Multimedia Tools and Applications,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 21, 2024
Abstract
Telemedicine
has
a
critical
role
in
healthcare
by
supporting
the
information
exchange
between
patients
and
physicians
as
well
for
consultation.
Such
technology
urgent
requirements
storage
reduction
efficient
use
of
transmission
channel
bandwidth.
can
be
achieved
efficiently
via
developing
accurate
medical
image
compression
techniques.
For
diagnosis,
lossless
methods
are
recommended.
However,
tradeoff
ratio
(CR)
preservation
quality
is
still
challenging.
On
other
hand,
advantages
convolutional
neural
networks
inspired
this
work
to
design
novel
proposed
system
dermoscopic
based
on
integration
direction-ConvNet
(CD-ConvNet)
decompression
(DD-ConvNet)
with
discrete
cosine
transform
(DCT)
Huffman
coding,
called
DermCompressNet.
To
reconstruct
high-quality
at
receiver,
inverse
processes
using
DD-ConvNet
network
were
followed.
The
was
evaluated
measuring
several
metrics,
namely
mean
square
error
(MSE),
peak
signal-to-noise
(PSNR),
structural
similarity
index
measure
(SSIM),
along
CR,
computational
time
(CT).
experimental
results
34.6
dB,
2.5,
0.85,
56%
PSNR,
MSE,
SSIM,
respectively.
A
comparison
studies
JPEG
state-of-the-art
proved
superiority
system,
showing23%,
16%,
4.3%,
1.8%
improvements
respectively,
compared
JPEG.
Algorithms,
Journal Year:
2025,
Volume and Issue:
18(2), P. 59 - 59
Published: Jan. 22, 2025
This
paper
explores
the
overall
picture
regarding
healthcare
security
systems
through
an
extensive
literature
review.
As
sector
has
now
become
digitalized,
of
and,
by
extension,
protection
patient
data
is
a
key
concern
in
modern
era
technological
advances.
Therefore,
secure
and
integrated
system
essential.
Thus,
to
evaluate
relationship
between
quality,
we
conducted
research
identify
studies
reporting
their
association.
The
timeline
our
review
based
on
published
covering
period
from
2018
2024,
with
entries
identified
search
relevant
literature,
focusing
most
recent
developments
due
advances
artificial
intelligence
algorithmic
approaches.
Thirty-two
were
included
final
survey.
Our
findings
underscore
critical
role
that
significantly
improve
outcomes
maintain
integrity
services.
According
approach,
analyzed
highlight
growing
importance
advanced
frameworks,
especially
those
incorporating
methodologies,
safeguarding
while
enhancing
care
quality.
this
study,
uses
technology
approaches,
many
researchers
prove
ransomware
common
threat
hospital
information
systems,
more
are
needed
performance
created
against
kind
attack.
Nanotechnology Reviews,
Journal Year:
2024,
Volume and Issue:
13(1)
Published: Jan. 1, 2024
Abstract
The
rapid
expansion
of
nanotechnology
has
transformed
numerous
sectors,
with
nanoproducts
now
ubiquitous
in
everyday
life,
electronics,
healthcare,
and
pharmaceuticals.
Despite
their
widespread
adoption,
concerns
persist
regarding
potential
adverse
effects,
necessitating
vigilant
risk
management.
This
systematic
literature
review
advocates
for
leveraging
artificial
intelligence
(AI)
machine
learning
(ML)
methodologies
to
enhance
simulations
refine
safety
assessments
nanomaterials
(NMs).
Through
a
comprehensive
examination
the
existing
literature,
this
study
seeks
explain
pivotal
role
AI
boosting
NMs
sustainability
efforts
across
six
key
research
themes.
It
explores
significance
advancing
sustainability,
hazard
identification,
diverse
applications
field.
In
addition,
it
evaluates
past
strategies
while
proposing
innovative
avenues
future
exploration.
By
conducting
analysis,
aims
illuminate
current
landscape,
identify
challenges,
outline
pathways
integrating
ML
promote
sustainable
practices
within
nanotechnology.
Furthermore,
extending
these
technologies
monitor
real-world
behaviour
delivery.
its
thorough
investigation,
endeavours
address
obstacles
pave
way
safe
utilization
nanotechnology,
thereby
minimizing
associated
risks.
Mathematics,
Journal Year:
2025,
Volume and Issue:
13(6), P. 947 - 947
Published: March 13, 2025
Interior
design,
which
integrates
art
and
science,
is
vulnerable
to
infringements
such
as
copying
tampering.
The
unique
often
intricate
nature
of
these
designs
makes
them
unauthorized
replication
misuse,
posing
significant
challenges
for
designers
seeking
protect
their
intellectual
property.
To
solve
the
above
problems,
we
propose
a
deep
learning-based
zero-watermark
copyright
protection
method.
method
aims
embed
undetectable
information
through
image
fusion
technology
without
destroying
interior
design
image.
Specifically,
fuses
watermark
learning
generate
highly
robust
This
study
also
proposes
verification
network
with
U-Net
verify
validity
extract
efficiently.
can
accurately
restore
from
protected
images,
thus
effectively
proving
ownership
work
design.
According
on
an
experimental
dataset,
proposed
in
this
against
various
image-oriented
attacks.
It
avoids
problem
quality
loss
that
traditional
watermarking
techniques
may
cause.
Therefore,
provide
strong
means
field
Engineering Research Express,
Journal Year:
2024,
Volume and Issue:
6(2), P. 022202 - 022202
Published: June 1, 2024
Abstract
Deep
learning
has
shown
tremendous
potential
for
transforming
healthcare
by
enabling
more
accurate
diagnoses,
improved
treatment
planning
and
better
patient
outcome
predictions.
In
this
comprehensive
survey,
we
provide
a
detailed
overview
of
the
state-of-the-art
deep
techniques
their
applications
across
ecosystem.
We
first
introduce
fundamentals
discuss
its
key
advantages
compared
to
traditional
machine
approaches.
then
present
an
in-depth
review
major
in
medical
imaging,
electronic
health
record
analysis,
genomics,
robotics
other
domains.
For
each
application,
summarize
advancements,
outline
technical
details
methods,
challenges
limitations
highlight
promising
directions
future
work.
examine
cross-cutting
deploying
clinical
settings,
including
interpretability,
bias
data
scarcity.
conclude
proposing
roadmap
accelerate
translation
adoption
high-impact
learning.
Overall,
survey
provides
reference
researchers
practitioners
working
at
intersection
healthcare.
