Swarm intelligence enhanced machine learning model for predicting prognostic outcome in IgA Nephropathy patients with mild proteinuria
Biomedical Signal Processing and Control,
Journal Year:
2025,
Volume and Issue:
103, P. 107392 - 107392
Published: Jan. 6, 2025
Language: Английский
IoT-driven cancer prediction: Leveraging AI for early detection of protein structure variations
B. KalaiSelvi,
No information about this author
P. Anandan,
No information about this author
Sathishkumar Veerappampalayam Easwaramoorthy
No information about this author
et al.
Alexandria Engineering Journal,
Journal Year:
2025,
Volume and Issue:
118, P. 21 - 35
Published: Jan. 18, 2025
Language: Английский
An Enhancing Diagnostic Pulmonary Diseases Diagnostic method for Differentiating Talaromycosis from Tuberculosis
iScience,
Journal Year:
2025,
Volume and Issue:
28(2), P. 111867 - 111867
Published: Jan. 22, 2025
Language: Английский
Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection
Frontiers in Physiology,
Journal Year:
2025,
Volume and Issue:
16
Published: March 18, 2025
Lung
nodule
detection
is
a
crucial
task
for
diagnosis
and
lung
cancer
prevention.
However,
it
can
be
extremely
difficult
to
identify
tiny
nodules
in
medical
images
since
pulmonary
vary
greatly
shape,
size,
location.
Further,
the
implemented
methods
have
certain
limitations
including
scalability,
robustness,
data
availability,
false
rate.
To
overcome
existing
techniques,
this
research
proposes
Cnidaria
Herd
Optimization
(CHO)
algorithm-enabled
Bi-directional
Long
Short-Term
Memory
(CHSTM)
model
effective
detection.
Furthermore,
statistical
texture
descriptors
extract
significant
features
that
aid
improving
accuracy.
In
addition,
FC2R
segmentation
combines
optimized
fuzzy
C-means
clustering
algorithm
Resnet
-101
deep
learning
approach
effectively
improves
performance
of
model.
Specifically,
CHO
modelled
using
combination
induced
movement
strategy
krill
with
time
control
mechanism
cnidaria
find
optimal
solution
improve
CHSTM
model's
performance.
According
experimental
findings
comparison
between
other
established
methods,
+
achieves
98.09%
sensitivity,
97.71%
accuracy,
97.03%
specificity
TP
80
utilizing
LUNA-16
dataset.
Utilizing
LIDC/IDRI
dataset,
proposed
attained
high
accuracy
97.59%,
sensitivity
96.77%,
98.41%
k-fold
validation
outperforming
techniques.
The
detects
minimum
loss
better
Language: Английский
Mastitis diagnosis with machine learning algorithms
Neural Computing and Applications,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 29, 2025
Language: Английский
Generalizing fuzzy k-nearest neighbor classifier using an OWA operator with a RIM quantifier
Expert Systems with Applications,
Journal Year:
2025,
Volume and Issue:
unknown, P. 127795 - 127795
Published: April 1, 2025
Language: Английский
Enhancing gastric cancer early detection: A multi-verse optimized feature selection model with crossover-information feedback
Jiejun Lin,
No information about this author
Fangchao Zhu,
No information about this author
Xiaoyu Dong
No information about this author
et al.
Computers in Biology and Medicine,
Journal Year:
2024,
Volume and Issue:
175, P. 108535 - 108535
Published: April 29, 2024
Language: Английский
Advancing Intrauterine Adhesion Severity Prediction: Integrative Machine Learning Approach with Hysteroscopic Cold Knife System, Clinical Characteristics and Hematological Parameters
Jie Yang,
No information about this author
Xiaodong zheng,
No information about this author
Jiajia Pan
No information about this author
et al.
Computers in Biology and Medicine,
Journal Year:
2024,
Volume and Issue:
177, P. 108599 - 108599
Published: May 13, 2024
Language: Английский