Talent
is
the
first
resource,
development
of
enterprise
to
retain
key
talent
essential,
main
research
based
on
machine
learning
and
ontological
reasoning,
human
resources
analysis
management
risk
prediction
early
warning
methods,
all,
according
specific
situation
target
case,
through
calculation
similarity
concept
name
attribute
assessment
source
case
in
library,
matching
knowledge-based
employees
company's
for
research.Then,
evaluation
results,
we
can
find
out
most
suitable
job
matches
problems
situations.This
a
solution
cases
criteria
companies
evaluate
candidates.Second,
have
successfully
developed
implemented
model
that
applies
study
HR
management.The
optimized
with
cross-validation
function,
convergence
training
accelerated
by
regularization
Newton's
iterative
method.Finally,
our
achieved
82%
yield.Ontological
reasoning
are
promising
resource
warning,
which
proved
high
accuracy
rate
verified
examples.Finally,
analyze
proposed
results
HRM
contribute
improvement
control
suggest
measures
possible
risks.
Water,
Journal Year:
2024,
Volume and Issue:
16(2), P. 208 - 208
Published: Jan. 6, 2024
Predicting
monthly
streamflow
is
essential
for
hydrological
analysis
and
water
resource
management.
Recent
advancements
in
deep
learning,
particularly
long
short-term
memory
(LSTM)
recurrent
neural
networks
(RNN),
exhibit
extraordinary
efficacy
forecasting.
This
study
employs
RNN
LSTM
to
construct
data-driven
forecasting
models.
Sensitivity
analysis,
utilizing
the
of
variance
(ANOVA)
method,
also
crucial
model
refinement
identification
critical
variables.
covers
data
from
1979
2014,
employing
five
distinct
structures
ascertain
most
optimal
configuration.
Application
models
Zarrine
River
basin
northwest
Iran,
a
major
sub-basin
Lake
Urmia,
demonstrates
superior
accuracy
algorithm
over
LSTM.
At
outlet
basin,
quantitative
evaluations
demonstrate
that
outperforms
across
all
structures.
The
S3
model,
characterized
by
its
inclusion
input
variable
values
four-month
delay,
exhibits
notably
exceptional
performance
this
aspect.
measures
applicable
particular
context
were
RMSE
(22.8),
R2
(0.84),
NSE
(0.8).
highlights
River’s
substantial
impact
on
variations
Urmia’s
level.
Furthermore,
ANOVA
method
discerning
relevance
factors.
underscores
key
role
station
streamflow,
upstream
maximum
temperature
influencing
model’s
output.
Notably,
surpassing
traditional
artificial
network
(ANN)
models,
excels
accurately
mimicking
rainfall–runoff
processes.
emphasizes
potential
filter
redundant
information,
distinguishing
them
as
valuable
tools
Buildings,
Journal Year:
2024,
Volume and Issue:
14(11), P. 3367 - 3367
Published: Oct. 24, 2024
Post-disaster
reconstruction
of
the
built
environment
represents
a
key
global
challenge
that
looks
set
to
remain
for
foreseeable
future,
but
it
also
offers
significant
implications
future
sustainability
and
resilience
environment.
The
purpose
this
research
is
explore
current
applications
advanced
digital/Industry
4.0
technologies
in
post-disaster
(PDR)
process
with
view
improving
its
effectiveness
efficiency
extant
literature
from
Scopus
database
on
identified
described.
In
novel
review
approach,
small
language
models
are
used
classification
filtering
technology-related
articles.
A
qualitative
content
analysis
then
carried
out
understand
extent
which
Industry
applied
practice,
mapping
their
specific
phases
PDR
identifying
dominant
trends
technology
deployment.
study
reveals
rapidly
evolving
landscape
technological
innovation
transformative
potential
enhancing
efficiency,
effectiveness,
rebuilding
efforts,
including
GIS,
remote
sensing,
AI,
BIM.
Key
include
increasing
automation
data-driven
decision-making,
integration
multiple
4.0/digital
technologies,
growing
emphasis
incorporating
community
needs
local
knowledge
into
plans.
highlights
need
address
challenges,
such
as
developing
interoperable
platforms,
addressing
ethical
using
AI
big
data,
exploring
contribution
sustainable
practices.
