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.
In
order
to
fight
the
pandemic
situations
like
COVID-19,
researchers
are
working
towards
find
an
early
vaccination
and
developing
a
multidisciplinary
strategy
that
explores
most
sophisticated
Internet
of
Things
(IoT)
technologies.
By
utilizing
IoT-based
industrial
manufacturing
breathing
units,
masks,
other
medical
equipment,
patients
might
either
be
observed
in
hospitals
or
separated
at
home
secured
manner
by
utilising
these
items;
alternatively,
new
methods
passive
ventilation
could
devised.
It
is
non-contact,
alcohol-based
hand
sanitizer
dispenser
used
number
environments
including
hospitals,
work
places,
companies,
schools,
educational
institutions.
An
automated
distributing
machine
system
distributes
automatically.
When
compared
liquid
soap
solid
soap,
alcohol
primarily
solvent
also
serves
as
highly
effective
disinfectant.
Additionally,
since
volatile
vaporises
immediately
after
application
hands,
it
does
not
need
water
washed
away.
has
additionally
been
demonstrated
concentration
hands
more
than
70%
may
able
destroy
Corona
virus.
infrared
sensor
detect
when
placed
close
proximity
it.
This
microcontroller
estimate
distance
between
sensor,
which
ultimately
results
pump
being
activated
dispense
sanitizer.
keep
track
two
objects,
ultrasonic
sensors
used;
becoming
too
near,
buzzer
will
ring
warn
user.
The
internal
body
temperature
measured
using
patient's
health
evaluated
with
assistance
well
pulse
sensor.
Finally,
few
data
points
sent
cloud
evaluation
complete.
A
proposed
system
that
utilizes
machine
learning
and
natural
language
processing
aims
to
facilitate
communication
among
individuals
who
speak
different
languages.
Specifically,
doctors
can
use
a
prototype
of
this
access
information
from
database
about
the
health
their
patients.
The
paper
suggests
Natural
Language
Processing
(NLP)
be
used
minimize
delays
during
online
consultations
between
NLP
plays
crucial
role
in
smart
healthcare
by
textual
data
enabling
human-machine
communication.
Through
algorithms,
teach
computers
comprehend
human
language,
including
speech
text,
extract
useful
unstructured
data.
This
overcome
barriers,
allowing
patients
communicate
native
while
respond
English
or
other
languages,
better
user
experience
for
everyone.
In
most
cases,
the
mental
impairment
caused
by
delirium
may
be
treated
and
eventually
reversed.
Lack
of
concentration,
disorientation,
incoherent
thought,
fluctuating
degrees
awareness
(consciousness)
are
all
symptoms.
Delirium,
an
acute
neuropsychiatric
disorder
characterised
inattention
generalised
cognitive
impairment,
is
common,
hazardous,
generally
linked
with
poor
results.
Patients
at
increased
risk
for
adverse
outcomes
throughout
their
time
in
critical
care
unit.
It
requires
medical
competence
to
diagnose
delirium.
Those
developing
should
identified
as
soon
possible.
Once
a
diagnosis
has
been
made,
treatment
process
lengthy
include
several
groups
working
together.
This
paper's
goal
show
how
model
built
using
Electronic
Health
Record
data
employing
Machine
Learning
technique.
Cancers,
Journal Year:
2023,
Volume and Issue:
15(12), P. 3139 - 3139
Published: June 10, 2023
Breast
cancer
is
the
second-leading
cause
of
mortality
among
women
around
world.
Ultrasound
(US)
one
noninvasive
imaging
modalities
used
to
diagnose
breast
lesions
and
monitor
prognosis
patients.
It
has
highest
sensitivity
for
diagnosing
masses,
but
it
shows
increased
false
negativity
due
its
high
operator
dependency.
Underserved
areas
do
not
have
sufficient
US
expertise
lesions,
resulting
in
delayed
management
lesions.
Deep
learning
neural
networks
may
potential
facilitate
early
decision-making
by
physicians
rapidly
yet
accurately
monitoring
their
prognosis.
This
article
reviews
recent
research
trends
on
mass
ultrasound,
including
beyond
diagnosis.
We
discussed
original
recently
conducted
analyze
which
modes
ultrasound
models
been
purposes,
where
they
show
best
performance.
