International Journal on Recent and Innovation Trends in Computing and Communication,
Journal Year:
2023,
Volume and Issue:
11(10), P. 172 - 184
Published: Nov. 2, 2023
With
the
proliferation
of
digital
content,
efficient
image
matching
in
natural
databases
has
become
paramount.
Traditional
techniques,
while
effective
to
a
certain
extent,
face
challenges
dealing
with
high
variability
inherent
images.
This
research
delves
into
application
deep
learning
models,
particularly
Convolutional
Neural
Networks
(CNNs),
Siamese
Networks,
and
Triplet
address
these
challenges.
We
introduce
various
techniques
enhance
efficiency,
such
as
data
augmentation,
transfer
learning,
dimensionality
reduction,
sampling,
amalgamation
traditional
computer
vision
strategies
learning.
Our
experimental
results,
garnered
from
specific
dataset,
demonstrate
significant
improvements
quantified
by
metrics
like
precision,
recall,
F1-Score,
time.
The
findings
underscore
potential
transformative
tool
for
database
matching,
setting
stage
further
optimization
this
domain.
Advances in healthcare information systems and administration book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 217 - 242
Published: Jan. 17, 2025
The
incorporation
of
the
Internet
Medical
Things
and
Generative
AI
to
this
process
shall
transform
patient
care
by
offering
continuous
tracking,
analysis
individualized
progression
control.
This
chapter
is
dedicated
synergistic
fusion
IoT
in
Technology
(IoMT)
Artificial
Intelligence
provides
a
brief
summary
what
it
is,
how
functions,
can
be
expected
future
field
health
care.
When
combined
with
data
acquiring
capacity
IoMT
analytical
potential
AI,
hospitals
other
medical
facilities
have
bring
diagnosis
treatment
higher
level.
Some
real-life
usage
examples
uses
SDN
are
shown
through
different
use
cases,
including
chronic
disease
management,
elderly
care,
virtual
assistance,
prognostic
management
maintenance
healthcare
facilities'
equipment
tools.
The
increasing
role
that
cloud
computing
plays
in
storing
e-health
data
has
highlighted
the
necessity
for
strong
security
measures.
purpose
of
this
study
is
to
shed
light
on
difficulties
protecting
private
health
kept
servers.
As
first
priority,
we
developed
a
unique
multi-layer
architecture
cloud-based
protect
electronic
data.
We
provide
complete
analysis
current
protocols,
perform
vulnerability
assessment,
and
create
more
robust
multi-layered
as
part
our
methodology.
concept
includes
sophisticated
encryption
methods,
strict
access
rules,
instruments
ongoing
threat
detection.
In
order
assess
effectiveness
framework,
ran
extensive
simulations
with
an
emphasis
integrity,
control,
confidentiality.
findings
show
considerable
improvement
over
conventional,
one-layer
techniques.
proposed
framework
guarantees
regulatory
compliance
addition
providing
enhanced
against
illegal
breaches.
have
found
safeguarding
records—which
are
critical
both
patients
healthcare
providers—requires
approach
built
cloud.
may
lead
improved
future.
The
creation
and
assessment
of
a
scalable
trustworthy
cloud-based
UV
monitoring
system
designed
for
public
health
applications
are
the
main
topics
this
research
article.
Monitoring
radiation
is
important
because
it
plays
crucial
part
in
understanding
reducing
variety
hazards,
such
as
vitamin
D
insufficiency
skin
cancer.
Traditional
techniques
frequently
have
limitations
due
to
their
confined
character
lack
access
real-time
data.
We
suggest
that
integrates
cutting-edge
sensors
with
cloud
computing
infrastructure
overcome
these
constraints.
process
entails
placing
various
geographical
places
order
collect
data
real
time.
Due
seamless
integration
platform,
continuous
transmission,
archival,
analysis
possible.
raw
sensor
readings
used
our
study
transformed
into
meaningful
index
values
using
processing
techniques,
giving
us
valuable
information
initiatives.
This
introduces
tracking
ultraviolet
(UV)
based
on
Internet
Things
(IoT).
goal
help
avoid
ailments
can
be
brought
by
prolonged
exposure
sun
light.
In
reaction
variations
level
over
time,
accomplishes
sending
warning
messages
suggesting
preventive
steps.
avoiding
number
illnesses,
including
calcium
shortage,
cardiovascular
disease,
Smartphones
have
completely
altered
the
mobile
communication
scene.
Wi-Fi,
global
positioning
system
navigation,
high-resolution
cameras,
and
touchscreens
with
high-speed
internet
access
are
just
some
of
cutting-edge
capabilities
that
these
devices
offer,
allowing
users
to
stay
in
constant
contact
present.
Since
many
features
embedded
deeply
operating
system,
they
typically
inaccessible
average
user.
However,
Google
released
Android,
a
revolutionary
system.
Because
its
open
architecture,
this
platform
encourages
third-party
development
debugging
environment
may
change
create
their
own
unique
apps.
In
research
project,
we
examine
an
Emergency
Based
Remote
Collateral
Tracking
System
app
on
Android
from
Google.
There
three
main
forms
emergencies:
those
involving
heart,
personal
safety,
roads.
Users
who
operate
motor
vehicles
primary
focus
app.
Our
program
can
keep
tabs
driver's
pulse
by
connecting
heart
rate
monitor.
application
has
backup
function
case
anomalies.
First,
it
sends
SMS
messages
containing
user's
location
data
after
using
GPS
do
so.
E-Health
Record
Security
Research
on
a
Cloud-Based
Multi-Layer
Framework
reaches
its
climax
in
string
of
noteworthy
discoveries,
demonstrating
how
the
framework
may
transform
cloud
health
data
security.
The
framework's
multi-tiered
design
proved
to
be
an
effective
barrier
against
wide
range
cyber
threats,
protecting
privacy
and
security
patients'
medical
records.
An
important
factor
varied
healthcare
industry
is
fact
that
it
can
easily
scaled
adjusted
meet
needs
providers
sizes
capabilities.
Nevertheless,
there
are
obstacles
need
addressed,
according
report.
These
include
difficulty
implementation
ongoing
for
upgrades
modifications
address
changing
threats
technical
advances.
Healthcare
firms
must
continuously
implement
educational
programs
due
reliance
user
compliance
training.
With
eye
toward
future,
this
structure
lays
groundwork
more
sophisticated
studies
subject.
Potential
research
directions
improving
cross-platform
compatibility,
optimizing
resource
utilization
reduce
performance
implications,
integrating
AI
ML
automated
threat
response
predictive
analytics.
Oral
health
is
a
crucial
aspect
of
general
health,
and
the
presence
fluoride
in
drinking
water
has
been
consistently
linked
to
its
improvement.
This
work
utilizes
deep
learning
machine
approaches
develop
prediction
models
that
can
estimate
oral
consequences
based
on
concentrations.
Our
analysis
comprehensive
dataset
includes
levels
indicators
from
several
geographic
locations.
The
covers
broad
range
demographic
environmental
factors.
study
involves
thorough
data
pretreatment
procedure,
which
activities
such
as
cleaning,
standardization,
feature
engineering.
All
these
processes
contribute
improving
making
input
variables
more
relevant.
approach
used
encompasses
algorithms,
including
neural
networks,
decision
trees,
ensemble
approaches,
are
create
models.
Thoroughly
adjusting
hyperparameters
using
cross-validation
methods
maximize
effectiveness
model.
