Flexible Services and Manufacturing Journal,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 30, 2024
Abstract
Industry
5.0
(I5.0)
marks
a
transformative
shift
toward
integrating
advanced
technologies
with
human-centric
design
to
foster
innovation,
resilient
manufacturing,
and
sustainability.
This
study
aims
examine
the
evolution
collaborative
dynamics
of
I5.0
research
through
bibliometric
analysis
942
journal
articles
from
Scopus
database.
Our
findings
reveal
significant
increase
in
research,
particularly
post-2020,
yet
highlight
fragmented
collaboration
networks
noticeable
gap
between
institutions
developed
developing
countries.
Key
thematic
areas
identified
include
human-robot
collaboration,
data
management
security,
AI-driven
sustainable
practices.
These
insights
suggest
that
more
integrated
approach
is
essential
for
advancing
I5.0,
calling
strengthened
global
collaborations
balanced
emphasis
on
both
technological
elements
fully
realize
its
potential
driving
industrial
provides
first
comprehensive
offering
valuable
researchers
practitioners.
Advances in finance, accounting, and economics book series,
Год журнала:
2025,
Номер
unknown, С. 199 - 224
Опубликована: Фев. 7, 2025
This
chapter
examines
how
automating
business
processes
and
changing
management
practices
can
be
achieved
through
the
use
of
artificial
intelligence
(AI).
It
draws
attention
to
increasing
AI
across
a
range
industries
emphasizes
it
improve
productivity
judgment
overall
performance.
The
looks
at
major
managerial
tasks
that
supports
like
employee
engagement
performance
offers
case
studies
real-world
application
insights.
Businesses
without
sacrificing
data
security
or
fairness
by
taking
ethical
issues
privacy
concerns
significance
responsible
deployment
into
account.
potential
for
ongoing
innovation
difficulties
businesses
may
encounter
in
putting
these
technologies
practice
are
highlighted
this
exploration
future
trends
AI-powered
automation.
looking
implement
their
best
practical
lessons
gleaned
from
industry
as
guide.
Informatics and Automation,
Год журнала:
2025,
Номер
24(2), С. 583 - 603
Опубликована: Апрель 1, 2025
People
re-identification
(ReID)
plays
a
pivotal
role
in
modern
surveillance,
enabling
continuous
tracking
of
individuals
across
various
CCTV
cameras
and
enhancing
the
effectiveness
public
security
systems.
However,
ReID
real-world
footage
presents
challenges,
including
changes
camera
angles,
variations
lighting,
partial
occlusions,
similar
appearances
among
individuals.
In
this
paper,
we
propose
robust
deep
learning
framework
that
leverages
convolutional
neural
networks
(CNNs)
with
customized
triplet
loss
function
to
overcome
these
obstacles
improve
accuracy.
The
is
designed
generate
unique
feature
embeddings
for
individuals,
allowing
precise
differentiation
even
under
complex
environmental
conditions.
To
validate
our
approach,
perform
extensive
evaluations
on
benchmark
datasets,
achieving
state-of-the-art
results
terms
both
accuracy
processing
speed.
Our
model's
performance
assessed
using
key
metrics,
Cumulative
Matching
Characteristic
(CMC)
mean
Average
Precision
(mAP),
demonstrating
its
robustness
diverse
surveillance
scenarios.
Compared
existing
methods,
approach
consistently
outperforms
scalability,
making
it
suitable
integration
into
large-scale
Furthermore,
discuss
practical
considerations
deploying
AI-based
models
infrastructure,
system
real-time
capabilities,
privacy
concerns.
By
advancing
techniques
re-identifying
people,
work
not
only
contributes
field
intelligent
but
also
provides
safety
applications
through
automated
reliable
capabilities.
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 313 - 332
Опубликована: Апрель 4, 2025
Computing
in
the
cloud
has
emerged
as
an
essential
element
digital
transformation
of
businesses,
it
provides
businesses
with
increased
scalability,
flexibility,
and
cost-effectiveness.
