International Journal of Information Management,
Journal Year:
2024,
Volume and Issue:
77, P. 102781 - 102781
Published: April 3, 2024
Artificial
intelligence
(AI)
is
playing
a
leading
role
in
the
digital
transformation
of
enterprises,
particularly
manufacturing
industry
where
it
has
been
responsible
for
profound
key
business
and
production
operations.
Despite
accelerated
growth
AI
technologies,
knowledge
implementation
by
small
medium-sized
enterprises
(SMEs)
remains
underexplored.
Thus,
this
study
seeks
to
examine
how
SMEs
orchestrate
resources
implementation.
Building
on
resource
orchestration
(RO)
theory
recent
work
implementation,
we
investigate
multiple
case
studies
involving
Sweden
operating
packaging,
plastic,
metal
sectors.
Our
findings
indicate
that
structure
portfolio
based
acquiring
accumulating
resources.
are
bundled
into
learning
governance
capabilities
leverage
configurations
Through
dynamic
process
orchestration,
effectively
mobilising
coordinating
processes,
empowering
skilled
people.
This
research
contributes
existing
practice
academic
literature
highlighting
drive
an
organisation's
whilst
creating
competitive
advantage.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: March 21, 2024
Abstract
Industrial
advancements
and
utilization
of
large
amount
fossil
fuels,
vehicle
pollution,
other
calamities
increases
the
Air
Quality
Index
(AQI)
major
cities
in
a
drastic
manner.
Major
AQI
analysis
is
essential
so
that
government
can
take
proper
preventive,
proactive
measures
to
reduce
air
pollution.
This
research
incorporates
artificial
intelligence
prediction
based
on
pollution
data.
An
optimized
machine
learning
model
which
combines
Grey
Wolf
Optimization
(GWO)
with
Decision
Tree
(DT)
algorithm
for
accurate
India.
quality
data
available
Kaggle
repository
used
experimentation,
like
Delhi,
Hyderabad,
Kolkata,
Bangalore,
Visakhapatnam,
Chennai
are
considered
analysis.
The
proposed
performance
experimentally
verified
through
metrics
R-Square,
RMSE,
MSE,
MAE,
accuracy.
Existing
models,
k-nearest
Neighbor,
Random
Forest
regressor,
Support
vector
compared
model.
attains
better
traditional
algorithms
maximum
accuracy
88.98%
New
Delhi
city,
91.49%
Bangalore
94.48%
97.66%
95.22%
97.68%
Visakhapatnam
city.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(12), P. 4959 - 4959
Published: June 10, 2024
This
paper
presents
an
in-depth
exploration
of
the
application
Artificial
Intelligence
(AI)
in
enhancing
resilience
microgrids.
It
begins
with
overview
impact
natural
events
on
power
systems
and
provides
data
insights
related
to
outages
blackouts
caused
by
Estonia,
setting
context
for
need
resilient
systems.
Then,
delves
into
concept
role
microgrids
maintaining
stability.
The
reviews
various
AI
techniques
methods,
their
further
investigates
how
can
be
leveraged
improve
microgrids,
particularly
during
different
phases
event
occurrence
time
(pre-event,
event,
post-event).
A
comparative
analysis
performance
models
is
presented,
highlighting
ability
maintain
stability
ensure
a
reliable
supply.
comprehensive
review
contributes
significantly
existing
body
knowledge
sets
stage
future
research
this
field.
concludes
discussion
work
directions,
emphasizing
potential
revolutionizing
system
monitoring
control.
Internet of Things,
Journal Year:
2024,
Volume and Issue:
26, P. 101156 - 101156
Published: March 12, 2024
Artificial
intelligence
(AI)
positively
remodels
industrial
processes,
notably
inventory
management
(IM),
from
planning,
scheduling,
and
optimization
to
logistics.
Intelligent
technologies
such
as
AI
have
enabled
innovative
processes
in
the
production
line
of
manufacturing
execution
systems
(MES),
particularly
predicting
IM.
This
study
proposes
a
Multi-MLP
model
with
LightGBM
feature
selection
technique
for
MES
IM
prediction
enable
high
accuracy,
minimal
computation
cost,
low
error,
minimum
time
cost.
The
proposed
is
evaluated
using
publicly
available
Product
Backorder
datasets
prove
its
reliability.
Investigating
varying
techniques
results
identifying
appropriate
data
features
relevant
building
an
AI-based
solution
MES.
experiment
demonstrate
efficient
decision-making
system
error
MAE
0.2331,
MSE
0.1225,
RMSE
0.3504.
