Physics of Fluids,
Journal Year:
2024,
Volume and Issue:
36(4)
Published: April 1, 2024
Deep
reinforcement
learning
(DRL)
has
emerged
as
a
promising
approach
for
handling
highly
dynamic
and
nonlinear
active
flow
control
(AFC)
problems.
However,
the
computational
cost
associated
with
training
DRL
models
presents
significant
performance
bottleneck.
To
address
this
challenge
enable
efficient
scaling
on
high-performance
computing
architectures,
study
focuses
optimizing
DRL-based
algorithms
in
parallel
settings.
We
validate
an
existing
state-of-the-art
framework
used
AFC
problems
discuss
its
efficiency
bottlenecks.
Subsequently,
by
deconstructing
overall
conducting
extensive
scalability
benchmarks
individual
components,
we
investigate
various
hybrid
parallelization
configurations
propose
strategies.
Moreover,
refine
input/output
(I/O)
operations
multi-environment
to
tackle
critical
overhead
data
movement.
Finally,
demonstrate
optimized
typical
problem
where
near-linear
can
be
obtained
framework.
achieve
boost
from
around
49%
approximately
78%,
process
is
accelerated
47
times
using
60
central
processing
unit
(CPU)
cores.
These
findings
are
expected
provide
valuable
insight
further
advancements
studies.
World Electric Vehicle Journal,
Journal Year:
2024,
Volume and Issue:
15(2), P. 39 - 39
Published: Jan. 26, 2024
Electric
vehicles
are
widely
adopted
globally
as
a
sustainable
mode
of
transportation.
With
the
increased
availability
onboard
computation
and
communication
capabilities,
moving
towards
automated
driving
intelligent
transportation
systems.
The
adaption
technologies
such
IoT,
edge
intelligence,
5G,
blockchain
in
vehicle
architecture
has
possibilities
efficient
In
this
article,
we
present
comprehensive
study
analysis
computing
paradigm,
explaining
elements
AI.
Furthermore,
discussed
intelligence
approach
for
deploying
AI
algorithms
models
on
devices,
which
typically
resource-constrained
devices
located
at
network.
It
mentions
advantages
its
use
cases
smart
electric
vehicles.
also
discusses
challenges
opportunities
provides
in-depth
optimizing
intelligence.
Finally,
it
sheds
some
light
research
roadmap
by
dividing
efforts
into
topology,
content,
service
segments,
model
adaptation,
framework
design,
processor
acceleration,
all
stand
to
gain
from
technologies.
Investigating
incorporation
important
technologies,
issues,
opportunities,
Roadmap
will
be
valuable
resource
community
engaged
Journal of Imaging,
Journal Year:
2025,
Volume and Issue:
11(2), P. 59 - 59
Published: Feb. 15, 2025
Artificial
intelligence
(AI)
transforms
image
data
analysis
across
many
biomedical
fields,
such
as
cell
biology,
radiology,
pathology,
cancer
and
immunology,
with
object
detection,
feature
extraction,
classification,
segmentation
applications.
Advancements
in
deep
learning
(DL)
research
have
been
a
critical
factor
advancing
computer
techniques
for
mining.
A
significant
improvement
the
accuracy
of
detection
algorithms
has
achieved
result
emergence
open-source
software
innovative
neural
network
architectures.
Automated
now
enables
extraction
quantifiable
cellular
spatial
features
from
microscope
images
cells
tissues,
providing
insights
into
organization
various
diseases.
This
review
aims
to
examine
latest
AI
DL
mining
microscopy
images,
aid
biologists
who
less
background
knowledge
machine
(ML),
incorporate
ML
models
focus
images.
Sensors,
Journal Year:
2024,
Volume and Issue:
24(8), P. 2461 - 2461
Published: April 11, 2024
Reinforcement
learning
(RL)
has
emerged
as
a
dynamic
and
transformative
paradigm
in
artificial
intelligence,
offering
the
promise
of
intelligent
decision-making
complex
environments.
This
unique
feature
enables
RL
to
address
sequential
problems
with
simultaneous
sampling,
evaluation,
feedback.
As
result,
techniques
have
become
suitable
candidates
for
developing
powerful
solutions
various
domains.
In
this
study,
we
present
comprehensive
systematic
review
algorithms
applications.
commences
an
exploration
foundations
proceeds
examine
each
algorithm
detail,
concluding
comparative
analysis
based
on
several
criteria.
then
extends
two
key
applications
RL:
robotics
healthcare.
manipulation,
enhances
precision
adaptability
tasks
such
object
grasping
autonomous
learning.
healthcare,
turns
its
focus
realm
cell
growth
problems,
clarifying
how
provided
data-driven
approach
optimizing
cultures
development
therapeutic
solutions.
offers
overview,
shedding
light
evolving
landscape
potential
diverse
yet
interconnected
fields.
International Journal of Science and Research (IJSR),
Journal Year:
2024,
Volume and Issue:
13(2), P. 273 - 280
Published: Feb. 5, 2024
This
paper
explores
the
evolving
landscape
of
patient
engagement
in
healthcare,
emphasizing
pivotal
role
artificial
intelligence
(AI).
It
delves
into
historical
context
-provider
dynamics,
shifting
from
a
predominantly
authoritative
approach
to
more
collaborative
and
tech
-driven
model.
The
highlights
impact
digital
technologies
like
health
apps,
AIdriven
chatbots,
virtual
assistants
personalizing
education,
improving
treatment
adherence,
enhancing
overall
care.
Additionally,
it
examines
various
applications
AI
diagnostics
personalized
administrative
efficiency,
underscoring
potential
revolutionize
healthcare
delivery
engagement.
