Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 449 - 480
Опубликована: Май 22, 2025
This
chapter
examines
the
vital
junction
between
artificial
intelligence
(AI)
and
critical
thinking,
highlighting
imperative
to
improve
thinking
abilities
effectively
manage
complexities
of
contemporary
society.
As
AI
technologies
are
progressively
incorporated
into
several
sectors,
capacity
assess,
evaluate,
synthesize
information
is
essential
for
effective
decision-making.
The
progression
AI,
its
applications
in
everyday
life,
ethical
implications
that
emerge
from
utilization.
It
underscores
significance
cultivating
using
educational
methodologies,
including
Socratic
seminars,
case
studies,
problem-based
learning.
By
fostering
a
culture
inquiry
adaptation,
humans
may
more
tackle
global
concerns
such
as
climate
change
inequality,
utilizing
means
inventive
solutions.
ultimately
promotes
proactive
strategy
emphasizes
era
AI.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 5, 2025
"PolynetDWTCADx"
is
a
sophisticated
hybrid
model
that
was
developed
to
identify
and
distinguish
colorectal
cancer.
In
this
study,
the
CKHK-22
dataset,
comprising
24
classes,
served
as
introduction.
The
proposed
method,
which
combines
CNNs,
DWTs,
SVMs,
enhances
accuracy
of
feature
extraction
classification.
study
employs
DWT
optimize
enhance
two
integrated
CNN
models
before
classifying
them
with
SVM
following
systematic
procedure.
PolynetDWTCADx
most
effective
we
evaluated.
It
capable
attaining
moderate
level
recall,
well
an
area
under
curve
(AUC)
during
testing.
testing
92.3%,
training
95.0%.
This
demonstrates
distinguishing
between
noncancerous
cancerous
lesions
in
colon.
We
can
also
employ
semantic
segmentation
algorithms
U-Net
architecture
accurately
segment
regions.
assessed
model's
exceptional
success
segmenting
providing
precise
delineation
malignant
tissues
using
its
maximal
IoU
value
0.93,
based
on
intersection
over
union
(IoU)
scores.
When
these
techniques
are
added
PolynetDWTCADx,
they
give
doctors
detailed
visual
information
needed
for
diagnosis
planning
treatment.
These
very
good
at
finding
separating
has
potential
recognition
management
cancer,
underscores.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 25, 2025
The
current
work
introduces
the
hybrid
ensemble
framework
for
detection
and
segmentation
of
colorectal
cancer.
This
will
incorporate
both
supervised
classification
unsupervised
clustering
methods
to
present
more
understandable
accurate
diagnostic
results.
method
entails
several
steps
with
CNN
models:
ADa-22
AD-22,
transformer
networks,
an
SVM
classifier,
all
inbuilt.
CVC
ClinicDB
dataset
supports
this
process,
containing
1650
colonoscopy
images
classified
as
polyps
or
non-polyps.
best
performance
in
ensembles
was
done
by
AD-22
+
Transformer
model,
AUC
0.99,
a
training
accuracy
99.50%,
testing
99.00%.
group
also
saw
high
97.50%
Polyps
99.30%
Non-Polyps,
together
recall
97.80%
98.90%
hence
performing
very
well
identifying
cancerous
healthy
regions.
proposed
here
uses
K-means
combination
visualisation
bounding
boxes,
thereby
improving
yielding
silhouette
score
0.73
cluster
configuration.
It
discusses
how
combine
feature
interpretation
challenges
into
medical
imaging
localization
precise
malignant
A
good
balance
between
generalization
shall
be
hyperparameter
optimization-heavy
learning
rates;
dropout
rates
overfitting
suppressed
effectively.
schema
treats
deficiencies
previous
approaches,
such
incorporating
CNN-based
effective
extraction,
networks
developing
attention
mechanisms,
finally
fine
decision
boundary
support
vector
machine.
Further,
we
refine
process
via
purpose
enhancing
procedure.
Such
holistic
framework,
hence,
further
boosts
results
generating
outcomes
rigorous
benchmarking
detecting
cancer
higher
reality
towards
clinical
application
feasibility.
