Computer Science & IT Research Journal,
Journal Year:
2024,
Volume and Issue:
5(3), P. 703 - 724
Published: March 22, 2024
In
the
ever-evolving
landscape
of
cybersecurity,
proliferation
artificial
intelligence
(AI)
technologies
introduces
both
promising
advancements
and
daunting
challenges.
This
paper
explores
theoretical
underpinnings
practical
implications
addressing
cybersecurity
challenges
in
age
AI.
With
integration
AI
into
various
facets
digital
infrastructure,
including
threat
detection,
authentication,
response
mechanisms,
cyber
threats
have
become
increasingly
sophisticated
difficult
to
mitigate.
Theoretical
approaches
delve
understanding
intricate
interplay
between
algorithms,
human
behavior,
adversarial
tactics,
elucidating
underlying
mechanisms
attacks
defense
strategies.
However,
this
complexity
also
engenders
novel
vulnerabilities,
as
AI-driven
leverage
machine
learning
algorithms
evade
traditional
security
measures,
posing
formidable
organizations
across
sectors.
As
such,
solutions
necessitate
a
multifaceted
approach,
encompassing
robust
intelligence,
adaptive
ethical
considerations
safeguard
against
effectively.
Leveraging
for
holds
promise
enhancing
detection
capabilities,
automating
actions,
augmenting
analysts'
capabilities.
Yet,
inherent
limitations,
such
algorithmic
biases,
data
privacy
concerns,
potential
AI-enabled
attacks,
underscore
need
comprehensive
risk
management
framework.
Regulatory
frameworks
industry
standards
play
crucial
role
shaping
development
deployment
AI-powered
solutions,
ensuring
accountability,
transparency,
compliance
with
principles.
Moreover,
fostering
interdisciplinary
collaboration
investing
education
training
are
vital
cultivating
skilled
workforce
equipped
navigate
evolving
landscape.
By
integrating
insights
strategies,
elucidates
key
opportunities
securing
systems,
offering
policymakers,
researchers,
practitioners
alike.
Keywords:
Cybersecurity;
Artificial
Intelligence;
Threat
Detection;
Defense
Strategies;
Ethical
Considerations;
Frameworks.
Open Access Research Journal of Science and Technology,
Journal Year:
2024,
Volume and Issue:
10(2), P. 021 - 030
Published: March 26, 2024
This
paper
delves
into
the
theoretical
underpinnings
of
agile
methodologies
and
investigates
their
potential
to
enhance
customer
satisfaction
in
digital
banking.
Theoretical
foundations
draw
on
several
key
frameworks
complexity
theory,
complex
systems,
like
banking
ecosystems,
exhibit
emergent
properties.
Traditional
linear
approaches
struggle
predict
these.
Agile
embraces
iterative
development
cycles
adaptability
changing
requirements,
acknowledging
this
lean
thinking,
derived
from
manufacturing,
thinking
prioritizes
eliminating
waste
maximizing
value.
translates
by
focusing
short
sprints,
prioritizing
features
with
highest
impact,
minimizing
unnecessary
functionalities
co-creation,
traditional
models
often
distance
customers
process.
emphasizes
actively
involving
them
design
testing.
fosters
a
deeper
understanding
needs
leads
more
relevant
satisfying
experiences.
practices
encompass
diverse
practices.
visual
management
system
focuses
workflow
optimization.
Promoting
continuous
flow
work
deployment
user
stories
acceptance
criteria,
User
Acceptance
criteria
define
specific
conditions
feature
must
meet
for
approval.
These
ensure
align
expectations.
hold
significant
promise
enhancing
digit
allows
banks
deliver
new
faster,
keeping
pace
evolving
demands.
Customers
benefit
quicker
access
innovative
solutions
that
address
financial
needs.
results
experiences
are
intuitive,
efficient,
cater
Increased
Innovation,
The
nature
learning
experimentation.
Banks
can
test
features,
gather
feedback,
rapidly
iterate
upon
them,
leading
dynamic
experience.
Improved
transparency
trust,
promote
open
communication
collaboration
between
teams
customers.
kept
informed
updates
have
voice
shaping
process,
fostering
trust
sense
ownership.
