Journal of risk and financial management,
Journal Year:
2024,
Volume and Issue:
17(4), P. 143 - 143
Published: April 2, 2024
In
this
paper,
we
utilize
a
machine
learning
model
(the
convolutional
neural
network)
to
analyze
aerial
images
of
winter
hard
red
wheat
planted
areas
and
cloud
coverage
over
the
as
proxy
for
future
yield
forecasts.
We
trained
our
forecast
futures
price
20
days
ahead
provide
recommendations
either
long
or
short
position
on
futures.
Our
method
shows
that
achieving
positive
alpha
within
time
window
is
possible
if
algorithm
data
choice
are
unique.
However,
model’s
performance
can
deteriorate
quickly
input
become
more
easily
available
and/or
trading
strategy
becomes
crowded,
was
case
with
imagery
utilized
in
paper.
International Journal of Quality & Reliability Management,
Journal Year:
2024,
Volume and Issue:
41(8), P. 2199 - 2225
Published: Feb. 26, 2024
Purpose
Deep
learning
(DL)
is
on
the
rise
because
it
can
make
predictions
and
judgments
based
data
that
unseen.
Blockchain
technologies
are
being
combined
with
DL
frameworks
in
various
industries
to
provide
a
safe
effective
infrastructure.
The
review
comprises
literature
lists
most
recent
techniques
used
aforementioned
application
sectors.
We
examine
current
research
trends
across
several
fields
evaluate
terms
of
its
advantages
disadvantages.
Design/methodology/approach
integration
blockchain
has
been
explored
domains
for
past
five
years
(2018–2023).
Our
guided
by
questions,
these
we
concentrate
key
such
as
usage
Internet
Things
(IoT)
applications,
healthcare
cryptocurrency
price
prediction.
have
analyzed
main
challenges
possibilities
concerning
technologies.
discussed
methodologies
pertinent
publications
areas
contrasted
during
previous
years.
Additionally,
comparison
widely
create
blockchain-based
frameworks.
Findings
By
responding
objectives,
study
highlights
assesses
effectiveness
already
published
works
using
DL.
findings
indicate
IoT
their
use
smart
cities
cars,
cryptocurrency,
research.
primary
focus
enhancement
existing
systems,
analysis,
storage
sharing
via
decentralized
systems
motivation
this
integration.
Amongst
employed,
Ethereum
Hyperledger
popular
among
researchers
domain
healthcare,
whereas
Bitcoin
cryptocurrency.
Originality/value
There
lack
summarizes
state-of-the-art
methods
incorporating
analyze
done
(2018–2023)
issues
emerging
trends.
BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN,
Journal Year:
2025,
Volume and Issue:
19(1), P. 385 - 396
Published: Jan. 13, 2025
Indonesia's
agricultural
sector
plays
a
crucial
role
in
the
national
economy,
with
significant
export
potential
and
supporting
livelihoods
of
majority
population.
As
part
government's
vision
to
make
Indonesia
world's
food
barn
by
2045,
increasing
volume
value
product
exports
is
primary
focus,
making
forecasting
essential
for
strategic
decision-making.
Sequential
data
analysis
an
important
approach
analyzing
collected
over
specific
period.
This
study
aims
compare
two
popular
methods
sector,
namely
Seasonal
AutoRegressive
Integrated
Moving
Average
(SARIMA)
model
Long
Short-Term
Memory
(LSTM)
model.
Monthly
from
West
Java
Province
January
2013
February
2024
were
used
as
dataset.
The
best
SARIMA
generated
was
(1,1,1)(0,1,1)12,
while
optimal
parameters
LSTM
neuron:
50,
dropout
rate:
0.3,
number
layers:
2,
activation
function:
relu,
epochs:
500,
batch
size:
64,
optimizer:
Adam,
learning
0.01.
Evaluation
performed
using
Root
Mean
Squared
Error
(RMSE)
method
measure
accuracy
both
models
sector.
results
showed
that
outperformed
model,
lower
RMSE
(SARIMA:
4182.133
LSTM:
1939.02).
research
provides
valuable
insights
decision-makers
planning
strategies
future.
With
this
comparison,
it
expected
provide
better
guidance
selecting
suitable
characteristics
data.
Advances in finance, accounting, and economics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 321 - 356
Published: Jan. 22, 2025
Accurately
forecasting
price
swings
is
nowadays
essential
to
investors
looking
maximize
their
portfolios
as
the
cryptocurrency
markets
continue
develop
and
fluctuate
rapidly.
The
intricate,
non-linear
patterns
in
these
are
sometimes
difficult
for
traditional
financial
models
depict.
In
response,
this
paper
presents
two
machine
learning
techniques
predicting
bitcoin
prices:
Extreme
Gradient
Boosting
Long
Short-Term
Memory.
study
first
evaluates
how
well
forecast
Bitcoin
prices,
assessing
accuracy
with
measures
like
Mean
Absolute
Error
Root
Squared
Error.
Four
significant
cryptocurrencies
then
predicted
by
LSTM.
order
allocate
assets
a
way
that
optimizes
returns
while
reducing
risk,
forecasted
prices
incorporated
into
portfolio
optimization
algorithms
utilizing
Monte
Carlo
simulation
efficient
frontier.
results
of
show
approaches
may
be
used
improve
investing
strategies
through
optimal
allocation,
addition
projecting
values.
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(3), P. 1554 - 1554
Published: Feb. 4, 2025
Bitcoin,
the
pioneering
cryptocurrency,
is
renowned
for
its
extreme
volatility
and
speculative
nature,
making
accurate
price
prediction
a
persistent
challenge
investors.
While
recent
studies
have
employed
multivariate
models
to
integrate
historical
data
with
social
media
sentiment
analysis,
this
study
focuses
on
improving
an
existing
univariate
approach
By
incorporating
tweet
volume
into
framework,
we
systematically
evaluated
benefits
of
integration.
Among
five
LSTM-based
developed
study,
Multi-LSTM-Sentiment
model
achieved
best
performance,
lowest
mean
absolute
error
(MAE)
0.00196
root-mean-square
(RMSE)
0.00304.
These
results
underscore
significance
including
in
predictive
modelling
demonstrate
potential
enhance
decision-making
highly
dynamic
cryptocurrency
market.
