Prioritizing Factors Influencing Global Network Readiness Index with Bayesian Belief Networks
Journal of Open Innovation Technology Market and Complexity,
Год журнала:
2025,
Номер
unknown, С. 100522 - 100522
Опубликована: Март 1, 2025
Язык: Английский
Structural equation modeling and Fuzzy set theory: Advancing risk assessment in oil and gas construction projects
Environmental Impact Assessment Review,
Год журнала:
2024,
Номер
109, С. 107622 - 107622
Опубликована: Авг. 20, 2024
Язык: Английский
Exploring the nexus of stakeholder management, project performance and stakeholder satisfaction in Malaysian residential building projects: a PLS-SEM approach
Engineering Construction & Architectural Management,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 11, 2024
Purpose
In
this
research,
the
authors
distributed
a
survey
to
156
residential
construction
developers
and
468
buyers
assess
level
of
perceived
agreement
on
key
indicators
for
measuring
stakeholder
management,
project
performance
satisfaction.
Following
this,
partial
least
squares
structural
equation
modelling
(PLS-SEM)
model
was
developed
quantitatively
analyse
direct
impacts
management
both
satisfaction,
mediating
role
satisfaction
in
enhancing
performance.
Design/methodology/approach
This
paper
seeks
investigate
effects
within
projects,
also
examine
by
surveying
buildings’
Malaysia.
Findings
research
found
that
effective
directly
improves
Malaysian
projects.
It
further
identified
significantly
enhances
performance,
serving
as
critical
mediator
relationship
between
Practical
implications
study
understanding
industry,
offering
strategic
insights
emphasise
importance
stakeholder-centric
practices
improving
outcomes,
ensuring
better
collaboration
fostering
enhanced
Integrating
these
with
digital
technologies
like
building
information
can
lead
clearer
communication,
more
informed
engagement,
and,
ultimately,
efficiency
Originality/value
offers
empirical
evidence
Malaysia’s
providing
novel
into
approaches
contribute
improved
outcomes.
Язык: Английский
Prediction of telecommunications market behavior based on LSTM models
Journal of Infrastructure Policy and Development,
Год журнала:
2024,
Номер
8(15), С. 8226 - 8226
Опубликована: Дек. 16, 2024
The
telecommunications
services
market
faces
essential
challenges
in
an
increasingly
flexible
and
customer-adaptable
environment.
Research
has
highlighted
that
the
monopolization
of
spectrum
by
one
operator
reduces
competition
negatively
impacts
users
general
dynamics
sector.
This
article
aims
to
present
a
proposal
predict
number
users,
level
traffic,
operators’
income
using
artificial
intelligence.
Deep
Learning
(DL)
is
implemented
through
Long-Short
Term
Memory
(LSTM)
as
prediction
technique.
database
used
corresponds
revenues,
traffic
15
network
operators
obtained
from
Communications
Regulation
Commission
Republic
Colombia.
ability
LSTMs
handle
temporal
sequences,
long-term
dependencies,
adaptability
changes,
complex
data
management
makes
them
excellent
strategy
for
predicting
forecasting
telecom
market.
Various
works
involve
LSTM
telecommunications.
However,
many
questions
remain
prediction.
strategies
can
be
proposed,
continued
research
should
focus
on
providing
cognitive
engines
address
further
challenges.
MATLAB
design
subsequent
implementation.
low
Root
Mean
Squared
Error
(RMSE)
values
acceptable
levels
Absolute
Percentage
(MAPE),
especially
environment
characterized
high
variability
support
conclusion
model
exhibits
performance
terms
precision
process
both
open-loop
closed-loop.
Язык: Английский
Estimation of the LINDA index prediction based on deep learning models
Journal of Infrastructure Policy and Development,
Год журнала:
2024,
Номер
8(15), С. 9003 - 9003
Опубликована: Дек. 16, 2024
Recognizing
the
importance
of
competition
analysis
in
telecommunications
markets
is
essential
to
improve
conditions
for
users
and
companies.
Several
indices
literature
assess
these
markets,
mainly
through
company
concentration.
Artificial
Intelligence
(AI)
emerges
as
an
effective
solution
process
large
volumes
data
manually
detect
patterns
that
are
difficult
identify.
This
article
presents
AI
model
based
on
LINDA
indicator
predict
whether
oligopolies
exist.
The
objective
offer
a
valuable
tool
analysts
professionals
sector.
uses
traffic
produced,
reported
revenues,
number
input
variables.
As
output
parameters
model,
index
obtained
according
information
by
operators,
prediction
using
Long-Short
Term
Memory
(LSTM)
variables,
finally,
LSTM
model.
Mean
Absolute
Percentage
Error
(MAPE)
levels
indicate
proposed
strategy
can
be
forecasting
dynamic
fluctuations
communications
market.
Язык: Английский