Engineering Science & Technology Journal,
Journal Year:
2024,
Volume and Issue:
5(3), P. 1072 - 1085
Published: March 24, 2024
This
paper
explores
the
transformative
potential
of
Artificial
Intelligence
(AI)
in
personalized
marketing.
It
highlights
how
AI
can
analyze
vast
amounts
customer
data
to
create
targeted
messages,
recommendations,
and
real-time
interactions
that
resonate
with
individual
needs
preferences.
approach
fosters
deeper
consumer
engagement,
leading
increased
satisfaction,
brand
loyalty,
business
success.
The
discusses
future
shaping
marketing
experiences.
However,
responsible
implementation
will
be
paramount
ensuring
a
positive
for
both
brands
consumers.
Enhanced
version
abstract
incorporating
additional
insights,
this
delves
into
power
algorithms
multitude
points,
including
purchase
history,
website
behavior,
social
media
interactions.
rich
empowers
highly
By
fostering
AI-powered
personalization
unlocks
pathway
ultimately,
significant
growth.
acknowledges
ethical
considerations
accompany
implementation.
Responsible
practices
are
paramount,
security
mitigating
bias
prevent
discriminatory
practices.
Transparency
is
collected
used
builds
trust
consumers,
mutually
beneficial
relationship.
Looking
ahead,
Imagine
Chat
bot
offering
product
recommendations
real-time,
or
virtual
reality
experiences
tailored
lies
creating
genuine
connections
provides
tools
personalize
journey
at
every
touch
point.
navigating
landscape
prioritizing
crucial
consumers.
Keywords:
(AI),
Personalized
Marketing,
Customer
Engagement,
Data,
Marketing
Strategy.
International Journal of Scientific Research Updates,
Journal Year:
2024,
Volume and Issue:
7(1), P. 092 - 102
Published: March 26, 2024
This
paper
delves
into
theoretical
frameworks
in
AI
for
credit
risk
assessment,
exploring
how
these
enhance
banking
efficiency
and
accuracy.
It
discusses
various
techniques
such
as
machine
learning
algorithms,
neural
networks,
natural
language
processing,
their
application
assessment.
Furthermore,
it
examines
the
challenges
opportunities
presented
by
frameworks,
highlighting
potential
to
revolutionize
sector.
Revolutionizing
Credit
Risk
Assessment
Banking,
The
Role
of
Artificial
Intelligence
In
dynamic
realm
finance,
assessment
stands
a
fundamental
pillar
institutions.
Traditionally,
this
process
has
heavily
relied
on
statistical
models
historical
data.
However,
emergence
(AI)
catalyzed
transformative
shift
domain.
elucidates
underpinnings
employed
investigates
profound
implications
enhancing
accuracy
operations.
exploration
begins
delineating
pertinent
Leveraging
processing
techniques,
offer
innovative
approaches
evaluate
creditworthiness.
Unlike
conventional
methods,
AI-driven
possess
capacity
ingest
vast
datasets,
identify
intricate
patterns,
adapt
dynamically
evolving
market
dynamics.
Such
capabilities
empower
banks
make
more
informed
timely
decisions
regarding
lending
activities.
Moreover,
practical
Through
case
studies
empirical
evidence,
advanced
methodologies
enable
mitigate
risks
while
maximizing
profitability.
By
harnessing
AI,
financial
institutions
can
optimize
scoring
processes,
defaulters
with
greater
accuracy,
customize
terms
based
individual
profiles.
Additionally,
facilitates
real-time
monitoring
portfolios,
allowing
proactive
management
interventions
prevent
adverse
outcomes.
Engineering Science & Technology Journal,
Journal Year:
2024,
Volume and Issue:
5(4), P. 1415 - 1430
Published: April 17, 2024
This
systematic
review
explores
the
advancements
and
challenges
associated
with
integration
of
artificial
intelligence
(AI)
in
promoting
technical
literacy.
Technical
literacy
is
increasingly
important
today's
digital
age,
where
understanding
utilizing
technology
are
essential
skills.
AI
has
potential
to
enhance
by
providing
personalized
learning
experiences,
facilitating
hands-on
learning,
offering
innovative
tools
resources.
