Advances in bioinformatics and biomedical engineering book series,
Год журнала:
2024,
Номер
unknown, С. 62 - 74
Опубликована: Март 22, 2024
Artificial
intelligence
(AI)
poses
a
number
of
moral
and
legal
challenges
to
modern
civilization.
These
include
invasions
privacy,
discrimination,
the
function
human
judgment.
The
use
more
recent
digital
technologies
has
sparked
worries
that
they
could
introduce
new
forms
error
data
breaches.
For
patients
who
fall
prey
healthcare
technique
or
protocol
errors,
repercussions
may
be
catastrophic.
Keep
this
in
mind
at
all
times;
often
interact
with
doctors
times
when
are
feeling
their
weakest.
potential
ethical
concerns
raised
by
widespread
AI
settings
not
yet
adequately
addressed
existing
legislation.
All
parties
participating
process
should
protected,
there
openness
privacy
algorithms;
also,
cybersecurity
measures
place
address
any
vulnerability
arise.
Abstract
In
recent
decades,
fog
computing
has
contributed
significantly
to
the
expansion
of
smart
cities.
It
generated
numerous
real‐time
data
and
coped
with
time‐constraint
applications.
They
use
sensors,
physical
objects,
network
standards
monitor
health
imaging,
traffic
surveillance,
industrial
management,
so
forth.
Interactive
applications
have
been
proposed
for
Internet
Things
(IoT)
control
wireless
channels
improve
communication.
However,
most
existing
lack
handing
interference
a
reliable
monitoring
process.
Moreover,
many
solutions
are
vulnerable
external
threats,
resulting
in
inconsistent
untrustworthy
information
end
users.
Thus,
this
article
proposes
framework
that
considers
possible
shortest
paths
provide
low‐latency
healthcare
decision
system
using
Q‐learning.
addition,
devices
offer
trusted
transmission
kept
secure.
The
is
specially
designed
rapid
medical
processing
while
enforcing
robust
security
throughout
IoT‐based
To
identify
sensors
pairwise
objects
initial
cost,
applies
graph
theory.
also
extracts
effective
least
loaded
communication
edges
by
examining
behaviour
devices.
identities
verified
lightweight
timestamps
secret
information,
accordingly,
it
decreases
privacy
threats.
The Innovation Life,
Год журнала:
2024,
Номер
2(3), С. 100079 - 100079
Опубликована: Янв. 1, 2024
<p>Biomedical
data
encompasses
images,
texts,
physiological
signals,
and
molecular
omics
data.
As
the
costs
of
various
acquisition
methods,
such
as
genomic
sequencing,
continue
to
decrease,
availability
biomedical
is
increasing.
However,
this
often
exhibits
high
dimensionality,
heterogeneity,
multimodal
characteristics,
necessitating
use
advanced
computational
modeling.
Transforming
raw
into
meaningful
biological
insights
a
critical
aspect
modeling,
which
plays
an
increasingly
important
role
in
research
era
big
This
review
outlines
collection
types
challenges
faced
including
standardization,
privacy
protection.
Additionally,
it
addresses
complexity
interpretability
models
used
guide
knowledge
discoveries.
The
also
discusses
architectures
parallel
computing,
cloud
edge
are
essential
meet
demands
large-scale
computation.
Furthermore,
highlights
driving
force
modeling
advancing
medical
research.
With
foundation
data,
models,
computation,
transitioning
from
experimental
observation
theoretical
deduction
data-driven
approaches,
profoundly
impacting
scientific
methodologies
paradigms.
development
steering
toward
intelligent
medicine,
redefining
paradigm
biomedicine.</p>
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Ноя. 20, 2024
In
the
rapidly
growing
Internet
of
Things
(IoT)
landscape,
federated
learning
(FL)
plays
a
crucial
role
in
enhancing
performance
heterogeneous
edge
computing
environments
due
to
its
scalability,
robustness,
and
low
energy
consumption.
However,
one
major
challenges
such
is
efficient
selection
nodes
optimization
resource
allocation,
especially
dynamic
resource-constrained
settings.
To
address
this,
we
propose
novel
architecture
called
Multi-Edge
Clustered
Edge
AI
Heterogeneous
Federated
Learning
(MEC-AI
HetFL),
which
leverages
multi-edge
clustering
AI-driven
node
communication.
This
enables
collaborate,
dynamically
selecting
significant
optimizing
global
tasks
with
complexity.
Compared
existing
solutions
like
EdgeFed,
FedSA,
FedMP,
H-DDPG,
MEC-AI
HetFL
improves
quality
score,
accuracy,
offering
up
5
times
better
distributed
environments.
The
solution
validated
through
simulations
network
traffic
tests,
demonstrating
ability
key
IoT
deployments.
Advances in bioinformatics and biomedical engineering book series,
Год журнала:
2024,
Номер
unknown, С. 62 - 74
Опубликована: Март 22, 2024
Artificial
intelligence
(AI)
poses
a
number
of
moral
and
legal
challenges
to
modern
civilization.
These
include
invasions
privacy,
discrimination,
the
function
human
judgment.
The
use
more
recent
digital
technologies
has
sparked
worries
that
they
could
introduce
new
forms
error
data
breaches.
For
patients
who
fall
prey
healthcare
technique
or
protocol
errors,
repercussions
may
be
catastrophic.
Keep
this
in
mind
at
all
times;
often
interact
with
doctors
times
when
are
feeling
their
weakest.
potential
ethical
concerns
raised
by
widespread
AI
settings
not
yet
adequately
addressed
existing
legislation.
All
parties
participating
process
should
protected,
there
openness
privacy
algorithms;
also,
cybersecurity
measures
place
address
any
vulnerability
arise.
