International Journal of Computer Applications Technology and Research,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 7, 2024
The
incorporation
of
artificial
intelligence
(AI)
and
deep
learning
(DL)
into
IT
infrastructure
is
revolutionizing
corporate
operations
competitiveness
in
this
present-day
dynamic
digital
economy.These
developments
are
crucial
to
the
transformation
businesses
since
they
provide
relevant
capabilities
data
processing,
automation,
decision-making.This
study
examines
impact
AI
DL
on
infrastructure,
emphasizing
how
these
technologies
can
foster
business
transformation.The
findings
review
indicate
that
by
streamlining
workflows,
eliminating
errors,
automating
repetitive
tasks,
increase
productivity
drive
innovation.Through
predictive
analytics
modified
services,
also
enhance
consumer
experience.Furthermore,
through
real-time
insights
analytics,
integration
improves
decision-making
capabilities.However,
some
associated
challenges
include
concerns
about
security
privacy.In
conclusion,
a
factor
businesses,
enabling
opportunities
for
innovation
growth.
El
libro
aborda
la
integración
de
inteligencia
artificial
(IA)
en
diferentes
áreas
las
ciencias
económicas
y
gestión
empresarial,
explorando
sus
impactos
beneficios.
En
introducción,
se
destaca
cómo
transformación
digital
redefine
estrategias
organizacionales
fomenta
innovación
continua,
mejorando
capacidad
respuesta
a
demandas
del
mercado.
Metodológicamente,
utilizan
estudios
caso
análisis
datos
para
ilustrar
aplicación
IA.
Los
resultados
muestran
que,
contabilidad,
IA
automatiza
tareas
repetitivas,
reduce
errores
mejora
precisión,
permitiendo
los
profesionales
centrarse
actividades
estratégicas.
el
ámbito
financiero,
algoritmos
trading
inteligente
aumentan
velocidad
precisión
transacciones,
competitividad
liquidez
La
riesgos
beneficia
modelos
predictivos
que
anticipan
posibles
amenazas,
mientras
cumplimiento
normativo
fortalece
mediante
monitoreo
automatizado.
términos
desarrollo
sostenible,
optimiza
distribución
recursos
eficiencia
energética,
contribuyendo
políticas
más
verdes
equitativas.
conclusión
resalta
necesidad
un
enfoque
ético
transparente
implementación
IA,
asegurar
decisiones
justas
responsables.
Advances in business strategy and competitive advantage book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 35 - 68
Published: Jan. 31, 2025
The
incorporation
of
Artificial
Intelligence
(AI)
within
Industry
5.0
significantly
enhances
resilience
among
small
businesses.
This
chapter
explores
how
AI
transforms
resilience,
sustainability,
and
customer
engagement
strategies.
With
Small
businesses
can
analyze
large
datasets
to
identify
risks,
optimize
operations,
deliver
personalized
experiences
that
align
with
consumer
expectations.
AI's
ability
process
data
efficiently
allows
anticipate
market
changes
navigate
uncertainties.
Additionally,
adopting
fosters
a
culture
encouraging
employees
embrace
change.
also
supports
sustainable
practices
by
optimizing
resource
use
reducing
waste.
Customer
improves
through
AI-driven
personalization,
allowing
tailor
products
services
individual
preferences.
concludes
recommendations
for
businesses:
invest
in
employee
training
collaboration,
ensure
leadership
commitment,
prioritize
foster
adaptability
thrive
today's
5.0.
International Journal For Multidisciplinary Research,
Journal Year:
2024,
Volume and Issue:
6(4)
Published: Aug. 14, 2024
This
integrative
literature
review
examines
the
transformative
impact
of
advanced
technologies,
particularly
artificial
intelligence
(AI),
Internet
Things
(IoT),
and
blockchain,
on
public
administration.
It
addresses
urgent
need
for
enhanced
operational
efficiency
transparency
through
AI-driven
decision-making.
The
study
reviews
current
to
highlight
AI's
potential
revolutionize
service
delivery,
improve
smart
city
initiatives,
enable
data-driven
policy-making.
