Frontiers in Public Health,
Journal Year:
2025,
Volume and Issue:
12
Published: Jan. 15, 2025
This
study
delves
into
the
parenting
cognition
perspectives
on
COVID-19
in
children,
exploring
symptoms,
transmission
modes,
and
protective
measures.
It
aims
to
correlate
these
with
sociodemographic
factors
employ
advanced
machine-learning
techniques
for
comprehensive
analysis.
Data
collection
involved
a
semi-structured
questionnaire
covering
parental
knowledge
attitude
transmission,
measures,
government
satisfaction.
The
analysis
utilised
Generalised
Linear
Regression
Model
(GLM),
K-Nearest
Neighbours
(KNN),
Support
Vector
Machine
(SVM),
Random
Forest
(RF),
Naive
Bayes
(NB),
AdaBoost
(AB).
revealed
an
average
score
of
18.02
±
2.9,
43.2
52.9%
parents
demonstrating
excellent
good
knowledge,
respectively.
News
channels
(85%)
emerged
as
primary
information
source.
Commonly
reported
symptoms
included
cough
(96.47%)
fever
(95.6%).
GLM
indicated
lower
awareness
rural
areas
(β
=
-0.137,
p
<
0.001),
scores
males
compared
females
-0.64,
0.025),
correlation
between
socioeconomic
status
-0.048,
0.009).
SVM
classifier
achieved
highest
performance
(66.70%)
classification
tasks.
offers
valuable
insights
attitudes
towards
highlighting
symptom
recognition,
awareness,
preventive
practices.
Correlating
underscores
need
tailored
educational
initiatives,
particularly
areas,
addressing
gender
disparities.
efficacy
analytics,
exemplified
by
classifier,
potential
informed
decision-making
public
health
communication
targeted
interventions,
ultimately
empowering
safeguard
their
children's
well-being
amidst
ongoing
pandemic.
International Journal of Intelligent Systems,
Journal Year:
2025,
Volume and Issue:
2025(1)
Published: Jan. 1, 2025
We
investigated
the
fusion
of
Intelligent
Internet
Medical
Things
(IIoMT)
with
depression
management,
aiming
to
autonomously
identify,
monitor,
and
offer
accurate
advice
without
direct
professional
intervention.
Addressing
pivotal
questions
regarding
IIoMT’s
role
in
identification,
its
correlation
stress
anxiety,
impact
machine
learning
(ML)
deep
(DL)
on
depressive
disorders,
challenges
potential
prospects
integrating
management
IIoMT,
this
research
offers
significant
contributions.
It
integrates
artificial
intelligence
(AI)
(IoT)
paradigms
expand
studies,
highlighting
data
science
modeling’s
practical
application
for
intelligent
service
delivery
real‐world
settings,
emphasizing
benefits
within
IoT.
Furthermore,
it
outlines
an
IIoMT
architecture
gathering,
analyzing,
preempting
employing
advanced
analytics
enhance
intelligence.
The
study
also
identifies
current
challenges,
future
trajectories,
solutions
domain,
contributing
scientific
understanding
management.
evaluates
168
closely
related
articles
from
various
databases,
including
Web
Science
(WoS)
Google
Scholar,
after
rejection
repeated
books.
shows
that
there
is
48%
growth
articles,
mainly
focusing
symptoms,
detection,
classification.
Similarly,
most
being
conducted
United
States
America,
trend
increasing
other
countries
around
globe.
These
results
suggest
essence
automated
monitoring,
suggestions
handling
depression.
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery,
Journal Year:
2024,
Volume and Issue:
14(6)
Published: Aug. 4, 2024
Abstract
This
comprehensive
review
article
embarks
on
an
extensive
exploration
of
anxiety
research,
navigating
a
multifaceted
landscape
that
incorporates
various
disciplines,
such
as
molecular
genetics,
hormonal
influences,
implant
science,
regenerative
engineering,
and
real‐time
cardiac
signal
analysis,
all
while
harnessing
the
transformative
potential
medical
intelligence
[medical
+
artificial
(AI)].
By
addressing
fundamental
research
questions,
this
study
investigated
foundations
underlying
disorders,
shedding
light
intricate
interplay
genetic
factors
contributing
to
etiology
progression
anxiety.
