Highlights in Business Economics and Management,
Год журнала:
2024,
Номер
45, С. 857 - 866
Опубликована: Дек. 28, 2024
Nowadays,
financial
markets
are
becoming
more
and
complex,
new
portfolios
need
to
be
built
cope
with
them.
This
paper
aims
build
a
Markowitz
model
for
portfolio
research
based
on
calibrations
nine
different
industries.
Firstly,
the
weights
minimum
variance
combinations
calculated
by
using
valid
information
such
as
mean,
standard
deviation,
variance,
covariance.
Second,
this
maximize
return
of
portfolio,
diversify
investment
risk
selected
finally
determine
optimal
portfolio.
The
can
adjusted
reduce
or
increase
adjusting
percentage
Bitcoin.
further
explores
Bitcoin
variable.
derives
volatility
least
risky
11.04%
-0.46%,
respectively,
when
is
calibrated
without
Bitcoin,
its
Sharpe
14.61%
7.11%,
respectively.
When
contains
risk-minimal
9.45%
0.6%,
Sharpe-optimal
16.31%
37.35%,
Ultimately,
it
concluded
that
has
some
risk-reducing
return-enhancing
effects.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 28, 2024
This
study
examines
the
formidable
and
complex
challenge
of
insider
threats
to
organizational
security,
addressing
risks
such
as
ransomware
incidents,
data
breaches,
extortion
attempts.
The
research
involves
six
experiments
utilizing
email,
HTTP,
file
content
data.
To
combat
threats,
emerging
Natural
Language
Processing
techniques
are
employed
in
conjunction
with
powerful
Machine
Learning
classifiers,
specifically
XGBoost
AdaBoost.
focus
is
on
recognizing
sentiment
context
malicious
actions,
which
considered
less
prone
change
compared
commonly
tracked
metrics
like
location
time
access.
enhance
detection,
a
term
frequency-inverse
document
frequency-based
approach
introduced,
providing
more
robust,
adaptable,
maintainable
method.
Moreover,
acknowledges
significant
impact
hyperparameter
selection
classifier
performance
employs
various
contemporary
optimizers,
including
modified
version
red
fox
optimization
algorithm.
proposed
undergoes
testing
three
simulated
scenarios
using
public
dataset,
showcasing
commendable
outcomes.
Soil Use and Management,
Год журнала:
2025,
Номер
41(1)
Опубликована: Янв. 1, 2025
Abstract
Slope
stability
is
a
critical
factor
in
ensuring
the
safety
and
longevity
of
infrastructure,
especially
areas
prone
to
landslides
soil
erosion.
Traditional
methods
slope
assessment,
while
widely
used,
often
struggle
provide
accurate
results
when
applied
Technosols—soils
modified
by
human
activities
composed
waste
materials.
This
study
proposes
novel
approach
that
combines
artificial
intelligence
techniques
improve
precision
predictions
these
complex
types.
The
method
utilizes
model
based
on
neural
networks,
trained
large
dataset
factors.
Unlike
conventional
techniques,
proposed
integrates
multiple
environmental
material
properties
more
assessment
compared
other
models.
model's
performance
demonstrated
R
2
values
.999975
for
test
datasets,
which
significantly
better
than
similar
work
statistical
analysis.
Moreover,
incorporating
Shapley
Additive
Explanations
(SHAP),
we
clear
understanding
impact
various
parameters
stability.
findings
suggest
machine
learning‐based
offers
reliable
tool
evaluation
Technosols,
making
it
valuable
addition
field.