Significant
challenges
such
as
data
privacy,
algorithmic
transparency,
ethical
considerations
are
also
identified.
methodology
involves
a
comprehensive
scholarly
articles,
reports,
academic
publications,
focusing
AI
applications
in
administration
technologies.
analysis
reveals
notable
improvements
due
AI,
alongside
concerns
about
biases,
implementation
issues.
findings
confirm
but
emphasize
necessity
ongoing
supervision,
regular
audits,
development
models
capable
detecting
rectifying
anomalies.
proposes
creating
positions
Public
Administration
Ethics
Officers
(PAAIEOs),
Data
Transparency
Managers
(PADTMs),
Predictive
Analytics
(PAPAOs)
ensure
responsible
utilization,
maintaining
integrity
services
while
addressing
challenges.
concludes
that
is
promising
transforming
administration;
however,
careful
crucial
uphold
resilience.
Future
research
should
prioritize
longitudinal
studies
evaluate
long-term
impact,
focus
concerns,
fair
transparent
integration
These
have
significant
implications
practice
policy,
underscoring
robust
frameworks
regulatory
measures
guide
effective
use
International Journal of Environmental Research and Public Health,
Journal Year:
2025,
Volume and Issue:
22(2), P. 199 - 199
Published: Jan. 30, 2025
Background:
Artificial
intelligence
(AI)
is
revolutionizing
occupational
health
and
safety
(OHS)
by
addressing
workplace
hazards
enhancing
employee
well-being.
This
review
explores
the
broader
context
of
increasing
automation
digitalization,
focusing
on
role
human–AI
interaction
in
improving
health,
safety,
productivity
while
considering
associated
challenges.
Methods:
A
narrative
methodology
was
employed,
involving
a
comprehensive
literature
search
PubMed,
Embase,
Scopus
for
studies
published
within
last
25
years.
After
screening
relevance
eligibility,
total
52
articles
were
included
final
analysis.
These
publications
examined
various
AI
applications
OHS,
such
as
wearable
technologies,
predictive
analytics,
ergonomic
tools,
with
focus
their
contributions
limitations.
Results:
Key
findings
demonstrate
that
enhances
hazard
detection,
enables
real-time
monitoring,
improves
training
through
immersive
simulations,
significantly
contributing
to
safer
more
efficient
workplaces.
However,
challenges
data
privacy
concerns,
algorithmic
biases,
reduced
worker
autonomy
identified
significant
barriers
adoption
OHS.
Conclusions:
holds
great
promise
transforming
OHS
practices,
but
its
integration
requires
ethical
frameworks
human-centric
collaboration
models
ensure
transparency,
equity,
empowerment.
Addressing
these
will
allow
workplaces
harness
full
potential
creating
safer,
healthier,
sustainable
environments.
Discover Oncology,
Journal Year:
2025,
Volume and Issue:
16(1)
Published: March 13, 2025
Cancer
remains
a
significant
health
issue,
resulting
in
around
10
million
deaths
per
year,
particularly
developing
nations.
Demographic
changes,
socio-economic
variables,
and
lifestyle
choices
are
responsible
for
the
rise
cancer
cases.
Despite
potential
to
mitigate
adverse
effects
of
by
early
detection
implementation
prevention
methods,
several
nations
have
limited
screening
facilities.
In
oncology,
use
artificial
intelligence
(AI)
represents
transformative
advancement
diagnosis,
prognosis,
treatment.
The
AI
biomarker
discovery
improves
precision
medicine
uncovering
signatures
that
essential
treatment
diseases
within
vast
diverse
datasets.
Deep
learning
machine
diagnostics
two
examples
technologies
changing
way
biomarkers
made
finding
patterns
large
datasets
making
new
make
it
possible
deliver
accurate
effective
therapies.
Existing
gaps
include
data
quality,
algorithmic
transparency,
ethical
concerns
privacy,
among
others.
methodologies
with
seeks
transform
improving
patient
survival
rates
through
enhanced
diagnosis
targeted
therapy.
This
commentary
aims
clarify
how
is
identification
novel
optimal
focused
treatment,
improved
clinical
outcomes,
while
also
addressing
certain
obstacles
issues
related
application
oncology.
Data
from
reputable
scientific
databases
such
as
PubMed,
Scopus,
ScienceDirect
were
utilized.
This
study
explores
the
role
of
artificial
intelligence
(AI)
in
project
management
by
considering
relationship
and
impact
on
resource
allocation,
decision-making,
risk
assessment.
The
research
identifies
machine
learning
(ML)
deep
(DL)
as
two
commonly
used
AI
tools
that
can
help
streamline
processes
for
improved
decision-
making.
With
an
emphasis
beyond
automation,
which
extends
to
decision
support
systems
data-driven
insights,
findings
suggest
managers
effectively
deploy
analyze
historical
data
identify
trends
patterns
informed
choices,
enhanced
mitigation.
