
problem.
Meanwhile,
limited
by
different
industry
applications,
datasets
are
hard
be
obtained
publicly.
The
employ
relatively
small
lower
resolution
data
that
apply
practice.
Therefore,
aiming
at
insufficiency
feature
extraction
ability
inadaptability
multiple
grades,
we
collected
largest
highest
dataset
proposed
an
method
based
on
deep
densely
convolutional
network
(DenseNet).
Diverging
other
approaches,
our
has
unique
connectivity
pattern
neural
concatenates
preceding
data.
This
connects
all
previous
layers
subsequent
layer
directly
for
transmission.
idea
can
better
extract
depth
image
information
features
transmit
each
layer’s
data,
thereby
reducing
loss
encouraging
reuse.
Then,
designed
whole
pre-processing
process
experimented
with
learning
algorithms
verify
usability.
experimental
results
showed
DenseNet
could
easily
adapted
changing
output
fully
connected
layers.
With
0.997,
significantly
higher
than
methods,
came
best
model
solving