IntechOpen eBooks,
Journal Year:
2024,
Volume and Issue:
unknown
Published: May 13, 2024
This
chapter
investigates
the
evolving
landscape
of
cybersecurity
and
risk
management,
highlighting
their
newfound
prominence
in
corporate
governance.
The
narrative
emphasizes
integral
role
boards
executives
orchestrating
robust
governance,
recognizing
it
as
a
strategic
necessity
rather
than
mere
technical
aspect.
Legal
regulatory
considerations,
notably
General
Data
Protection
Regulation
(GDPR)
California
Consumer
Privacy
Act
(CCPA),
are
explored
critical
dimensions
influencing
integration
into
governance
frameworks
is
dissected,
underscoring
importance
aligning
strategies
with
enterprise
management.
further
explores
dynamic
landscape,
detailing
surge
sophisticated
threats
such
ransomware,
phishing,
state-sponsored
cyber
activities.
It
concludes
by
outlining
best
practices,
including
proactive
assessments,
fostering
security
awareness,
continuous
evolution
future
outlook
encompasses
emerging
technologies,
international
collaboration,
board-level
decision-making,
presenting
holistic
vision
for
resilient
digital
age.
Electronics,
Journal Year:
2025,
Volume and Issue:
14(8), P. 1641 - 1641
Published: April 18, 2025
With
the
rapid
expansion
of
Electric
Sport
Utility
Vehicle
(ESUV)
market,
capturing
consumer
aesthetic
preferences
and
emotional
needs
through
front-end
styling
has
become
a
key
issue
in
automotive
design.
However,
traditional
Kansei
Engineering
(KE)
approaches
suffer
from
limited
timeliness,
subjectivity,
low
predictive
accuracy
when
extracting
affective
vocabulary
modeling
nonlinear
relationship
between
product
form
imagery.
To
address
these
challenges,
this
study
proposes
an
improved
KE-based
ESUV
framework
that
integrates
data
mining,
machine
learning,
generative
AI.
First,
real
reviews
samples
are
collected
via
Python-based
web
scraping.
Next,
Biterm
Topic
Model
(BTM)
Analytic
Hierarchy
Process
(AHP)
used
to
extract
representative
vocabulary.
Subsequently,
Back
Propagation
Neural
Network
(BPNN)
Support
Vector
Regression
(SVR)
models
constructed
optimized
using
Seagull
Optimization
Algorithm
(SOA)
Particle
Swarm
(PSO).
Experimental
results
show
SOA-BPNN
achieves
superior
accuracy.
Finally,
Stable
Diffusion
is
applied
generate
design
schemes,
optimal
model
employed
evaluate
their
The
proposed
offers
systematic
data-driven
approach
for
predicting
responses
conceptual
stage,
effectively
addressing
limitations
conventional
experience-based
Thus,
both
methodological
innovation
practical
guidance
integrating
into