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 209 - 230
Опубликована: Фев. 5, 2025
The
development
of
the
predictive
model
for
forecasting
hospital
readmissions
among
diabetic
patients
represents
a
remarkable
footstep
in
applying
machine
learning
techniques.
These
are
responsible
enhancing
healthcare
delivery.
Ethical
considerations,
such
as
transparent
judgment
and
bias
monitoring,
must
be
precisely
addressed
to
uphold
fairness
convince
therapist.
proposed
utilizes
Long
Short-Term
Memory
(LSTM)
neural
networks.
has
indicated
magnificent
accuracy
rate
83%
during
pilot
testing,
this
results
39%
reduction
30-day
readmission
with
cost
effective
enhanced
diagnosis
solutions.
Ongoing
research
efforts
should
enhance
interpretability,
explore
new
data
sources,
maintain
relevance
through
evolving
architectures
methodologies.
By
addressing
these
multifaceted
challenges
comprehensive
iterative
approach,
can
potentially
revolutionize
chronic
illness
management,
leading
improved
patient
outcomes
reduced
operational
costs
within
systems.
International Journal of Computational and Experimental Science and Engineering,
Год журнала:
2025,
Номер
11(2)
Опубликована: Апрель 4, 2025
Health
fraternity
is
invariably
challenged
with
early
diagnosis,
detection,
identification,
classification,
treatment
and
convalescence
of
globally
prevalent
life-threatening
fatal
diseases
as
liver
cancer.
The
detection
cancer
through
medical
image
processing
technique
so
challenging
that
an
iota
deviation
conspicuous
among
healthy
tissues,
benign
tumour
malignant
tissues
a
matter
wake
up
call.
This
work
entailed
introduction
novel,
optimized
YOLOv8-based
model
for
harnessing
the
strengths
transformer-based
feature
extraction,
global
attention
mechanisms,
advanced
aggregation
techniques.
was
subjected
to
rigorous
performance
relevant
methods
messages
parameters
time
again
repeated
refinements.
Eventually,
it
concluded
proposed
surpasses
all
models
in
extant
now
terms
precision,
recall,
means
average
precision
(mAP).
ascertained
by
inference
drawn
from
model’s
achievement
attaining
95.34%
96.49%
97.31%
[email protected].
In
regard
excels
differentiating
normal
cases,
tumours,
tumours.
These
innovations
represent
significant
step
toward
improving
accuracy
automated
diagnosis
systems,
potential
revolutionize
clinical
workflows
enhance
patient
outcomes.
Cancers,
Год журнала:
2025,
Номер
17(2), С. 285 - 285
Опубликована: Янв. 17, 2025
This
study
explores
a
semi-supervised
learning
(SSL),
pseudo-labeled
strategy
using
diverse
datasets
such
as
head
and
neck
cancer
(HNCa)
to
enhance
lung
(LCa)
survival
outcome
predictions,
analyzing
handcrafted
deep
radiomic
features
(HRF/DRF)
from
PET/CT
scans
with
hybrid
machine
systems
(HMLSs).
We
collected
199
LCa
patients
both
PET
CT
images,
obtained
TCIA
our
local
database,
alongside
408
HNCa
images
TCIA.
extracted
215
HRFs
1024
DRFs
by
PySERA
3D
autoencoder,
respectively,
within
the
ViSERA
1.0.0
software,
segmented
primary
tumors.
The
supervised
(SL)
employed
an
HMLS-PCA
connected
six
classifiers
on
DRFs.
SSL
expanded
adding
cases
(labeled
Random
Forest
algorithm)
cases,
same
HMLS
techniques.
Furthermore,
principal
component
analysis
(PCA)
linked
four
prediction
algorithms
were
utilized
in
hazard
ratio
analysis.
outperformed
SL
method
(p
<<
0.001),
achieving
average
accuracy
of
0.85
±
0.05
PCA
+
Multi-Layer
Perceptron
(MLP),
compared
0.69
0.06
for
Light
Gradient
Boosting
(LGB).
Additionally,
Component-wise
Survival
Analysis
DRFs,
CT,
had
C-index
0.80,
log
rank
p-value
0.001,
confirmed
external
testing.
Shifting
strategies,
particularly
contexts
limited
data
points,
enabling
or
alone,
can
significantly
achieve
high
predictive
performance.