Advances in computational intelligence and robotics book series,
Год журнала:
2024,
Номер
unknown, С. 371 - 383
Опубликована: Апрель 1, 2024
The
'Umber'
mobile
app
stands
as
a
pioneering
technological
advancement,
reshaping
the
traditional
usage
of
umbrellas.
This
abstract
encapsulates
app's
diverse
objectives,
focusing
on
elevated
user
convenience,
innovation,
environmental
sustainability,
and
increased
accessibility.
Through
seamless
integration
technology,
revolutionizes
umbrella
experience,
introducing
on-demand
services,
reducing
forgetfulness,
promoting
sustainability.
Drawing
inspiration
from
successful
applications
like
GCOO,
Kakao
T,
Panda
Korea,
adopts
proven
model
to
address
umbrella-related
challenges.
chapter
delves
into
innovative
development
app,
paradigm-shifting
solution
at
intersection
global
mobility,
Internet
Things
(IoT)
integration,
cutting-edge
technologies.
core
architecture
involves
incorporation
Firebase
for
real-time
data
management,
ESP32
microcontrollers,
Reed
switches
interaction,
GPS
tracking
precise
location
awareness,
map
enhance
experience.
not
only
redefines
but
also
serves
testament
transformative
potential
thoughtful,
purpose-driven
technology
scale.
Future
research
endeavors
may
entail
augmenting
capabilities
through
exploration
advanced
sensor
technologies
machine
learning
algorithms.
could
lead
an
enhanced
experience
adaptability
weather
conditions,
potentially
manifested
in
compact
screen
box.
Users
conveniently
access
updates,
monitor
condition
box,
receive
information
about
housed
inside.
Additionally,
there
is
further
innovation
by
upgrading
each
include
features
such
built-in
flashlight,
providing
users
with
added
convenience
safety
nighttime
walks.
IEEE Internet of Things Journal,
Год журнала:
2024,
Номер
11(9), С. 16638 - 16646
Опубликована: Янв. 18, 2024
Cybersecurity
has
become
an
inevitable
concern
in
the
healthcare
industry
due
to
rapid
growth
of
Internet
Health
Things
(IoHT).
The
IoHT
is
revolutionizing
by
enabling
remote
access
hospital
equipment,
real-time
patient
monitoring,
and
urgent
alerts
patients
hospitals.
However,
convenience
these
systems
also
makes
them
vulnerable
cyberattacks,
with
hackers
seeking
disrupt
health
services
or
extort
money
through
ransomware
attacks.
Efficiently
detecting
multiple
threats
a
challenging
task
because
generates
large
temporal
data
system
log
information.
In
this
paper,
we
propose
time
series
classification
models
for
identification
potential
cyberattacks
networks.
First,
introduce
Neighborhood
Component
Analysis
(NCA)
modifications
regularization
parameter
select
vital
input
features.
With
selected
features,
two
LSTM-based
models:
Directed
Acyclic
Graph-based
Long
Short-Term
Memory
(DAG-LSTM)
Projected
Layer-based
(PL-LSTM)
cyberattacks.
We
evaluate
existing
(i.e.,
GRU,
LSTM,
Bi-LSTM)
proposed
DAG-LSTM
PL-LSTM)
using
real-world
data.
validate
applying
non-parametric
statistical
test,
Friedman
test.
Our
evaluation
results
show
that
achieves
highest
accuracy
99.89%
training
92.04%
average
testing
accuracy.
Advances in healthcare information systems and administration book series,
Год журнала:
2025,
Номер
unknown, С. 29 - 54
Опубликована: Фев. 4, 2025
Digital
Twin
(DT)
technology
has
been
employed
as
an
innovator
prototype
in
all
industries;
it
adds
to
the
creation
of
a
virtual
picture
physical
facilities,
processes
and
systems.
This
concept
which
evolved
from
engineering
areas
manufacturing
industries
extended
other
fields
operation
like
manufacturing,
health,
transport,
agricultural
urban
development
fields.
Real-time
data,
stream
acquisition,
modeling
simulation,
optimization,
decision-making
improvement,
analytics,
DTs
allow
businesses
achieve
better
insight.
Next
generation
DT
means
next
is
innovation
DT.
paper
provides
brief
description
on
what
generation,
elements
at
center
how
works.
Furthermore,
we
consider
problems,
prospects,
tendencies
application
DTs.
Hence,
presents
focus
enabled
machine
learning
architecture,
security
concerns
remedies.
To
justify
that
are
useful
for
designing
future
interconnected
data
driven
systems,
examples
articles
industry
presented.
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 83 - 104
Опубликована: Фев. 27, 2025
Increased
congestion,
inefficiency,
and
accidents
in
cities
are
major
issues
for
urban
traffic
systems.
However,
rapid
urbanization
increasing
numbers
of
cars
exacerbate
problems
that
have
created
an
environment
too
dynamic
sophisticated
traditional
solutions
like
static
signals
or
road
expansion.
The
chapter
discusses
the
use
machine
learning
robotics
with
graph
neural
networks
reinforcement
optimizing
flow.
Traffic
pose
intricate
relationships
GNNs
model
under
form
nodes
edges
representing
roads,
intersections,
vehicles.
RL
allows
continuous
real-time
interaction
through
which
autonomous
agents
learn
optimal
strategies;
thus,
better
decision-making
takes
place
conditions
system
can
proactively
adjust
signal
timings,
reroute
vehicles,
manage
congestion.
Integration
these
technologies
will
indeed
be
transformative
to
management;
hence,
more
effective,
flexible,
safest
transportation
systems
expected
future.