Sustainability,
Journal Year:
2022,
Volume and Issue:
14(11), P. 6624 - 6624
Published: May 28, 2022
Although
many
meteorological
prediction
models
have
been
developed
recently,
their
accuracy
is
still
unreliable.
Post-processing
a
task
for
improving
predictions.
This
study
proposes
post-processing
method
the
Climate
Forecast
System
Version
2
(CFSV2)
model.
The
applicability
of
proposed
shown
in
Iran
observation
data
from
1982
to
2017.
designs
software
perform
organizations
automatically.
From
another
point
view,
this
presents
decision
support
system
(DSS)
controlling
precipitation-based
natural
side
effects
such
as
flood
disasters
or
drought
phenomena.
It
goes
without
saying
that
DSS
model
can
meet
sustainable
development
goals
(SDGs)
with
regards
grantee
human
health
and
environmental
protection
issues.
present
study,
first
time,
implemented
platform
based
on
graphical
user
interface
due
precipitation
application
machine
learning
computations.
research
an
academic
idea
into
industrial
tool.
final
finding
paper
introduce
set
efficient
computations
where
random
forest
(RF)
algorithm
has
great
level
more
than
0.87
correlation
coefficient
compared
other
methods.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(6), P. 2644 - 2644
Published: March 17, 2025
Disaster
management
minimizes
potential
harm
and
protects
populations
across
four
phases:
preparedness,
mitigation,
response,
recovery.
Diverse
scientific
approaches
could
be
applied
at
each
phase,
among
which
Multi-Criteria
Decision-Making
(MCDM)
methods
are
widely
recognized
utilized.
Their
integration
provides
a
systematic
framework
for
prioritizing
disaster-related
criteria,
optimizing
resource
use,
minimizing
environmental
impact,
ultimately
enhancing
community
resilience.
This
study
conducts
bibliometric
analysis
to
identify
pioneering
researchers,
leading
institutions,
contributing
countries,
interaction
levels
working
on
MCDM
in
disaster
emergency
transportation,
as
well
reveal
key
trends.
365
Web
of
Science
Scopus
publications
(2000–2024)
were
analyzed
using
the
Bibliometrix
tool
R.
As
significant
outcome,
three
important
clusters
emerged:
Planning
Logistics,
Risk
Resilience,
Crisis
Response
Decision
Support.
The
interplay
between
these
methodologies
shaping
them
was
highlighted,
alongside
insights
from
most
recent
studies.
serve
roadmap
future
research,
guiding
efforts
address
gaps
such
real-time
applications,
multi-hazard
integration,
scalability.
It
contributes
limited
body
research
laying
groundwork
upcoming
studies
that
enhance
resilience
promote
sustainable
development.
Benchmarking An International Journal,
Journal Year:
2023,
Volume and Issue:
31(6), P. 2090 - 2128
Published: June 12, 2023
Purpose
This
paper
aims
to
examine
and
compare
extant
literature
on
the
application
of
multi-criteria
decision-making
(MCDM)
techniques
in
humanitarian
operations
(HOs)
supply
chains
(HSCs).
It
identifies
status
existing
research
field
suggests
a
roadmap
for
academicians
undertake
further
HOs
HSCs
using
MCDM
techniques.
Design/methodology/approach
The
systematically
reviews
applications
HO
HSC
domains
from
2011
2022,
as
gained
traction
post-2004
Indian
Ocean
Tsunami
phenomena.
In
first
step,
an
exhaustive
search
journal
articles
is
conducted
48
keyword
searches.
To
ensure
quality,
only
those
published
journals
featuring
quartile
Scimago
Journal
Ranking
were
selected.
A
total
103
peer-reviewed
selected
review
then
segregated
into
different
categories
analysis.
Findings
highlights
insufficient
high-quality
that
utilizes
methods.
proposes
scholars
enhance
outcomes
by
advocating
adopting
mixed
analysis
various
studies
revealed
notable
absence
contextual
reference.
mind
map
specific
has
been
developed
assist
future
endeavors.
resource
can
guide
researchers
determining
appropriate
framework
their
studies.
Practical
implications
will
help
practitioners
understand
carried
out
field.
aspiring
identify
gap
work
directions.
Originality/value
best
authors’
knowledge,
this
applying
HSCs.
summarises
current
