IEEE Transactions on Neural Systems and Rehabilitation Engineering,
Journal Year:
2024,
Volume and Issue:
33, P. 220 - 231
Published: Dec. 24, 2024
Adult
attention
deficit
hyperactivity
disorder
(ADHD),
a
prevalent
psychiatric
disorder,
significantly
impacts
social,
academic,
and
occupational
functioning.
However,
it
has
been
relatively
less
prioritized
compared
to
childhood
ADHD.
This
study
employed
functional
near-infrared
spectroscopy
(fNIRS)
during
verbal
fluency
tasks
in
conjunction
with
machine
learning
(ML)
techniques
differentiate
between
healthy
controls
(N=75)
ADHD
individuals
(N=120).
Efficient
feature
selection
high-dimensional
fNIRS
datasets
is
crucial
for
improving
accuracy.
To
address
this,
we
propose
hybrid
method
that
combines
wrapper-based
embedded
approach,
termed
Bayesian-Tuned
Ridge
RFECV
(BTR-RFECV).
The
proposed
facilitated
streamlined
hyperparameter
tuning
data,
thereby
reducing
the
number
of
features
while
enhancing
HbO
from
combined
frontal
temporal
regions
were
key,
models
achieving
precision
(89.89%),
recall
(89.74%),
F-1
score
(89.66%),
accuracy
MCC
(78.36%),
GDR
(88.45%).
outcomes
this
highlight
promising
potential
combining
ML
as
diagnostic
tools
clinical
settings,
offering
pathway
reduce
manual
intervention.
Biomimetics,
Journal Year:
2025,
Volume and Issue:
10(1), P. 41 - 41
Published: Jan. 10, 2025
With
the
advancement
of
Internet,
social
media
platforms
have
gradually
become
powerful
in
spreading
crisis-related
content.
Identifying
informative
tweets
associated
with
natural
disasters
is
beneficial
for
rescue
operation.
When
faced
massive
text
data,
choosing
pivotal
features,
reducing
calculation
expense,
and
increasing
model
classification
performance
a
significant
challenge.
Therefore,
this
study
proposes
multi-strategy
improved
black-winged
kite
algorithm
(MSBKA)
feature
selection
disaster
based
on
wrapper
method's
principle.
Firstly,
BKA
by
utilizing
enhanced
Circle
mapping,
integrating
hierarchical
reverse
learning,
introducing
Nelder-Mead
method.
Then,
MSBKA
combined
excellent
classifier
SVM
(RBF
kernel
function)
to
construct
hybrid
model.
Finally,
MSBKA-SVM
performs
tweet
tasks.
The
empirical
analysis
data
from
four
shows
that
proposed
has
achieved
an
accuracy
0.8822.
Compared
GA,
PSO,
SSA,
BKA,
increased
4.34%,
2.13%,
2.94%,
6.35%,
respectively.
This
research
proves
can
play
supporting
role
risk.
