Data & Metadata,
Journal Year:
2025,
Volume and Issue:
4, P. 756 - 756
Published: March 19, 2025
Vesicoureteral
reflux
(VUR)
is
traditionally
assessed
using
subjective
grading
systems,
leading
to
variability
in
diagnosis.
This
study
explores
the
potential
of
machine
learning
enhance
diagnostic
accuracy
by
analysing
voiding
cystourethrogram
(VCUG)
images.
The
objective
develop
predictive
models
that
provide
an
and
consistent
approach
VUR
classification.
A
total
113
VCUG
images
were
reviewed,
with
experts
them
based
on
severity.
Nine
distinct
image
features
selected
build
six
models,
which
evaluated
'leave-one-out'
cross-validation.
analysis
identified
renal
calyces’
deformation
patterns
as
key
indicators
high-grade
VUR.
models—Logistic
Regression,
Tree,
Gradient
Boosting,
Neural
Network,
Stochastic
Descent—achieved
precise
classifications
no
false
positives
or
negatives.
High
sensitivity
subtle
characteristic
different
grades
was
confirmed
substantial
Area
Under
Curve
(AUC)
values.
demonstrates
can
address
limitations
assessments,
offering
a
more
reliable
standardized
system.
findings
highlight
significance
predictor
severe
cases.
Future
research
should
focus
refining
methodologies,
exploring
additional
features,
expanding
dataset
model
clinical
applicability.
Gamification and Augmented Reality.,
Journal Year:
2025,
Volume and Issue:
3, P. 91 - 91
Published: Jan. 16, 2025
Phishing
remains
one
of
the
most
dangerous
threats
to
internet
users
and
organizations
today
since
it
utilizes
spoofed
websites
coax
into
revealing
their
data.
This
paper
focuses
on
effectiveness
algorithms
in
detecting
such
abusive
websites.
It
goes
analyze
dataset
phishing
non-
URLs
providing
explanatory
attributes
as
domain
registration
date,
URL
length
or
existence
HTTPS.
The
models
studied
include
Decision
Tree,
Random
Forest,
Support
Vector
Machines.
results
found
that
Forest
algorithm
had
best
performance
97%
terms
classification
accuracy,
Machines
performed
generalization
accuracy
with
precision
recall
values
0.92
0.95,
respectively.
study
investigates
feature
selection
determinants
structural
features
which
are
crucial
determining
efficiency
detection.
Also,
enhance
model
assessment
stratified
10-fold
cross-validation
technique
was
reduce
bias
variance.
These
Results
show
prospect
One
Layer
Neural
Networks
a
tool
improve
Detection
Systems
help
provide
low-cost
fast
solutions
for
current
future
cyberspace
struggles.
work
aims
increase
confidence
online
security
applications
against
modern
methods.The
proposed
modifications
will
strengthen
counter
measures
attacks
shifting
technological
context
while
also
working
towards
sustaining
thus
require
further
inquiry
facets
applicability
sophisticated
artificial
intelligence
techniques
use
useful
yet
diverse
sets
data
incorporation
explainable
intelligent
systems
Gamification and Augmented Reality.,
Journal Year:
2025,
Volume and Issue:
3, P. 81 - 81
Published: Jan. 16, 2025
Phishing
attacks
continue
to
be
a
danger
in
our
digital
world,
with
users
being
manipulated
via
rogue
websites
that
trick
them
into
disclosing
confidential
details.
This
article
focuses
on
the
use
of
machine
learning
techniques
process
identifying
phishing
websites.
In
this
case,
study
was
undertaken
critical
factors
such
as
URL
extension,
age
domain,
and
presence
HTTPS
whilst
exploring
effectiveness
Random
Forest,
Gradient
Boosting
and,
Support
Vector
Machines
algorithms
allocating
status
or
non-phishing.
study,
dataset
containing
real
URLs
are
employed
build
model
using
feature
extraction.
