Tehnički glasnik,
Год журнала:
2024,
Номер
18(3), С. 342 - 353
Опубликована: Июль 15, 2024
For
preventing
the
spread
of
COVID-19,
health
authorities
urgently
turned
their
attention
to
urban
public
transportation.
It
is
believed
that
virus
transmission
more
likely
occur
in
transportation
due
increased
exposure
infected
individuals
closed
and
crowded
spaces
transport.
This
study
aimed
model
effective
factors
use
systems
during
a
pandemic
based
on
technology
acceptance
(TAM)
theory
planned
behaviour
(TPB).
The
methodology
used
was
structural
equation
modeling,
with
358
Iranian
passengers
Tehran
participating
data
collected
through
questionnaire.
underwent
analysis
by
means
partial
least
squares
method
assistance
SMARTPLS
software.
results
indicate
passenger
satisfaction
affected
positively
significantly
expectation
service
quality.
Behavioral
control,
subjective
norm,
attitude,
perceived
usefulness
(PU),
ease
(PEU)
each
contribute
formation
intention.
Service
quality,
PU,
PEU
affect
attitude.
Finally,
expectation,
intention,
system.
Therefore,
it
can
be
inferred
amalgamating
TPB
TAM
serve
as
robust
indicator
passengers'
inclination
towards
using
situations,
well
actual
usage
it.
Journal of Engineering and Applied Science,
Год журнала:
2024,
Номер
71(1)
Опубликована: Янв. 3, 2024
Abstract
Rapid
technological
advances
have
made
daily
life
easier
and
more
convenient
in
recent
years.
As
an
emerging
technology,
the
Internet
of
Things
(IoT)
facilitates
interactions
between
physical
devices.
With
advent
sensors
features
on
everyday
items,
they
become
intelligent
entities
able
to
perform
multiple
functions
as
services.
IoT
enables
routine
activities
intelligent,
deeper
communication,
processes
efficient.
In
dynamic
landscape
IoT,
effective
service
discovery
is
key
optimizing
user
experiences.
A
Quality
Service
(QoS)-aware
technique
proposed
this
paper
address
challenge.
Through
whale
optimization
genetic
algorithms,
our
method
aims
streamline
decision-making
selection.
The
bio-inspired
techniques
employed
approach
facilitate
services
efficiently
than
traditional
methods.
Our
results
demonstrate
superior
performance
regarding
reduced
data
access
time,
optimized
energy
utilization,
cost-effectiveness
through
comprehensive
simulations.
Electronics,
Год журнала:
2023,
Номер
12(18), С. 3985 - 3985
Опубликована: Сен. 21, 2023
Elevated
levels
of
fine
particulate
matter
(PM2.5)
in
the
atmosphere
present
substantial
risks
to
human
health
and
welfare.
The
accurate
assessment
PM2.5
concentrations
plays
a
pivotal
role
facilitating
prompt
responses
by
pertinent
regulatory
bodies
mitigate
air
pollution.
Additionally,
it
furnishes
indispensable
information
for
epidemiological
studies
concentrating
on
exposure.
In
recent
years,
predictive
models
based
deep
learning
(DL)
have
offered
promise
improving
accuracy
efficiency
quality
forecasts
when
compared
other
approaches.
Long
short-term
memory
(LSTM)
networks
proven
be
effective
time
series
forecasting
tasks,
including
pollution
prediction.
However,
optimizing
LSTM
enhanced
remains
an
ongoing
research
area.
this
paper,
we
propose
novel
approach
that
integrates
binary
chimp
optimization
algorithm
(BChOA)
with
optimize
prediction
models.
proposed
BChOA,
inspired
social
behavior
chimpanzees,
provides
powerful
technique
fine-tune
architecture
its
parameters.
evaluation
results
is
performed
using
cross-validation
methods
such
as
coefficient
determination
(R2),
accuracy,
root
mean
square
error
(RMSE),
receiver
operating
characteristic
(ROC)
curve.
performance
BChOA-LSTM
model
against
eight
DL
architectures.
Experimental
evaluations
real-world
data
demonstrate
superior
BChOA-based
traditional
algorithms.
achieved
highest
96.41%
validation
datasets,
making
most
successful
approach.
show
performs
better
than
architectures
terms
R2
convergence
curve,
RMSE,
accuracy.
Journal of Intelligent & Fuzzy Systems,
Год журнала:
2024,
Номер
46(2), С. 5021 - 5032
Опубликована: Янв. 9, 2024
Intelligent
Transportation
Systems
(ITS)
have
experienced
significant
growth
over
the
past
decade
thanks
to
advances
in
control,
communication,
and
information
technology
applied
vehicles,
roads,
traffic
control
systems.
Vehicle
type
classification
plays
a
vital
role
implementing
ITS
because
of
its
ability
collect
useful
information,
enable
future
development
transport
infrastructures,
increase
human
comfort.
As
branch
machine
learning,
deep
learning
represents
frontier
for
artificial
intelligence,
which
seeks
be
closer
primary
goal.
Deep
is
powerful
tool
classifying
vehicle
types
it
can
capture
complex
data
characteristics
learn
from
large
amounts
data.
This
means
that
used
accurately
classify
generate
valuable
insights
improve
management.
Researchers
successfully
adopted
these
algorithms
as
solution
propose
optimal
vehicle-type
strategies.
paper
highlights
solving
problem,
reviewing
state-of-the-art
approaches
this
field.
International Journal of Advanced Computer Science and Applications,
Год журнала:
2023,
Номер
14(7)
Опубликована: Янв. 1, 2023
In
today's
rapidly
evolving
digital
landscape,
ensuring
the
security
of
networks
and
systems
has
become
more
crucial
than
ever
before.
The
ever-present
threat
hackers
intruders
attempting
to
disrupt
compromise
online
services
highlights
pressing
need
for
robust
measures.
With
continuous
advancement
systems,
new
dangers
arise,
but
so
do
innovative
solutions.
One
such
solution
is
implementation
Network
Intrusion
Detection
Systems
(NIDSs),
which
play
a
pivotal
role
in
identifying
potential
threats
computer
by
categorizing
network
traffic.
However,
effectiveness
an
intrusion
detection
system
lies
its
ability
prepare
data
identify
critical
attributes
necessary
constructing
classifiers.
light
this,
this
paper
proposes,
DeepShield,
cutting-edge
NIDS
that
harnesses
power
deep
learning
leverages
hybrid
feature
selection
approach
optimal
performance.
DeepShield
consists
three
essential
steps:
selection,
rule
assessment,
detection.
By
combining
strengths
machine
technologies,
developed
excels
detecting
intrusions.
process
begins
capturing
packets
from
network,
are
then
carefully
preprocessed
reduce
their
size
while
retaining
information.
These
refined
fed
into
algorithm,
employs
characteristics
learn
test
patterns.
Simulation
results
demonstrate
superiority
over
previous
approaches.
achieves
exceptional
level
accuracy
malicious
attacks,
as
evidenced
outstanding
performance
on
widely
recognized
CSE-CIC-DS2018
dataset.
