e-Prime - Advances in Electrical Engineering Electronics and Energy,
Journal Year:
2024,
Volume and Issue:
8, P. 100522 - 100522
Published: March 25, 2024
Machine
learning
is
the
analysis
based
on
data
that
gives
strategic
decisions
to
cultivate
an
accurate
and
stable
framework
for
different
applications.
Access
medical
with
utmost
privacy
high
rates
still
a
challenging
problem.
To
accomplish
above-mentioned
features,
performance
of
federated
(FL)
5G
massive
multiple-input-multiple-output
(MIMO)
investigated
IoMT
systems.
This
provides
energy-efficient
privacy-preserving
solution
throughput
digital
health
system.
In
proposed
model,
uplink
scenario
using
detection
techniques.
The
are
evaluated
at
central
server
edge
devices
signal-to-noise
ratios
(SNRs)
fading
channels.
ML
bit
error
rate
(BER)
better
than
MRC
but
higher
complexity.
accuracy
obtained
approximately
90%
improvement
around
8%
9%
as
compared
baseline
approach.
Mathematical Biosciences & Engineering,
Journal Year:
2023,
Volume and Issue:
20(11), P. 20155 - 20187
Published: Jan. 1, 2023
<abstract><p>A
continuous-time
exhaustive-limited
(K
=
2)
two-level
polling
control
system
is
proposed
to
address
the
needs
of
increasing
network
scale,
service
volume
and
performance
prediction
in
Internet
Things
(IoT)
Long
Short-Term
Memory
(LSTM)
an
attention
mechanism
used
for
its
predictive
analysis.
First,
central
site
uses
exhaustive
policy
common
Limited
K
2
establish
a
system.
Second,
exact
expressions
average
queue
length,
delay
cycle
period
are
derived
using
probability
generating
functions
Markov
chains
MATLAB
simulation
experiment.
Finally,
LSTM
neural
model
constructed
prediction.
The
experimental
results
show
that
theoretical
simulated
values
basically
match,
verifying
rationality
Not
only
does
it
differentiate
priorities
ensure
receives
quality
fairness
site,
but
also
improves
by
7.3
12.2%,
respectively,
compared
with
one-level
limited
service;
gated-
model,
length
this
smaller
than
service,
indicating
higher
priority
model.
Compared
1
increases
number
information
packets
sent
at
once
has
better
latency
performance,
providing
stable
reliable
guarantee
wireless
services
high
requirements.
Following
on
from
this,
fast
evaluation
method
proposed:
Neural
prediction,
which
can
accurately
predict
as
size
simplify
calculations.</p></abstract>
e-Prime - Advances in Electrical Engineering Electronics and Energy,
Journal Year:
2024,
Volume and Issue:
8, P. 100522 - 100522
Published: March 25, 2024
Machine
learning
is
the
analysis
based
on
data
that
gives
strategic
decisions
to
cultivate
an
accurate
and
stable
framework
for
different
applications.
Access
medical
with
utmost
privacy
high
rates
still
a
challenging
problem.
To
accomplish
above-mentioned
features,
performance
of
federated
(FL)
5G
massive
multiple-input-multiple-output
(MIMO)
investigated
IoMT
systems.
This
provides
energy-efficient
privacy-preserving
solution
throughput
digital
health
system.
In
proposed
model,
uplink
scenario
using
detection
techniques.
The
are
evaluated
at
central
server
edge
devices
signal-to-noise
ratios
(SNRs)
fading
channels.
ML
bit
error
rate
(BER)
better
than
MRC
but
higher
complexity.
accuracy
obtained
approximately
90%
improvement
around
8%
9%
as
compared
baseline
approach.