Following
this,
various
were
put
test
dataset;
out
all
models,
Forest
performed
exceptionally
well
having
achieved
an
accuracy
97.6%,
also
found
extremely
effective
possessing
strong
accuracy.
we
compared
discussed
methods
detect
site.
Some
features
affect
detection
performance
include
length,
special
characters
focus
even
more
aspects
need
further
development.
The
new
proposed
method
improves
because
applied,
recall
(true
positive)
increase,
while
false
positive
decrease.
results
enrich
electronic
security
system,
they
enable
time
mode.
has
demonstrated
importance
employing
cutting-edge
deal
safeguard
against
advanced
cyber
threats,
thus
laying
groundwork
for
innovation
systems
future
Data & Metadata,
Journal Year:
2025,
Volume and Issue:
4, P. 545 - 545
Published: Feb. 18, 2025
Diabetes
has
emerged
as
a
significant
global
health
issue,
especially
with
the
increasing
number
of
cases
in
many
countries.
This
trend
Underlines
need
for
greater
emphasis
on
early
detection
and
proactive
management
to
avert
or
mitigate
severe
complications
this
disease.
Over
recent
years,
machine
learning
algorithms
have
shown
promising
potential
predicting
diabetes
risk
are
beneficial
practitioners.
Objective:
study
highlights
prediction
capabilities
statistical
non-statistical
methods
over
classification
768
samples
from
Pima
Indians
Database.
It
consists
demographic
clinical
features
age,
body
mass
index
(BMI)
blood
glucose
levels
that
greatly
depend
vulnerability
against
Diabetes.
The
experimentation
assesses
various
types
terms
accuracy
effectiveness
regarding
prediction.
These
include
Logistic
Regression,
Decision
Tree,
Random
Forest,
K-Nearest
Neighbors,
Naive
Bayes,
Support
Vector
Machine,
Gradient
Boosting
Neural
Network
Models.
results
show
algorithm
gained
highest
predictive
78.57%,
then
Forest
had
second
position
76.30%
accuracy.
findings
techniques
not
just
highly
effective.
Still,
they
also
can
potentially
act
screening
tools
within
data-driven
fashion
valuable
information
who
is
more
likely
get
affected.
In
addition,
help
realize
timely
intervention
longer
term,
which
step
towards
reducing
outcomes
disease
burden
attributable
healthcare
systems.
Data & Metadata,
Journal Year:
2025,
Volume and Issue:
3
Published: Jan. 2, 2025
Oral
cancer
presents
a
formidable
challenge
in
oncology,
necessitating
early
diagnosis
and
accurate
prognosis
to
enhance
patient
survival
rates.
Recent
advancements
machine
learning
data
mining
have
revolutionized
traditional
diagnostic
methodologies,
providing
sophisticated
automated
tools
for
differentiating
between
benign
malignant
oral
lesions.
This
study
comprehensive
review
of
cutting-edge
including
Neural
Networks,
K-Nearest
Neighbors
(KNN),
Support
Vector
Machines
(SVM),
ensemble
techniques,
specifically
applied
the
cancer.
Through
rigorous
comparative
analysis,
our
findings
reveal
that
Networks
surpass
other
models,
achieving
an
impressive
classification
accuracy
93.6%
predicting
Furthermore,
we
underscore
potential
benefits
integrating
feature
selection
dimensionality
reduction
techniques
model
performance.
These
insights
significant
promise
advanced
bolstering
detection,
optimizing
treatment
strategies,
ultimately
improving
outcomes
realm
oncology
LatIA,
Journal Year:
2024,
Volume and Issue:
3, P. 84 - 84
Published: Nov. 30, 2024
Gene
microarray
classification
is
yet
a
difficult
task
because
of
the
bigness
data
and
limited
number
samples
available.
Thus,
need
for
efficient
selection
subset
genes
necessary
to
cut
down
on
computation
costs
improve
performance.
Consistently,
this
study
employs
Correlation-based
Feature
Selection
(CFS)
algorithm
identify
informative
genes,
thereby
decreasing
dimensions
isolating
discriminative
features.