Furthermore,
delves
into
emerging
implications
biomaterials,
defibrillators,
state‐of‐the‐art
devices
for
elucidating
their
roles
in
diagnosis,
treatment,
patient
management.
A
pivotal
contribution
is
development
AI‐driven
model
analysis.
innovative
approach
offers
promising
avenue
enhancing
precision
timeliness
diagnosis
monitoring.
Leveraging
machine
learning
AI
techniques
enables
accurate
classification
persons
with
based
data,
thereby
ushering
new
era
personalized
data‐driven
mental
health
care.
Identifying
themes
knowledge
gaps
lays
foundation
future
directions
roadmap
scholars
practitioners
navigate
field.
In
conclusion,
serves
vital
resource,
consolidating
diverse
perspectives
fostering
deeper
understanding
disorders
from
biological,
technological
standpoints,
ultimately
advancing
clinical
practice.
categorized
under:
Application
Areas
>
Health
Care
Science
Technology
Technologies
Classification
International Journal of Intelligent Systems,
Journal Year:
2024,
Volume and Issue:
2024, P. 1 - 15
Published: May 22, 2024
The
misleading
information
during
the
coronavirus
disease
2019
(COVID-19)
pandemic’s
peak
time
is
very
sensitive
and
harmful
in
our
community.
Analyzing
detecting
COVID-19
on
social
media
are
a
crucial
task.
Early
detection
of
helpful
minimizes
risk
psychological
security
which
leads
to
inconvenience
daily
life.
In
this
paper,
deep
ensemble
transfer
learning
framework
with
an
understanding
context
Arabic
text
proposed.
This
inspired
spontaneously
analyze
recognize
about
COVID-19.
ArCOVID-19Vac
dataset
has
been
used
train
test
proposed
model.
A
comprehensive
experimental
study
for
each
scenario
performed.
For
binary
classification
scenario,
records
better
evaluation
results
83.0%,
84.0%,
84.0%
terms
accuracy,
precision,
recall,
F
1-score,
respectively.
second
(three
classes),
overall
performance
recorded
accuracy
82.0%,
precision
80.0%,
recall
1-score
last
ten
classes,
best
67.0%,
58.0%,
59.0%,
addition,
we
have
applied
model
get
64.0%,
66.0%,
65.0%
show
that
through
provides
than
all
state-of-the-art
methods.
Frontiers in Neurology,
Journal Year:
2025,
Volume and Issue:
16
Published: March 26, 2025
Objective
This
study
aims
to
develop
an
unsupervised
automated
method
for
detecting
high-frequency
oscillations
(HFOs)
in
intracranial
electroencephalogram
(iEEG)
signals,
addressing
the
limitations
of
manual
detection
processes.
Method
The
proposed
utilizes
convolutional
variational
autoencoder
(CVAE)
model
conjunction
with
short-term
energy
(STE)
analyze
two-dimensional
time-frequency
representations
iEEG
signals.
Candidate
HFOs
are
identified
using
STE
and
transformed
into
maps
continuous
wavelet
transform
(CWT).
CVAE
is
trained
dimensionality
reduction
feature
reconstruction,
followed
by
clustering
reconstructed
K-means
algorithm
detection.
Results
Evaluation
on
clinical
data
demonstrates
its
superior
performance
compared
traditional
supervised
models.
approach
achieves
accuracy
93.02%,
sensitivity
94.48%,
specificity
92.06%,
highlighting
efficacy
high
accuracy.
Conclusion
developed
this
offers
a
reliable
efficient
solution
overcoming
processes
By
providing
clinicians
clinically
useful
diagnostic
tool,
holds
promise
enhancing
surgical
resection
planning
epilepsy
patients
improving
patient
outcomes.
Work,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 15, 2025
Background
The
COVID-19
pandemic
has
significantly
disrupted
daily
life
and
education,
prompting
institutions
to
adopt
online
teaching.
Objective
This
study
delves
into
the
effectiveness
of
these
methods
during
lockdown
in
Pakistan,
employing
machine
learning
techniques
for
data
analysis.
Methods
A
cross-sectional
survey
was
conducted
with
300
respondents
using
a
semi-structured
questionnaire
assess
perceptions
education.
