Neurocomputing,
Journal Year:
2024,
Volume and Issue:
590, P. 127759 - 127759
Published: April 25, 2024
This
paper
engages
in
a
comprehensive
investigation
concerning
the
application
of
Explainable
Artificial
Intelligence
(xAI)
within
context
deep
learning
and
Intelligence,
with
specific
focus
on
its
implications
for
cybersecurity.
Firstly,
gives
an
overview
xAI
techniques
their
significance
benefits
when
applied
Subsequently,
authors
methodically
delineate
systematic
mapping
study,
which
serves
as
investigative
tool
discerning
potential
trajectory
field.
strategic
methodological
framework
lets
one
identify
future
research
directions
opportunities
that
underlie
integration
realm
Deep
Learning,
cybersecurity,
are
described
in-depth.
Then,
brings
together
all
gathered
insights
from
this
extensive
closes
final
conclusions.
Expert Systems with Applications,
Journal Year:
2023,
Volume and Issue:
244, P. 122778 - 122778
Published: Dec. 10, 2023
Class
imbalance
(CI)
in
classification
problems
arises
when
the
number
of
observations
belonging
to
one
class
is
lower
than
other.
Ensemble
learning
combines
multiple
models
obtain
a
robust
model
and
has
been
prominently
used
with
data
augmentation
methods
address
problems.
In
last
decade,
strategies
have
added
enhance
ensemble
methods,
along
new
such
as
generative
adversarial
networks
(GANs).
A
combination
these
applied
many
studies,
evaluation
different
combinations
would
enable
better
understanding
guidance
for
application
domains.
this
paper,
we
present
computational
study
evaluate
prominent
benchmark
CI
We
general
framework
that
evaluates
9
Our
objective
identify
most
effective
improving
performance
on
imbalanced
datasets.
The
results
indicate
can
significantly
improve
find
traditional
synthetic
minority
oversampling
technique
(SMOTE)
random
(ROS)
are
not
only
selected
problems,
but
also
computationally
less
expensive
GANs.
vital
development
novel
handling
Agronomy,
Journal Year:
2023,
Volume and Issue:
13(5), P. 1397 - 1397
Published: May 18, 2023
Artificial
intelligence
(AI)
involves
the
development
of
algorithms
and
computational
models
that
enable
machines
to
process
analyze
large
amounts
data,
identify
patterns
relationships,
make
predictions
or
decisions
based
on
analysis.
AI
has
become
increasingly
pervasive
across
a
wide
range
industries
sectors,
with
healthcare,
finance,
transportation,
manufacturing,
retail,
education,
agriculture
are
few
examples
mention.
As
technology
continues
advance,
it
is
expected
have
an
even
greater
impact
in
future.
For
instance,
being
used
agri-food
sector
improve
productivity,
efficiency,
sustainability.
It
potential
revolutionize
several
ways,
including
but
not
limited
precision
agriculture,
crop
monitoring,
predictive
analytics,
supply
chain
optimization,
food
processing,
quality
control,
personalized
nutrition,
safety.
This
review
emphasizes
how
recent
developments
transformed
by
improving
reducing
waste,
enhancing
safety
quality,
providing
particular
examples.
Furthermore,
challenges,
limitations,
future
prospects
field
summarized.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 101603 - 101625
Published: Jan. 1, 2024
Autonomous
driving
has
achieved
significant
milestones
in
research
and
development
over
the
last
two
decades.
There
is
increasing
interest
field
as
deployment
of
autonomous
vehicles
(AVs)
promises
safer
more
ecologically
friendly
transportation
systems.
With
rapid
progress
computationally
powerful
artificial
intelligence
(AI)
techniques,
AVs
can
sense
their
environment
with
high
precision,
make
safe
real-time
decisions,
operate
reliably
without
human
intervention.
However,
intelligent
decision-making
such
not
generally
understandable
by
humans
current
state
art,
deficiency
hinders
this
technology
from
being
socially
acceptable.
Hence,
aside
making
must
also
explain
AI-guided
process
order
to
be
regulatory-compliant
across
many
jurisdictions.
Our
study
sheds
comprehensive
light
on
explainable
(XAI)
approaches
for
AVs.
In
particular,
we
following
contributions.
First,
provide
a
thorough
overview
state-of-the-art
emerging
XAI-based
driving.
We
then
propose
conceptual
framework
considering
essential
elements
end-to-end
Finally,
present
prospective
directions
paradigms
future
that
hold
promise
enhancing
transparency,
trustworthiness,
societal
acceptance
Computer Science & IT Research Journal,
Journal Year:
2023,
Volume and Issue:
4(3), P. 185 - 199
Published: Dec. 3, 2023
This
paper
examines
the
role
of
Artificial
Intelligence
(AI)
in
developing
countries,
focusing
on
bridging
gap
between
its
vast
potential
and
effective
implementation.
As
AI
technologies
advance
globally,
their
impact
socio-economic
development
becomes
increasingly
critical,
particularly
regions
with
diverse
challenges
opportunities.
The
study
investigates
current
landscape
adoption
analyzing
benefits,
challenges,
ethical
considerations.
