Advances in media, entertainment and the arts (AMEA) book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 265 - 290
Published: Dec. 13, 2024
Sophisticated
recommendation
systems
are
crucial
in
the
dynamic
digital
content
consumption
environment
to
improve
user
engagement
and
discoverability.
This
work
provides
an
in-depth
analysis
of
different
models
such
as
User-based
Collaborative
Filtering,
NMF-based
Content
a
Hybrid
Model.
It
uses
detailed
dataset
interactions
with
streaming
platform
evaluate
their
performance.We
utilized
Filtering
utilize
similarities
among
users
for
recommendations,
break
down
user-item
interaction
matrix
reveal
hidden
features,
Model
that
combines
advantages
both
methods
offer
more
tailored
precise
recommendations.
The
results
demonstrate
intricate
strengths
weaknesss
each
model,
displaying
favorable
combination
customisation
accuracy.
findings
from
this
provide
valuable
contributions
discussion
on
also
have
practical
implications
Advances in educational technologies and instructional design book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 293 - 316
Published: June 28, 2024
Using
a
large
dataset
that
includes
students'
grades,
demographic
information,
and
other
educational
variables
from
three
American
high
schools,
this
research
work
investigates
the
predictive
modeling
of
mathematical
performance.
Gender,
race/ethnicity,
parental
education,
lunch
subsidy
status,
standardized
test
results
(math,
reading,
writing),
course
enrollment
in
preparation
are
all
part
dataset.
The
purpose
study
is
to
examine
relationship
between
socioeconomic
status
their
achievement
discover
important
predictors
using
sophisticated
machine
learning
algorithms
such
as
ensemble
methods,
decision
trees,
linear
regression.
A
more
complex
picture
factors
lead
can
be
gained
study,
which
uncovers
illuminating
relationships
across
variables,
interventions,
academic
results.
highlight
promise
analytics
for
developing
individualized
plans
improve
experiences.
Educators,
legislators,
future
researchers
benefit
data-driven
methods
planning
decision-making,
highlighted
paper's
examination
findings'
ramifications.
Advances in healthcare information systems and administration book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 68 - 86
Published: June 30, 2024
In
order
to
improve
obstetric
risk
categorization,
this
study
employs
a
thorough
machine
learning
approach
that
makes
use
of
cardiotocogram
data.
The
goal
is
find
patterns
and
correlations
are
relevant
for
forecasting
fetal
well-being
by
evaluating
dataset
includes
several
health
indicators,
such
as
baseline
heart
rates,
uterine
contractions,
movements,
decelerations.
To
lay
the
groundwork
feature
selection
model
construction,
authors
conducted
exploratory
data
analysis,
which
yielded
important
insights
into
distributions
these
clinical
variables.
provide
predictive
foetal
status
classification
state-of-the-art
methods.
With
approach,
they
can
better
comprehend
distress
signals
make
more
informed
decisions,
in
turn
help
reduce
rates
neonatal
perinatal
morbidity
mortality.
revolutionary
effect
on
improving
healthcare
efficiency
patient
outcomes
highlighted
results,
stress
need
incorporating
data-driven
methods
care.
Advances in medical technologies and clinical practice book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 235 - 256
Published: Sept. 14, 2024
Adverse
Drug
Reaction
(ADR)
detection
and
study
are
important
for
pharmacovigilance,
which
is
the
safety
of
medicines.
Underreporting
can
get
in
way
traditional
ADR
ratings.
Natural
Language
Processing
(NLP)
used
this
to
look
ADRs
that
haven't
been
mentioned
on
social
media
online
health
forums.
The
goal
see
if
listening
be
addition
pharmacovigilance.
Many
types
NLP
techniques,
such
as
sentiment
analysis,
topic
modeling,
named
entity
identification,
were
gather
a
big
set
user-generated
content
from
different
websites.
Names
drugs
bad
reactions
looked
for.
Our
research
shows
pharmacovigilance
databases
missed
lot
ADRs.
found
through
compared
those
medical
books
databases.
This
fill
gaps
current
reporting
systems.
It
also
looks
at
how
reliable
data
is,
hard
it
make
filtering
algorithms,
users
should
protect
their
privacy
ethically
use
data.
Advances in computational intelligence and robotics book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 47 - 68
Published: May 31, 2024
This
chapter
explores
the
transformative
impact
of
artificial
intelligence
(AI)
and
ChatGPT
techniques
in
facilitating
real-time
data-driven
decision-making
within
government
sectors.
The
adoption
AI
advanced
language
models
like
represents
a
significant
shift
how
governments
process
vast
amounts
information
to
make
timely
informed
decisions.
research
focuses
on
application
these
technologies
enhancing
governmental
operations,
policy-making,
public
services.
Case
studies
are
presented
illustrate
practical
applications
various
functions
such
as
health
management,
accelerating
teaching
learning,
urban
planning,
environmental
monitoring,
emergency
response.
Additionally,
addresses
ethical
considerations
need
for
robust
data
governance
frameworks
ensure
responsible
use
government.
includes
discussion
privacy,
security,
potential
risks
bias
models.
Advances in hospitality, tourism and the services industry (AHTSI) book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 407 - 422
Published: Nov. 27, 2024
This
research
conducts
a
detailed
study
on
the
timing
of
tourism
arrivals
in
different
countries
using
an
advanced
multi-country
time
series
method.
Our
attempts
to
reveal
patterns
and
trends
visitor
nations
over
last
twenty
years,
considering
dynamic
nature
industry
its
significant
impact
national
economies.
We
have
used
sophisticated
clustering
methods
classify
counties
into
three
separate
clusters
based
pattern
throughout
specified
timeframe.We
use
several
data
processing
techniques
such
as
normalization,
detrending,
seasonality
correction
make
sure
findings
are
comparable
reliable.
The
study's
results
provide
important
information
for
policymakers,
tourist
marketers,
stakeholders
industry.
can
be
develop
strategic
plans,
allocate
resources
effectively,
create
focused
promotional
campaigns
support
sustainable
growth.
Advances in media, entertainment and the arts (AMEA) book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 265 - 290
Published: Dec. 13, 2024
Sophisticated
recommendation
systems
are
crucial
in
the
dynamic
digital
content
consumption
environment
to
improve
user
engagement
and
discoverability.
This
work
provides
an
in-depth
analysis
of
different
models
such
as
User-based
Collaborative
Filtering,
NMF-based
Content
a
Hybrid
Model.
It
uses
detailed
dataset
interactions
with
streaming
platform
evaluate
their
performance.We
utilized
Filtering
utilize
similarities
among
users
for
recommendations,
break
down
user-item
interaction
matrix
reveal
hidden
features,
Model
that
combines
advantages
both
methods
offer
more
tailored
precise
recommendations.
The
results
demonstrate
intricate
strengths
weaknesss
each
model,
displaying
favorable
combination
customisation
accuracy.
findings
from
this
provide
valuable
contributions
discussion
on
also
have
practical
implications
