The
demand
for
advanced
traffic
management
systems
in
smart
cities
has
grown
recent
years
response
to
rising
urban
populations
and
the
increasing
importance
of
environmental
preservation.
With
use
Internet
Things
(IoT),
it
investigates
how
Distributed
Ledger
Technology
(DLT)
may
be
implemented
decentralized
virtual
signal
systems.
To
overcome
drawbacks
conventional
light
regulation,
suggested
approach
decentralizes
decision-making
process
over
a
set
interconnected
devices.
Integration
DLT,
like
blockchain,
improves
system's
openness,
security,
robustness.
Connected
sensors
actuators
IoT
make
possible
gather
share
real-time
data,
techniques
that
is
both
responsive
aware
its
surroundings.
improve
flow,
design
helps
save
resources
lessens
overall
carbon
footprint.
This
article
describes
technological
architecture,
explores
potential
advantages,
explains
revolutionize
creating
are
sustainable
resilient.
A
system
for
smart
vehicle
emissions
monitoring
and
analysis
using
cloud
computing
neural
networks
is
presented
in
this
paper.
As
global
environmental
concerns
develop,
reducing
crucial.
Scalability
real-time
data
processing
are
issues
traditional
approaches.
Thus,
cloud-enabled
network
architecture
scalable
efficient
distributed
developed.
model
trained
on
various
emission
datasets,
allowing
precise
pattern
prediction
of
emissions.
The
scalability
the
guaranteed
by
resources,
which
can
accommodate
rising
amount
created
an
increasing
number
vehicles.
By
incorporating
cloud-based
seamlessly,
accomplished,
enabling
immediate
prompt
alerts.
suggested
tracking
platform
robust
to
intelligent
transportation
systems
revealing
emissions'
effect.
Future
city
sustainable
urban
developments
built
networks.
proposed
shows
how
may
solve
complicated
problems.
2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO),
Journal Year:
2024,
Volume and Issue:
unknown
Published: March 14, 2024
The
significant
morbidity
and
death
rates
associated
with
sepsis
indicate
that
it
is
still
an
important
health
care
concern.
Wearable
technologies,
cloud
computing,
logistic
regression
predictive
analytics
are
proposed
as
unique
techniques
to
identify
early
on
in
this
research.
sensors
continuously
monitor
physiological
parameters,
collecting
real-time
data
sending
the
for
analysis.
A
model
trained
using
prior
patient
can
analyze
incoming
predict
chance
of
development.
cloud's
scalability,
adaptability,
processing
communicate
necessity
quick
interventions.
method
shows
some
encouraging
accuracy
spotting
first
symptoms
alerting
doctors
just
time.
Combining
wearable
technology
computing
enhances
accessibility
crucial
data,
permitting
remote
monitoring
proactive
healthcare
management.
feasibility
suggested
approach
diagnosis
has
been
shown
via
both
simulated
real-world
studies.
This
system
could
enhance
outcomes
by
facilitating
interventions
individualized
recommendations.
cloud-based
solution's
scalability
flexibility
also
open
door
more
widespread
use
a
wide
range
medical
disorders.
The
maritime
sector
is
shifting
towards
predictive
maintenance
to
improve
marine
propulsion
system
dependability
and
efficiency.
This
research
introduces
neural
networks
IoT
sensor
fusion
for
health
monitoring.
Real-time
operational
data
collected
by
a
sophisticated
array
spanning
crucial
components.
Fusing
using
modern
algorithms
gives
comprehensive
overview
of
health.
suggested
technique
uses
maintenance.
A
deep
learning
model
analyses
sensor-fused
detect
flaws
or
performance
deterioration.
Training
the
network
on
past
from
various
operating
situations
allows
it
adapt
forecast
faults.
model's
capacity
learn
develop
improves
its
vessel
state
adaptation.
Neural
offer
early
defect
identification
dynamic
schedules.
Low
downtime,
expenses,
longevity
are
achieved
this
strategy.
Case
studies
simulations
indicate
that
can
predict
avoid
significant
failures,
making
suitable
use.
The
Internet
of
Things
(IoT)-powered
black
box
for
advanced
driver
behavior
analysis
and
vehicle
safety
is
a
revolutionary
strategy
improving
road
via
the
use
boxes
powered
by
Things.
This
project
captures
realtime
data
on
driving
behavior,
encompassing
characteristics
such
as
speed,
acceleration,
braking,
adherence
to
lanes
seamless
integration
IoT
technologies
into
vehicle's
box.
It
does
this
providing
in-depth
insights
drivers'
behaviors,
which
encourages
adopting
safe
practices
thanks
analytics
it
employs.
In
addition
its
monitoring
capabilities,
IoT-driven
enhances
including
collision
detection
technologies.
If
an
accident
occurs,
system
immediately
sends
alert,
communicating
severity
exact
GPS
locations
emergency
services,
speeds
up
process
responding
incident.
attempt
combines
cutting-edge
capabilities
with
develop
responsible
behaviors
and,
eventually,
safer
highway
environment
all
parties
involved.
invention
can
modify
norms
comprehensive
approach,
highlighting
potential
technology-driven
solutions
in
promoting
roadways.
