2022 9th International Conference on Computing for Sustainable Global Development (INDIACom),
Journal Year:
2024,
Volume and Issue:
unknown, P. 504 - 509
Published: Feb. 28, 2024
In
today's
fast-paced
world,
the
need
for
real-time
tracking
technology
has
grown
more
and
significant,
particularly
assets
vehicles.
The
goal
of
this
project
is
to
equip
vehicles
with
a
cutting-edge
system
based
on
ESP32.
To
effectively
track
monitor
or
vehicles,
combines
an
ESP32
Wi-Fi
board,
GPS
Neo
module,
SIM800
battery
power
source.
This
create
cars
For
effective
monitoring,
comprises
supply.
While
module
enables
precise
location,
handles
wireless
communication
processing.
Innovative
cellular
connectivity
made
possible
remote
monitoring
by
inclusion
module.
source
ensures
continuous
operation.
research
demonstrates
how
these
elements
may
be
combined
produce
modern
that
will
useful
applications
in
asset
management,
transportation,
security.
Numerous
advantages
come
suggested
ESP32-based
system,
including
increased
security,
improved
logistical
operations.
It
makes
it
people
companies
keep
whereabouts
condition
their
instantly,
enabling
proactive
decision-making,
resource
allocation,
general
operational
efficiency
improvements.
Bioengineering,
Journal Year:
2024,
Volume and Issue:
11(12), P. 1302 - 1302
Published: Dec. 23, 2024
Accurate
segmentation
of
brain
tumors
in
MRI
scans
is
critical
for
diagnosis
and
treatment
planning.
Traditional
models,
such
as
U-Net,
excel
capturing
spatial
information
but
often
struggle
with
complex
tumor
boundaries
subtle
variations
image
contrast.
These
limitations
can
lead
to
inconsistencies
identifying
regions,
impacting
the
accuracy
clinical
outcomes.
To
address
these
challenges,
this
paper
proposes
a
novel
modification
U-Net
architecture
by
integrating
attention
mechanism
designed
dynamically
focus
on
relevant
regions
within
scans.
This
innovation
enhances
model's
ability
delineate
fine
improves
precision.
Our
model
was
evaluated
Figshare
dataset,
which
includes
annotated
images
meningioma,
glioma,
pituitary
tumors.
The
proposed
achieved
Dice
similarity
coefficient
(DSC)
0.93,
recall
0.95,
an
AUC
0.94,
outperforming
existing
approaches
V-Net,
DeepLab
V3+,
nnU-Net.
results
demonstrate
effectiveness
our
addressing
key
challenges
like
low-contrast
boundaries,
small
overlapping
Furthermore,
lightweight
design
ensures
its
suitability
real-time
applications,
making
it
robust
tool
automated
segmentation.
study
underscores
potential
mechanisms
significantly
enhance
medical
imaging
models
paves
way
more
effective
diagnostic
tools.
Sensors,
Journal Year:
2025,
Volume and Issue:
25(9), P. 2746 - 2746
Published: April 26, 2025
A
brain
tumor
is
the
result
of
abnormal
growth
cells
in
central
nervous
system
(CNS),
widely
considered
as
a
complex
and
diverse
clinical
entity
that
difficult
to
diagnose
cure.
In
this
study,
we
focus
on
current
advances
medical
imaging,
particularly
magnetic
resonance
imaging
(MRI),
how
machine
learning
(ML)
deep
(DL)
algorithms
might
be
combined
with
assessments
improve
diagnosis.
Due
its
superior
contrast
resolution
safety
compared
other
methods,
MRI
highlighted
preferred
modality
for
tumors.
The
challenges
related
analysis
different
processes
including
detection,
segmentation,
classification,
survival
prediction
are
addressed
along
ML/DL
approaches
significantly
these
steps.
We
systematically
analyzed
107
studies
(2018–2024)
employing
ML,
DL,
hybrid
models
across
publicly
available
datasets
such
BraTS,
TCIA,
Figshare.
light
recent
developments
analysis,
many
have
been
proposed
accurately
obtain
ontological
characteristics
tumors,
enhancing
diagnostic
precision
personalized
therapeutic
strategies.