Our
analysis
reveals
that
lesion
classification
showed
performance
compared
those
other
purposes.
also
found
fewer
studies
were
performed
than
limitations
future
directions
ongoing
ultrasound.
The
rapidly
expanding
discipline
of
data
analysis
has
an
important
role
to
play
in
the
medical
industry.
Using
this
knowledge,
we
can
uncover
previously
concealed
details
that
might
aid
early
illness
prediction.
Predicting
cardiovascular
is
one
most
pressing
issues
our
day.
community
views
heart
disease
prediction
as
a
challenging
endeavour.
Machine
learning
for
field's
massive
training
and
testing
needs.
Creating
assessing
system
crucial
detection
treatment
condition.
This
research
uses
variety
machine
methods
predict
possibility
diagnose
patient
with
or
not.
These
include
Decision
Tree,
K
-
Nearest
Neighbour
classifier,
Support
Vector
Machine.
Finally,
study
provides
cardiac
result,
trials
comparing
suggested
technique
others
have
shown
it
may
be
used
provide
forecast
patient.
The
modern
lifestyle's
busy
schedule
often
results
in
unhealthy
habits
that
lead
to
anxiety
and
depression.
To
deal
with
stress,
many
people
engage
harmful
behaviours
such
as
heavy
smoking,
drinking,
drug
usage.
Heart
disease,
cancer,
other
fatal
conditions
may
all
be
traced
back
these
bad
routines.
World
Health
Organization
(WHO)
reports
healthcare
spending
is
becoming
unsustainable
due
the
prevalence
of
cardiovascular
disease.
address
this
issue,
it
essential
have
a
fast,
accurate,
early
clinical
assessment
disease
severity.
This
work
proposes
an
effective
CVD
prediction
approach
using
deep
learning,
which
considers
cytokines
important
feature
for
prediction.
proposed
scheme
shown
provide
better
predictions,
supporting
decision-making
logistical
planning
systems.
2022 7th International Conference on Communication and Electronics Systems (ICCES),
Journal Year:
2023,
Volume and Issue:
unknown, P. 1417 - 1424
Published: June 1, 2023
Smart
Ambulance
and
Patient
Health
Monitoring
is
a
system
designed
to
enhance
the
quality
of
medical
care
during
patient
transport.
it
cutting-edge
technology
that
integrates
healthcare
with
transportation
It
aims
improve
efficiency
emergency
services.
This
work
an
effort
address
critical
issue
in
modern
delivery.
consists
three
major
sections.
First,
sensors
would
be
used
detect
patient's
vitals;
second,
data
sent
cloud
storage
service;
third,
discovered
made
available
for
remote
viewing
via
Java
GUI.
The
ambulance
equipped
real-time
communication
connects
database,
enabling
professionals
remotely
monitor
advise
on
vital
signs
(heart
rate,
respiration
temperature)
are
tracked
real
time
by
wireless
devices
health
surveillance
system.
information
transmitted
GUI
including
safety
parameters
like
Fire
sensor,
IR
GPS
tracking
Gas
make
informed
decisions
regarding
care,
ambulance's
ability
reach
hospital
safely.
outcomes
providing
timely
accurate
interventions
transport
may
reduce
between
diagnosis
treatment.
The
current
research
is
directed
toward
the
usage
of
Internet
Things
(IoT)
solutions
for
improvement
urban
environment
by
joining
traffic
optimization
and
pollution
management.
Through
utilization
data-driven
analysis
MATLAB
simulations,
evaluates
effects
implemented
IoT
technologies
on
management
in
areas
emission
reduction.
concludes
that
IoT-related
initiatives
can
make
a
great
difference
improvements
flow,
reducing
levels
principal
pollutants,
inspiring
move
towards
more
environmentally-friendly
modes
transport.
Moreover
paper
investigates
system's
resilient
nature
energy
efficiency
making
case
technological
paradigm
shift
will
transform
cities
planning
governance.
In
addition
to
these
challenges
like
privacy,
data
security,
access
equitable
also
examined.
results
survey
strongly
point
out
transformative
power
IOT
creation
which
are
pragmatic,
sustainable
liveable
moreover
showing
future
where
smart
indispensable
part
solving
complexities
environmental
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.