Unmanned
Aerial
Vehicles
(UAVs)
gather
data
efficiently
for
power
line
inspection.
Anomaly
detection
is
essential
infrastructure
dependability
and
security.
It
proposes
a
Cloud-Enabled
Isolation
Forest
(CEIF)
method
UAV-based
improves
the
isolation
forest
algorithm's
efficiency
scalability
in
cloud
computing.
can
process
huge
UAV
inspection
datasets
by
dispersing
The
technique,
which
effectively
isolates
anomalies,
applied
to
fast
anomaly
identification.
describes
CEIF
system's
service
integration
distributed
computing
algorithm
optimization.
Real-world
show
it
accurately
detect
abnormalities
with
low
false-positive
rates.
scalable
robust
improving
allows
services
deploy
real-world
settings
implement
different
scales.
The
Internet
of
Things
(IoT)
and
horticulture
lighting
systems
may
improve
plant
growth
agricultural
operations.
variation
in
the
light
spectrum,
intensities,
durations
affect
physiological
systems.
proposed
system
can
dynamically
adjust
control
settings
using
real-time
sensor
data
by
seamlessly
integrating
IoT
capabilities.
These
sensors
carefully
track
health,
ambient
conditions,
energy
use.
This
dynamic
feedback
allows
operators
to
make
informed
choices
techniques
for
optimum
growth,
yields,
resource
Plant
efficiency
benefit
from
parameter
improvement.
connection
between
leads
sustainable
agriculture
that
maximizes
yields
efficiency.
Moving
static
adaptive
represents
a
paradigm
change
agriculture,
as
data-driven
decisions
enable
precision
farming.
Combining
IoT's
abilities
with
promises
unique
savings,
practices.
This
study
uses
intelligent
headlights
to
improve
car
safety
and
economy.
The
suggested
system
Raspberry
Pi
Convolutional
Neural
Networks
(CNNs)
dynamically
modify
beam
patterns
real-time
ambient
circumstances
context.
is
a
flexible
computer
platform,
CNNs
automatically
analyze
sensor
video
data
headlight
patterns.
A
trained
CNN
model
recognizes
impending
vehicles,
pedestrians,
road
conditions.
By
analyzing
this
information,
the
optimize
pattern
maximize
visibility
minimize
user
glare.
flexibility
improves
by
optimizing
lighting
when
required,
minimizing
driver
fatigue,
enhancing
response
times.
allows
for
cost-effective
easy
retrofitting
of
current
vehicles
with
headlights.
system's
scalability
adaptability
make
it
suitable
many
automotive
applications,
helping
construct
smart
linked
transportation
systems.
discovery
major
step
towards
adaptable
systems
that
vehicle
2022 International Conference on Inventive Computation Technologies (ICICT),
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 24, 2024
Public
restrooms
needs
to
be
frequently
cleaned
maintain
public
health
and
hygiene.
Traditional
bathroom
cleaning
techniques
may
not
solve
current
cleanliness
issues.
This
research
proposes
an
advanced
robotics,
Internet
of
Things
(IoT),
Recurrent
Neural
Networks
(RNNs)
solution
these
problems.
The
proposed
system
uses
autonomous
robots
with
IoT
sensors
cameras.
Robots
identify
maintenance
in
restrooms.
A
central
receives
data
from
on
indicators,
including
toilet
paper,
soap,
foot
movement.
(RNN)
processes
this
predict
prioritize
requirements.
RNN
monitors
conditions
real
time
reacts
changing
use
needs.
dynamic
technique
optimizes
resource
allocation
maintains
facility
cleanliness.
device
also
warns
personnel
when
certain
areas
need
quick
attention.
novel
makes
restroom
care
more
cost-effective,
responsive,
environmentally
friendly
by
combining
robots,
IoT,
RNNs.
study
advances
smart
management,
which
technology
improve
space
cleanliness,
user
experience,
usage.
In
this
ground-breaking
endeavor,
we
describe
an
automated
skin
cancer
screening
booth
that
can
transform
current
approaches
to
early
detection.
The
merging
of
Raspberry
Pi-controlled
technology
with
cutting-edge
Artificial
Neural
Network
(ANN)
models
forms
the
basis
our
groundbreaking
breakthrough.
uses
high-resolution
imaging
sensors
acquire
detailed
photographs
user's
skin,
which
are
then
analyzed
in
real-time
by
ANN.
ability
identify
potentially
malignant
anomalies
a
timely
and
precise
manner
is
made
possible
combination.
public
places,
user
contact
facilitated
use
intuitive
touchscreen
interface,
ensures
both
accessibility
convenience
use.
ANN
model,
trained
on
wide
variety
data,
very
good
at
differentiating
between
normal
diseases,
findings
being
instantly
shown
interface.
Beyond
its
technological
capabilities,
bears
prospect
broad
application,
will
turn
for
into
preventative
healthcare
tool
available
everyone.
This
all-encompassing
strategy
highlights
significance
detection
argues
solution
scalable
effective
crossroads
health.
