FindMySpot: AI & AR Based Parking System
G. Padmapriya,
Alok Prasad,
Vrutika Panikar
и другие.
SSRN Electronic Journal,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
A
new
initiative
has
been
endorsed
to
develop
a
user-friendly
smart
parking
system
that
operates
with
minimal
human
intervention.
The
proposed
leverages
computer
vision
and
AR/VR
technology
create
seamless
experience.
Upon
arrival,
the
scans
vehicle's
license
plate
verifies
user's
details
using
combination
of
advanced
techniques.
suitable
spot
is
then
located
based
on
real-time
availability,
user
guided
slot
through
an
immersive
navigation
system.
also
integrates
robust
security
features,
employing
sensors
cameras
for
comprehensive
surveillance,
including
automated
recognition
emergency
alerting
mechanisms.
This
dual-layer
ensures
vehicle
safety
while
minimizing
Additionally,
platform
offers
mobile
application,
featuring
payment
processing,
customizable
interfaces,
community
page
enhanced
interaction.
Our
comparative
analysis
highlights
system's
superiority
over
traditional
methods,
which
often
rely
static
maps
manual
searching,
leading
inefficiencies
dissatisfaction.
On
contrary,
our
recommended
solution
provides
experience
easy,
effective
safe
especially
considering
difficulties
presented
by
cities,
most
notably
in
places
like
India
where
this
not
common.
information
provided
via
application
easy-to-use
interface
handling
specifics,
involvement.
Furthermore,
it
internet-based
storage
acts
as
central
place
data
management
verification
users.
Proposed
AI-driven
systems
have
found
satisfactorily
meet
demands
showing
functioned
optimally
high
levels
precision
time
plus
features
working
perfectly
even
under
conditions.
Язык: Английский
Challenges in Pursuing AI Transparency
The American Journal of Bioethics,
Год журнала:
2025,
Номер
25(3), С. 4 - 6
Опубликована: Фев. 24, 2025
Язык: Английский
Integrating Machine Learning and AI into IoT-Enabled Smart Parking
Опубликована: Янв. 1, 2025
Язык: Английский
Machine Learning Models and Mathematical Approaches for Predictive IoT Smart Parking
Sensors,
Год журнала:
2025,
Номер
25(7), С. 2065 - 2065
Опубликована: Март 26, 2025
This
paper
aims
to
create
an
innovative
approach
improving
IoT-based
smart
parking
systems
by
integrating
machine
learning
(ML)
and
Artificial
Intelligence
(AI)
with
mathematical
approaches
in
order
increase
the
accuracy
of
availability
predictions.
Three
regression-based
ML
models,
random
forest,
gradient
boosting,
LightGBM,
were
developed
their
predictive
capability
was
compared
using
data
collected
from
three
locations
Skopje,
North
Macedonia
2019
2021.
The
main
novelty
this
study
is
based
on
use
autoregressive
modeling
strategies
lagged
features
Z-score
normalization
improve
time
series
forecasts.
Bayesian
optimization
chosen
for
its
ability
efficiently
explore
hyperparameter
space
while
minimizing
RMSE.
able
capture
temporal
dependencies
more
effectively
than
other
resulting
lower
RMSE
values.
LightGBM
model
produced
R2
0.9742
0.1580,
making
it
best
prediction.
Furthermore,
system
architecture
also
deployed
which
included
real-time
collection
sensors
placed
at
entry
exit
lots
individual
slots.
integration
ML,
AI,
IoT
technologies
improves
efficiency
management
system,
reduces
traffic
congestion
and,
most
importantly,
offers
a
scalable
development
urban
mobility
solutions.
Язык: Английский
Bluetooth-Based Dynamic Nexus Mesh Communication Network for High-Density Urban Interaction Spaces
Sensors,
Год журнала:
2025,
Номер
25(8), С. 2495 - 2495
Опубликована: Апрель 15, 2025
Traditional
centralized
network
structures
exhibit
clear
scalability
and
communication
efficiency
bottlenecks.
This
paper
proposes
a
solution
based
on
bidirectional
unweighted
heterogeneous
graph,
Dynamic
Nexus
Mesh
Communication,
designed
to
improve
optimize
user
experience,
particularly
for
high-density
urban
interaction
spaces.
DNMC
reduces
the
reliance
central
super
nodes
in
traditional
networks
by
redistributing
centrality
while
increasing
betweenness
of
broader
set
nodes.
Additionally,
introduces
multiple
node
types,
improving
both
robustness
network.
Through
series
simulation
experiments,
we
compared
performance
with
that
networks.
The
results
show
achieves
an
average
delay
1.5824
s,
representing
13.69%
improvement
over
architectures.
These
findings
demonstrate
significantly
outperforms
terms
efficiency,
at
larger
scales,
where
exhibits
enhanced
stability
scalability.
Язык: Английский
Enhancing Smart Parking Management through Machine Learning and AI Integration in IoT Environments
IntechOpen eBooks,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 23, 2024
The
integration
of
Internet
Things
(IoT)
technology
has
profoundly
transformed
urban
life,
particularly
in
the
realm
parking
management.
Smart
systems
harness
capabilities
IoT
to
optimize
space
utilization,
alleviate
congestion,
and
elevate
user
experience.
This
chapter
delves
into
intricate
process
data
collection
within
IoT-enabled
smart
environments,
with
a
specific
emphasis
on
seamless
machine
learning
artificial
intelligence
(AI)
techniques.
By
conducting
comprehensive
analysis
various
sources,
algorithms,
AI
technologies,
this
elucidates
how
leverage
intelligent
enhance
operational
efficiency
effectiveness.
Through
convergence
IoT,
learning,
AI,
are
poised
revolutionize
mobility
drive
sustainable
development.
Язык: Английский