However,
education
also
presents
challenges,
such
as
ensuring
equitable
access,
addressing
ethical
considerations,
overcoming
barriers.
The
examines
a
range
studies
literature
related
for
literacy,
focusing
on
key
themes
tools.
It
highlights
transform
tailored
experiences
that
cater
individual
needs
preferences.
AI-driven
tools,
simulations,
virtual
laboratories,
intelligent
tutoring
systems,
have
been
shown
student
engagement
concepts.
Despite
benefits,
identifies
integration,
including
need
teacher
training,
concerns
about
data
privacy,
risk
reinforcing
existing
inequalities.
Addressing
these
requires
careful
planning,
collaboration
between
educators
developers,
commitment
access
educational
Overall,
this
provides
insights
into
current
state
opportunities
approach.
By
leveraging
AI,
can
prepare
students
success
technology-driven
world.
Keywords:
Advancement,
Challenges,
Integration,
Literacy.
International Journal of Management & Entrepreneurship Research,
Journal Year:
2024,
Volume and Issue:
6(4), P. 1069 - 1077
Published: April 7, 2024
This
review
paper
explores
the
intricate
balance
between
profit,
social
responsibility,
and
environmental
stewardship
in
ethical
supply
chain
management.
It
delves
into
challenges
businesses
face
integrating
practices
within
their
chains,
highlighting
conflict
profitability
imperatives.
The
proposes
a
multifaceted
approach
to
navigate
these
complexities,
encompassing
best
practices,
adherence
policies
regulations,
leveraging
technology
innovation.
lens
emphasizes
importance
of
considerations
enhancing
corporate
sustainability,
competitiveness,
stakeholder
trust.
Recommendations
for
directions
future
research
are
provided,
aiming
further
understanding
implementation
management
strategies
that
benefit
both
society.
Keywords:
Ethical
Supply
Chain
Management,
Corporate
Social
Responsibility,
Environmental
Stewardship,
Sustainability,
Technology
Innovation,
Stakeholder
Engagement.
Engineering Science & Technology Journal,
Journal Year:
2024,
Volume and Issue:
5(4), P. 1524 - 1541
Published: April 26, 2024
Agile
development
has
emerged
as
a
prominent
approach
in
digital
transformations
due
to
its
flexibility
and
adaptability
changing
requirements.
This
review
explores
the
theoretical
foundations
practical
applications
of
agile
context
transformations.
Theoretical
stem
from
iterative
incremental
methodologies
that
prioritize
customer
collaboration,
adaptive
planning,
continuous
improvement.
principles,
outlined
Manifesto,
emphasize
individuals
interactions
over
processes
tools,
working
software
comprehensive
documentation,
collaboration
contract
negotiation,
responding
change
following
plan.
These
principles
are
supported
by
various
frameworks
methodologies,
such
Scrum,
Kanban,
Extreme
Programming
(XP),
which
provide
specific
practices
guidelines
for
implementing
Practical
diverse
impactful.
enable
organizations
respond
quickly
market
demands
needs,
allowing
faster
delivery
value-added
products
services.
practices,
daily
stand-up
meetings,
sprint
retrospective
reviews,
promote
among
cross-functional
teams
foster
culture
also
enhances
project
transparency
stakeholder
engagement
through
regular
demonstrations
feedback
loops,
ensuring
final
product
meets
expectations.
Overall,
conceptualization
is
characterized
enabling
adapt
change,
deliver
value,
collaboration.
Understanding
underpinnings
implications
crucial
seeking
leverage
their
transformation
efforts.
Keywords:
Conceptualizing,
Development,
Digital
Transformation,
Foundations,
Applications.
International Journal of Management & Entrepreneurship Research,
Journal Year:
2024,
Volume and Issue:
6(5), P. 1489 - 1497
Published: May 4, 2024
This
review
paper
examines
the
pivotal
role
of
AI-driven
predictive
analytics
in
optimizing
supply
chain
operations
within
IT
industry.
By
leveraging
machine
learning,
deep
and
neural
networks,
can
significantly
enhance
demand
forecasting,
inventory
management,
supplier
selection,
risk
management.
Despite
its
potential
to
revolutionize
chains,
integration
AI
faces
challenges,
including
data
quality,
need
for
skilled
personnel,
organizational
resistance.
