IEEE Access,
Journal Year:
2021,
Volume and Issue:
9, P. 11911 - 11920
Published: Jan. 1, 2021
The
tuning
of
the
robot
actuator
represents
many
challenges
to
follow
a
predefined
trajectory
on
account
uncertainties
parameters
and
model
nonlinearity.
Furthermore,
controller
gains
require
proper
optimization
achieve
good
performance.
In
this
paper,
use
modified
neural
network
algorithm
(MNNA)
is
proposed
as
novel
adaptive
optimize
gains.
new
mathematical
modulation
introduced
promote
exploration
manner
NNA
without
initial
parameters.
Specifically,
formed
by
using
polynomial
mutation.
applied
select
proportional
integral
derivative
(PID)
manipulator
arms
in
lieu
conventional
procedures
designer
expertise.
Another
vital
contribution
formulating
performance
index
that
guarantees
improve
settling
time
overshoot
every
arm
output
simultaneously.
evaluated
with
different
intelligent
techniques
literature,
including
genetic
(GA)
cuckoo
search
(CSA)
PID
controllers,
where
its
superiority
various
trajectories
demonstrated.
To
affirm
robustness
efficiency
algorithm,
several
are
considered
for
assessing
response
robotic
manipulator.
Alexandria Engineering Journal,
Journal Year:
2023,
Volume and Issue:
68, P. 141 - 180
Published: Jan. 18, 2023
The
use
of
metaheuristics
is
one
the
most
encouraging
methodologies
for
taking
care
real-life
problems.
Bald
eagle
search
(BES)
algorithm
latest
swarm-intelligence
metaheuristic
inspired
by
intelligent
hunting
behavior
bald
eagles.
In
recent
research
works,
BES
has
performed
reasonably
well
over
a
wide
range
application
areas
such
as
chemical
engineering,
environmental
science,
physics
and
astronomy,
structural
modeling,
global
optimization,
engineering
design,
energy
efficiency,
etc.
However,
it
still
lacks
adequate
searching
efficiency
tendency
to
stuck
in
local
optima
which
affects
final
outcome.
This
paper
introduces
modified
(mBES)
that
removes
shortcomings
original
incorporating
three
improvements;
Opposition-based
learning
(OBL),
Chaotic
Local
Search
(CLS),
Transition
&
Pharsor
operators.
OBL
embedded
different
phases
standard
viz.
initial
population,
selecting,
space,
swooping
update
positions
individual
solutions
strengthen
exploration,
CLS
used
enhance
position
best
agent
will
lead
enhancing
all
individuals,
operators
help
provide
sufficient
exploration–exploitation
trade-off.
mBES
initially
evaluated
with
29
CEC2017
10
CEC2020
optimization
benchmark
functions.
addition,
practicality
tested
real-world
feature
selection
problem
five
design
Results
are
compared
against
number
classical
algorithms
using
statistical
metrics,
convergence
analysis,
box
plots,
Wilcoxon
rank
sum
test.
case
composite
test
functions
F21-F30,
wins
70%
cases,
whereas
rest
functions,
generates
good
results
65%
cases.
proposed
produces
performance
55%
45%
generated
competitive
results.
On
other
hand,
problems,
among
algorithms.
problem,
also
showed
competitiveness
observations
problems
show
superiority
robustness
baseline
metaheuristics.
It
can
be
safely
concluded
improvements
suggested
proved
effective
making
enough
solve
variety
Journal of King Saud University - Computer and Information Sciences,
Journal Year:
2023,
Volume and Issue:
35(7), P. 101611 - 101611
Published: June 10, 2023
The
process
of
creating
high-quality
labeled
data
is
crucial
for
training
machine-learning
models,
but
it
can
be
a
time-consuming
and
labor-intensive
process.
Moreover,
manual
annotation
by
human
annotators
lead
to
varying
degrees
competency,
training,
experience,
which
result
in
inconsistent
labeling
arbitrary
standards.
To
address
these
challenges,
researchers
have
been
exploring
automated
methods
enhancing
testing
datasets.
This
paper
proposes
SRL-ACO,
novel
text
augmentation
framework
that
leverages
Semantic
Role
Labeling
(SRL)
Ant
Colony
Optimization
(ACO)
techniques
generate
additional
natural
language
processing
(NLP)
models.
uses
SRL
identify
the
semantic
roles
words
sentence
ACO
new
sentences
preserve
roles.
SRL-ACO
enhance
accuracy
NLP
models
generating
without
requiring
annotation.
presents
experimental
results
demonstrating
effectiveness
on
seven
classification
datasets
sentiment
analysis,
toxic
detection
sarcasm
identification.
show
improves
performance
classifier
different
tasks.
These
demonstrate
has
potential
quality
quantity
various