The
strategic
role
that
clouds
technology
plays
enhancing
operational
efficiency
propelling
research
development.
In
particular,
highlights
significance
data
integrity,
security,
compliance
within
multi-cloud
environments,
which
are
characterized
by
complexity
arises
from
management
multiple
platforms.
order
for
organizations
to
successfully
adopt
computing,
they
need
develop
migration
strategies
tailored
their
specific
needs,
carry
out
comprehensive
readiness
assessments,
put
place
robust
change
procedures.
Advances in hospitality, tourism and the services industry (AHTSI) book series,
Год журнала:
2024,
Номер
unknown, С. 17 - 34
Опубликована: Июль 26, 2024
The
advent
of
artificial
intelligence
(AI)
has
revolutionized
service
marketing,
offering
unprecedented
opportunities
to
enhance
customer
engagement
and
experience.
This
chapter
delves
into
the
transformative
impact
AI-driven
innovations
on
marketing
strategies,
emphasizing
how
AI
technologies
such
as
machine
learning,
natural
language
processing,
predictive
analytics
are
redefining
interactions.
By
automating
personalized
communications,
predicting
needs,
providing
real-time
solutions,
is
enabling
businesses
deliver
more
efficient,
tailored,
satisfying
experiences.
explores
various
applications,
from
chatbots
virtual
assistants
advanced
data
analysis,
illustrating
these
tools
being
integrated
foster
deeper
relationships
drive
business
growth.
Through
case
studies
empirical
data,
demonstrates
practical
implications
in
enhancing
delivery,
improving
satisfaction,
creating
a
competitive
edge
market.
BACKGROUND
The
COVID-19
pandemic
has
exposed
the
vulnerabilities
of
global
supply
chains
(SC),
particularly
within
healthcare
sector,
underscoring
need
for
advanced
methods
to
enhance
SC
resilience
and
sustainability.
Pandemics,
such
as
Influenza,
pose
considerable
risks
chain
(HSC)
performance,
demanding
robust
analytical
tools
optimize
system
efficiency
under
uncertain
conditions.
OBJECTIVE
In
this
paper,
we
map
current
literature
synthesize
insights
on
role
leadership
in
driving
Artificial
Intelligence
(AI)-driven
approaches
enhancing
HSC
organizations.
This
systematic
review
aims
HSC-resilience
(HSCR)
apply
a
novel
network
range
directional
measure
model
evaluate
sustainability
response
pandemic.
METHODS
followed
PRISMA
guidelines,
encompassing
multiple
databases,
including
Business
Source
Premier,
CINAHL,
ACM
Digital
Library,
MEDLINE,
PsycINFO,
Web
Science,
PubMed,
ScienceDirect.
targeted
articles
published
from
2016
2024,
focusing
empirical
studies.
A
predetermined
search
strategy
used
keywords
resilience,
artificial
intelligence,
healthcare,
related
terms.
analysis
involved
an
inductive,
thematic
approach
qualitatively
evidence.
screening
data
extraction
processes
were
independently
carried
out
by
two
reviewers,
with
Cohen's
kappa
assess
interrater
agreement.
Data
synthesis
was
accomplished
through
narrative
approach.
RESULTS
comprehensive
case
study
demonstrates
practical
application
model,
revealing
its
capability
diverse
findings
highlight
how
decision-making
unit
varies
changing
circumstances,
showcasing
model’s
robustness
evaluating
performance
during
disruptions.
final
number
studies
included
39.
These
clinical
units
quantitative
qualitative
decision
support
models
16/39
(41%)
25/39
(59%),
respectively.
earliest
article
2018;
most
recent
2022.
CONCLUSIONS
is
one
first
compare
AI
conventional
human
real-time
gathering
AI-driven
strategies
strengthen
HSC.
While
proves
effective
assessing
sustainability,
key
limitation
lies
implementation
methodologies
Future
research
should
focus
real-world
deployment
these
face
potential