Strategic
implementation
approaches
are
discussed,
emphasizing
robust
infrastructure,
stakeholder
engagement,
continuous
innovation.
contributes
academic
discourse
by
highlighting
AI's
economic
social
implications
chains
suggesting
directions
future
research.
It
is
a
comprehensive
guide
practitioners
academics
navigating
complexities
optimization.
Keywords:
Predictive
Analytics,
Supply
Chain
Optimization,
Industry,
Machine
Learning,
Implementation.
World Journal of Advanced Science and Technology,
Journal Year:
2024,
Volume and Issue:
5(1), P. 026 - 030
Published: March 30, 2024
The
review
investigates
the
pressing
need
for
robust
cybersecurity
measures
within
logistics
and
shipping
sector,
where
digital
supply
chain
is
vulnerable
to
a
myriad
of
cyber
threats.
paper
delves
into
specific
challenges
faced
by
companies,
including
interconnectedness
global
chains,
reliance
on
technologies
operations,
high
value
goods
in
transit.
It
explores
multifaceted
nature
risks,
encompassing
threats
such
as
ransomware,
phishing
attacks,
data
breaches,
disruptions,
which
can
have
far-reaching
consequences
business
continuity
reputation.
Through
detailed
analysis,
study
elucidates
best
practices
tailored
industry,
both
technical
solutions
organizational
policies.
These
include
implementing
authentication
access
controls,
encrypting
sensitive
transit
at
rest,
establishing
secure
communication
channels,
conducting
regular
vulnerability
assessments
penetration
testing.
Furthermore,
emphasizes
importance
fostering
culture
awareness
among
employees
through
comprehensive
training
programs
incident
response
drills.
also
discusses
role
regulatory
compliance
frameworks
GDPR,
CCPA,
industry-specific
standards
like
ISO
27001
guiding
efforts
ensuring
adherence
practices.
By
providing
actionable
recommendations
insights
garnered
from
real-world
case
studies,
equips
companies
with
knowledge
tools
needed
bolster
their
defenses,
safeguard
critical
assets,
maintain
trust
ecosystem.
International Journal of Management & Entrepreneurship Research,
Journal Year:
2024,
Volume and Issue:
6(5), P. 1607 - 1624
Published: May 12, 2024
This
abstract
delves
into
the
realm
of
manufacturing
productivity
enhancement
through
review
AI-driven
supply
chain
management
(SCM)
optimization
and
Enterprise
Resource
Planning
(ERP)
systems
integration.
As
industries
strive
for
operational
excellence,
convergence
artificial
intelligence
(AI)
emerges
as
a
transformative
force
in
driving
efficiency,
agility,
competitiveness.
Through
comprehensive
analysis,
this
examines
synergistic
relationship
between
SCM
integration
ERP
systems,
elucidating
their
collective
impact
on
productivity.
encompasses
spectrum
technologies
methodologies,
including
predictive
analytics,
machine
learning,
autonomous
decision-making
aimed
at
optimizing
various
facets
chain,
from
demand
forecasting
inventory
to
production
planning
logistics
optimization.
By
harnessing
power
AI,
manufacturers
can
enhance
accuracy,
reduce
lead
times,
optimize
levels,
mitigate
disruptions,
thereby
improving
overall
customer
satisfaction.
Integration
plays
complementary
role
by
providing
centralized
platform
data
management,
process
automation,
cross-functional
collaboration.
seamless
with
tools,
enable
real-time
exchange,
actionable
insights,
end-to-end
visibility
across
facilitating
informed
agile
response
dynamic
market
conditions.
Drawing
insights
case
studies
industry
examples,
highlights
best
practices,
challenges,
emerging
trends
Strategies
successful
implementation,
organizational
readiness
assessment,
change
stakeholder
engagement,
are
discussed
guide
unlocking
full
potential
these
technologies.
In
conclusion,
offers
compelling
pathway
enhancing
productivity,
sustaining
competitive
advantage
digital
era..
Keywords:
Artificial
Intelligence,
Supply
Chain
Management,
Planning,
Manufacturing
Productivity,
AI
Integration,
Predictive
Analytics.