Thereafter,
three
classifiers,
Decision
Table,
JRip
OneR
were
used
assess
The
strategy
was
implemented
eleven
such
that
reduced
compared
with
complete
gene
set
results.
observed
results
lead
conclusion
CFS
efficiently
eliminates
irrelevant,
redundant,
noisy
features
as
well.
This
method
showed
great
prediction
opportunities
relevant
differentiation
datasets.
performed
best
among
Table
by
average
accuracy
in
all
mentioned
However,
approach
has
many
advantages
enhances
several
classes
large
numbers
high
time
complexity.
Data & Metadata,
Journal Year:
2024,
Volume and Issue:
4, P. 472 - 472
Published: Nov. 26, 2024
Brain
cancer
remains
one
of
the
most
challenging
medical
conditions
due
to
its
intricate
nature
and
critical
functions
brain.
Effective
diagnostic
treatment
strategies
are
essential,
particularly
given
high
stakes
involved
in
early
detection.
Magnetic
Resonance
(MR)
imaging
has
emerged
as
a
crucial
modality
for
identification
monitoring
brain
tumors,
offering
detailed
insights
into
tumor
morphology
behavior.
Recent
advancements
artificial
intelligence
(AI)
machine
learning
(ML)
have
revolutionized
analysis
imaging,
significantly
enhancing
precision
efficiency.
This
study
classifies
three
primary
types—glioma,
meningioma,
general
tumors—utilizing
comprehensive
dataset
comprising
15,000
MR
images
obtained
from
Kaggle.
We
evaluated
performance
six
distinct
models:
K-Nearest
Neighbors
(KNN),
Neural
Networks,
Logistic
Regression,
Support
Vector
Machine
(SVM),
Decision
Trees,
Random
Forests.
Each
model's
effectiveness
was
assessed
through
multiple
metrics,
including
classification
accuracy
(CA),
Area
Under
Curve
(AUC),
F1
score,
precision,
recall.
Our
findings
reveal
that
KNN
Networks
achieved
remarkable
accuracies
98.5%
98.4%,
respectively,
surpassing
other
models.
These
results
underscore
promise
ML
algorithms,
improving
process
imaging.
Future
research
will
focus
on
validating
these
models
with
real-world
clinical
data,
aiming
refine
enhance
methodologies,
thus
contributing
development
more
accurate,
efficient,
accessible
tools
diagnosis
management.
Deleted Journal,
Journal Year:
2024,
Volume and Issue:
3
Published: Dec. 30, 2024
Smoking
remains
a
global
health
crisis,
contributing
to
addiction
and
diverse
diseases
through
complex
biological
mechanisms.
This
study
explores
the
hypothesis
that
smoking
induces
epigenetic
modifications
alters
bidirectional
neurobiological
pathways,
perpetuating
disease
progression.
Leveraging
dataset
of
55,692
individuals
with
27
metrics,
we
analyze
associations
between
status
physiological
markers
(e.g.,
lipid
profiles,
blood
pressure,
liver
enzymes)
infer
potential
mediators.
Preliminary
data
reveal
significant
correlations
elevated
triglycerides,
LDL
cholesterol,
function
markers,
suggesting
systemic
inflammation
oxidative
stress
as
plausible
intermediaries.
We
propose
methodology
integrating
bioinformatics
systems
biology
map
smoking-associated
phenotypic
changes
loci
DNA
methylation)
neural
circuits
dopaminergic
pathways).
work
aims
bridge
clinical
observations
molecular
mechanisms,
offering
insights
into
personalized
interventions
targeting
smoking’s
"fingerprints"
their
consequences.
Deleted Journal,
Journal Year:
2024,
Volume and Issue:
3
Published: Dec. 30, 2024
Tobacco
smoking
keeps
to
exert
a
profound
effect
on
cardiovascular
health,
contributing
situations
including
arterial
stiffness,
hypertension,
and
microcirculatory
disorder.
Traditional
studies
strategies,
often
siloed
into
remoted
domains
like
biomarker
analysis
or
behavioral
surveys,
fail
seize
the
dynamic
interplay
between
behaviors
biological
disruptions.