Artificial
intelligence
analyzed
specificity,
sensitivity,
accuracy,
precision
collected
data.
Results
Among
participants,
42.3%
expressed
satisfaction
learning,
while
49.3%
preferred
Zoom.
Convenience
noted
72%
favoring
classes
between
8
AM
12
PM.
revealed
87.33%
felt
placement
activities
were
negatively
impacted,
85%
reported
effects
on
individual
growth.
Additionally,
90.33%
stated
that
their
routines,
84.66%
citing
adverse
physical
health.
Decision
Tree
classifier
achieved
highest
accuracy
at
86%.
Overall,
preferences
leaned
toward
traditional
in-person
teaching
despite
methods.
Conclusions
highlights
significant
challenges
transitioning
emphasizing
disruptions
routines
overall
well-being.
Notably,
age
gender
did
not
influence
growth
or
Finally,
collaborative
efforts
among
educators,
policymakers,
stakeholders
are
crucial
ensuring
equitable
access
quality
education
future
crises.
International Journal of Intelligent Systems,
Journal Year:
2025,
Volume and Issue:
2025(1)
Published: Jan. 1, 2025
The
Internet
of
Things
(IoT)
has
become
a
transformative
force
across
various
sectors,
including
healthcare,
offering
new
opportunities
for
automation
and
enhanced
service
delivery.
evolving
architecture
the
IoT
presents
significant
challenges
in
establishing
comprehensive
cyber‐physical
framework.
This
paper
reviews
recent
advancements
IoT‐driven
healthcare
automation,
focussing
on
integrating
technologies
such
as
cloud
computing,
augmented
reality
wearable
devices.
work
examines
network
architectures
platforms
that
support
applications
while
addressing
critical
security
privacy
issues,
specific
threat
models,
attack
classifications
prerequisites
relevant
to
sector.
study
highlights
how
emerging
like
distributed
intelligence,
big
data
analytics
devices
are
incorporated
into
improve
patient
care
streamline
medical
operations.
findings
reveal
potential
transform
practices,
particularly
in‐patient
monitoring,
clinical
decision‐making.
However,
concerns
continue
be
substantial
barrier.
also
explores
implications
global
ehealth
strategies
their
influence
sustainable
economic
community
growth.
It
proposes
an
innovative
cooperative
model
mitigate
risks
IoT‐enabled
systems.
Finally,
it
identifies
key
unresolved
future
research
IoT‐based
healthcare.
Annals of Animal Science,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 7, 2024
Abstract
Background
Lumpy
skin
disease
(LSD)
has
been
a
significant
concern
in
veterinary
medicine
since
its
discovery.
Despite
decades
of
research,
understanding
the
full
spectrum
this
remains
challenge.
To
address
gap,
comprehensive
analysis
existing
body
knowledge
on
LSD
is
essential.
Bibliometric
offers
systematic
approach
towards
mapping
research
landscape,
identifying
key
contributors,
and
uncovering
emerging
trends
research.
Objective
This
study
aims
to
conduct
thorough
bibliometric
spanning
from
1947
till
present
date
order
map
domain
LSD.
The
objective
gain
insights
into
global
trends,
identify
influential
explore
collaboration
networks,
predict
future
outlook
Method
Data
extracted
Scopus
database
was
used
perform
analysis.
341
relevant
documents
were
selected
for
indicators,
including
publication
numbers,
citation
counts,
h-index,
utilized
assess
contributions
nations,
organizations,
authors,
source
titles.
Additionally,
cooperation
networks
between
countries,
authors
visualized
using
VOSviewer
tool.
Results
revealed
increase
output
LSD,
with
notable
growth
rate
19.26%.
Since
discovery
Zambia
1929,
grown
steadily,
an
average
annual
5.21%.
University
Pretoria
Federal
Centre
Animal
Health
emerged
as
most
active
institutions
organizations
Journal
Virology
identified
cited
journal,
reflecting
impact
field,
strong
international
observed
United
Kingdom
South
Africa.
Conclusion
provides
valuable
landscape
highlighting
networks.
By
reviewing
enhances
our
serves
foundation
endeavours.
findings
will
aid
researchers
navigating
vast
literature
ultimately
contributing
advancements
management
strategies.
