Journal of Personalized Medicine,
Journal Year:
2022,
Volume and Issue:
12(9), P. 1454 - 1454
Published: Sept. 5, 2022
Diabetic
retinopathy
(DR)
is
a
drastic
disease.
DR
embarks
on
vision
impairment
when
it
left
undetected.
In
this
article,
learning-based
techniques
are
presented
for
the
segmentation
and
classification
of
lesions.
The
pre-trained
Xception
model
utilized
deep
feature
extraction
in
phase.
extracted
features
fed
to
Deeplabv3
semantic
segmentation.
For
training
model,
an
experiment
performed
selection
optimal
hyperparameters
that
provided
effective
results
testing
multi-classification
developed
using
fully
connected
(FC)
MatMul
layer
efficient-net-b0
pool-10
squeeze-net.
from
both
models
fused
serially,
having
dimension
N
×
2020,
amidst
best
1032
chosen
by
applying
marine
predictor
algorithm
(MPA).
lesions
into
grades
0,
1,
2,
3
neural
network
KNN
classifiers.
proposed
method
performance
validated
open
access
datasets
such
as
DIARETDB1,
e-ophtha-EX,
IDRiD,
Messidor.
obtained
better
compared
those
latest
published
works.
Journal of Healthcare Engineering,
Journal Year:
2023,
Volume and Issue:
2023(1)
Published: Jan. 1, 2023
Diabetic
retinopathy
(DR)
is
a
common
eye
retinal
disease
that
widely
spread
all
over
the
world.
It
leads
to
complete
loss
of
vision
based
on
level
severity.
damages
both
blood
vessels
and
eye’s
microscopic
interior
layers.
To
avoid
such
issues,
early
detection
DR
essential
in
association
with
routine
screening
methods
discover
mild
causes
manual
initiation.
But
these
diagnostic
procedures
are
extremely
difficult
expensive.
The
unique
contributions
study
include
following:
first,
providing
detailed
background
traditional
techniques.
Second,
various
imaging
techniques
deep
learning
applications
presented.
Third,
different
use
cases
real‐life
scenarios
explored
relevant
wherein
have
been
implemented.
finally
highlights
potential
research
opportunities
for
researchers
explore
deliver
effective
performance
results
diabetic
detection.
2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON),
Journal Year:
2023,
Volume and Issue:
unknown, P. 1551 - 1556
Published: Dec. 1, 2023
Industrial
data
has
increased
significantly
in
the
emerging
data-driven
age,
and
it
often
contains
abnormalities
that
could
point
to
crucial
system
faults
or
inefficiencies.
The
complexity
high
dimensionality
of
provide
special
hurdles
for
anomaly
identification
such
large-scale
settings.
In
this
study,
a
robust
deep
learning
framework
detection
is
presented,
one
can
function
with
large
complex
datasets
are
common
industrial
applications.
To
capture
temporal
spatial
relationships
present
sensor
data,
makes
use
sophisticated
neural
network
designs,
as
convolutional
networks
(CNNs)
recurrent
(RNNs).
suggested
model
learns
underlying
structure
using
unsupervised
learning,
which
allows
recognize
variations
may
indicate
possible
abnormalities.
An
extensive
dataset
used
evaluate
system's
effectiveness,
results
reveal
performs
better
than
conventional
machine
techniques
terms
both
computing
efficiency
accuracy.
flexibility
scalability
concept
reinforced
by
its
implementation
across
many
sectors,
further
demonstrates
adaptability.
study
not
only
advances
theoretical
understanding
mechanisms
but
also
provides
industry
practitioners
useful
tool
ensure
safety
dependability
operations
face
increasing
complexity.
Innovation and Emerging Technologies,
Journal Year:
2024,
Volume and Issue:
11
Published: Jan. 1, 2024
The
inhalation
of
airborne
particles
can
endanger
the
health
any
human
being.
Natural
fiber
and
natural
reinforced
with
matrix
material
are
employed
in
this
work
to
create
an
indoor
air
purifier.
Various
composite
combinations
used
purify
interior
environment
by
eliminating
particulate
matter
various
sizes
volatile
organic
chemicals.
An
purifier
is
created
using
four
distinct
fibers,
including
hemp,
jute,
silk
cocoon,
coir
as
well
neem
aloe
vera
gel
filler
materials.
quality-monitoring
instrument
validate
performance
designed
fiber/natural
plant-based
material-equipped
Particulate
compounds
detected
at
time
intervals.
efficacy
afterward
determined
lungs
ages
utilizing
impact
simulation
studies.
current
product
utilized
effectively
particulates