This
take
look
at
integrates
AI-driven
analytics,
wearable
sensor
networks,
deep
biomolecular
profiling
map
smoking’s
multidimensional
effects.
By
combining
actual-time
physiological
statistics
(e.g.,
PPG,
HRV)
with
epigenetic
proteomic
markers,
research
objectives
are
expecting
individual
risks
enable
preemptive
interventions.
Results
reveal
efficacy
of
ensemble
models
Random
Forest
(AUC
=
zero.889)
in
taking
pictures
complex
interactions
among
variables
consisting
γ-GTP,
waist
circumference,
blood
stress.
The
paintings
highlight
capability
AI
wearables
convert
reactive
healthcare
personalized,
preventive
strategies.
Diagnostics,
Journal Year:
2025,
Volume and Issue:
15(5), P. 562 - 562
Published: Feb. 26, 2025
Background:
Typhoid
fever
remains
a
significant
public
health
challenge,
especially
in
developing
countries
where
diagnostic
resources
are
limited.
Accurate
and
timely
diagnosis
is
crucial
for
effective
treatment
disease
containment.
Traditional
methods,
while
effective,
can
be
time-consuming
resource-intensive.
This
study
aims
to
develop
lightweight
machine
learning-based
tool
the
early
efficient
detection
of
typhoid
using
clinical
data.
Methods:
A
custom
dataset
comprising
14
demographic
parameters-including
age,
gender,
headache,
muscle
pain,
nausea,
diarrhea,
cough,
range
(°F),
hemoglobin
(g/dL),
platelet
count,
urine
culture
bacteria,
calcium
(mg/dL),
potassium
(mg/dL)-was
analyzed.
learning
metamodel,
integrating
Support
Vector
Machine
(SVM),
Gaussian
Naive
Bayes
(GNB),
Decision
Tree
classifiers
with
Light
Gradient
Boosting
(LGBM),
was
trained
evaluated
k-fold
cross-validation.
Performance
assessed
precision,
recall,
F1-score,
area
under
receiver
operating
characteristic
curve
(AUC).
Results:
The
proposed
metamodel
demonstrated
superior
performance,
achieving
precision
99%,
recall
100%,
an
AUC
1.00.
It
outperformed
traditional
methods
other
standalone
algorithms,
offering
high
accuracy
generalizability.
Conclusions:
provides
cost-effective,
non-invasive,
rapid
alternative
fever,
particularly
suited
resource-limited
settings.
Its
reliance
on
accessible
parameters
ensures
practical
applicability
scalability,
potentially
improving
patient
outcomes
aiding
control.
Future
work
will
focus
broader
validation
integration
into
workflows
further
enhance
its
utility.
Data & Metadata,
Journal Year:
2025,
Volume and Issue:
4, P. 894 - 894
Published: April 7, 2025
Identification
of
a
person
is
central
in
forensic
science,
security,
and
healthcare.
Methods
such
as
iris
scanning
genomic
profiling
are
more
accurate
but
expensive,
time-consuming,
difficult
to
implement.
This
study
focuses
on
the
relationship
between
fingerprint
patterns
ABO
blood
group
biometric
identification
tool.
A
total
200
subjects
were
included
study,
types
(loops,
whorls,
arches)
groups
compared.
Associations
evaluated
with
statistical
tests,
including
chi-square
Pearson
correlation.The
found
that
loops
most
common
pattern
O+
was
prevalent.
Discussion:
Even
though
there
some
associative
pattern,
no
statistically
significant
difference
different
groups.
Overall,
results
indicate
data
do
not
significantly
improve
personal
when
used
conjunction
fingerprinting.Although
shows
weak
correlation,
it
may
emphasize
efforts
multi-modal
based
systems
enhancing
current
systems.
Future
studies
focus
larger
diverse
samples,
possibly
machine
learning
additional
biometrics
methods.
addresses
an
element
ever-changing
nature
fields
science
identification,
highlighting
importance
resilient
analytical
methods
for
identification.