Through
a
comprehensive
review
literature
case
studies,
explores
strategies
solutions
for
harnessing
AI's
transformative
power
sectors
such
as
healthcare,
agriculture,
education.
findings
emphasize
importance
capacity
building,
public-private
partnerships,
tailored
policy
frameworks
to
address
infrastructure
limitations
skill
gaps.
research
contributes
nuanced
understanding
opportunities
complexities
surrounding
implementation
providing
insights
policymakers,
practitioners,
scholars
seeking
navigate
this
evolving
technological
landscape
Keywords:
Intelligence;
Global
Connectivity;
Emerging
Technologies;
Organizational
Resilience;
Sustainable
Growth;
Developing
Country.
Decision Support Systems,
Journal Year:
2024,
Volume and Issue:
180, P. 114194 - 114194
Published: Feb. 17, 2024
Although
artificial
intelligence
can
contribute
to
decision-making
processes,
many
industry
players
lag
behind
pioneering
companies
in
utilizing
intelligence-driven
technologies,
which
is
a
significant
problem.
Explainable
be
viable
solution
mitigate
this
This
paper
proposes
research
model
address
how
explainable
impact
processes.
Using
an
experimental
design,
empirical
data
collected
test
the
model.
one
of
pioneer
papers
providing
evidence
about
on
supply
chain
We
propose
serial
mediation
path,
includes
transparency
and
agile
decision-making.
Findings
reveal
that
enhances
transparency,
thereby
significantly
contributing
for
improving
cyber
resilience
during
cyberattacks.
Moreover,
we
conduct
post
hoc
analysis
using
text
explore
themes
present
tweets
discussing
decision
support
systems.
The
results
indicate
predominantly
positive
attitude
towards
within
these
Furthermore,
reveals
two
main
emphasize
importance
explainability,
interpretability
intelligence.
Advances in educational technologies and instructional design book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 19 - 52
Published: Oct. 11, 2024
As
the
digital
revolution
transforms
education,
Explainable
AI
(XAI)
plays
a
key
role
in
advancing
educational
intelligence.
This
chapter
examines
how
XAI
is
reshaping
education
by
making
machine
learning
processes
transparent.
Unlike
traditional
AI's
“black
boxes,”
clarifies
algorithms
make
recommendations,
assessments,
and
personalized
pathways.
transparency
helps
educators
understand
trust
tools,
them
effective
partners
education.
The
also
explores
XAI's
practical
uses
adaptive
platforms
intelligent
tutoring
systems,
showing
clarity
can
enhance
environments.
It
allows
to
address
biases,
customize
strategies,
track
outcomes
more
precisely.
Through
real-world
case
studies
theoretical
insights,
illustrates
bridges
advanced
technology
with
teaching
practices,
promoting
transparent
equitable
system.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(8)
Published: July 26, 2024
Abstract
As
the
range
of
decisions
made
by
Artificial
Intelligence
(AI)
expands,
need
for
Explainable
AI
(XAI)
becomes
increasingly
critical.
The
reasoning
behind
specific
outcomes
complex
and
opaque
financial
models
requires
a
thorough
justification
to
improve
risk
assessment,
minimise
loss
trust,
promote
more
resilient
trustworthy
ecosystem.
This
Systematic
Literature
Review
(SLR)
identifies
138
relevant
articles
from
2005
2022
highlights
empirical
examples
demonstrating
XAI's
potential
benefits
in
industry.
We
classified
according
tasks
addressed
using
XAI,
variation
XAI
methods
between
applications
tasks,
development
application
new
methods.
most
popular
were
credit
management,
stock
price
predictions,
fraud
detection.
three
commonly
employed
black-box
techniques
finance
whose
explainability
was
evaluated
Neural
Networks
(ANN),
Extreme
Gradient
Boosting
(XGBoost),
Random
Forest.
Most
examined
publications
utilise
feature
importance,
Shapley
additive
explanations
(SHAP),
rule-based
In
addition,
they
employ
frameworks
that
integrate
multiple
techniques.
also
concisely
define
existing
challenges,
requirements,
unresolved
issues
applying
sector.
IEEE Transactions on Learning Technologies,
Journal Year:
2024,
Volume and Issue:
17, P. 1898 - 1919
Published: Jan. 1, 2024
Modern
online
education
has
the
capacity
to
provide
intelligent
educational
services
by
automatically
analyzing
substantial
amounts
of
student
behavioral
data.
Knowledge
Tracing
(KT)
is
one
fundamental
tasks
for
data
analysis,
aiming
monitor
students'
evolving
knowledge
state
during
their
problem-solving
process.
In
recent
years,
a
number
studies
have
concentrated
on
this
rapidly
growing
field,
significantly
contributing
its
advancements.
survey,
we
will
conduct
thorough
investigation
these
progressions.
Firstly,
present
three
types
KT
models
with
distinct
technical
routes.
Subsequently,
review
extensive
variants
that
consider
more
stringent
learning
assumptions.
Moreover,
development
cannot
be
separated
from
applications,
thereby
typical
applications
in
various
scenarios.
To
facilitate
work
researchers
and
practitioners
developed
two
open-source
algorithm
libraries:
EduData
enables
download
preprocessing
KT-related
datasets,
EduKTM
provides
an
extensible
unified
implementation
existing
mainstream
models.
Finally,
discuss
potential
directions
future
research
field.
We
hope
current
survey
assist
both
fostering
KT,
benefiting
broader
range
students.
