F1000Research,
Journal Year:
2023,
Volume and Issue:
12, P. 1060 - 1060
Published: Aug. 31, 2023
The
management
of
medical
waste
is
a
complex
task
that
necessitates
effective
strategies
to
mitigate
health
risks,
comply
with
regulations,
and
minimize
environmental
impact.
In
this
study,
novel
approach
based
on
collaboration
technological
advancements
proposed.By
utilizing
colored
bags
identification
tags,
smart
containers
sensors,
object
recognition
air
soil
control
vehicles
Global
Positioning
System
(GPS)
temperature
humidity
outsourced
treatment,
the
system
optimizes
sorting,
storage,
treatment
operations.
Additionally,
incorporation
explainable
artificial
intelligence
(XAI)
technology,
leveraging
scikit-learn,
xgboost,
catboost,
lightgbm,
skorch,
provides
real-time
insights
data
analytics,
facilitating
informed
decision-making
process
optimization.The
integration
these
cutting-edge
technologies
forms
foundation
an
efficient
intelligent
system.
Furthermore,
article
highlights
use
genetic
algorithms
(GA)
solve
vehicle
routing
models,
optimizing
collection
routes
minimizing
transportation
time
centers.Overall,
combination
advanced
technologies,
optimization
algorithms,
XAI
contributes
improved
practices,
ultimately
benefiting
both
public
environment.
Technological Forecasting and Social Change,
Journal Year:
2024,
Volume and Issue:
202, P. 123301 - 123301
Published: March 5, 2024
Research
on
the
application
of
Artificial
Intelligence
(AI)-based
technologies
in
HRM
domain
has
attracted
significant
scholarly
attention.
Yet,
few
studies
have
consolidated
key
trends
adopting
AI
for
HRM,
especially
managerial
competencies
required
AI-based
and
identifying
research
directions
HR
managers,
including
development
an
AI-focused
competency
framework
managers.
A
systematic
literature
review
(SLR)
bibliometrics
analysis
were
conducted
to
identify
current
direction
managers
HRM.
Several
themes
capabilities
identified,
utilizing
Dynamic
Capabilities
View
(DCV).
The
SLR
identified
applications
various
tools
techniques
functions,
recruitment
selection
was
one
with
broadest
use
applications.
Managerial
cognitive
capability,
human
capital,
social
capital
DCV
considered
initial
coding
categories
under
which
are
adoption
This
study
utilized
SLR,
Bibliometric,
directed
content
as
three
distinct
but
interrelated
sets
methodologies
extracting
novel
insights
into
It
highlights
associated
that
need
mapping
its
adoption.
The
growing
availability
of
generative
AI
technologies
such
as
large
language
models
(LLMs)
has
significant
implications
for
creative
work.
This
paper
explores
twofold
aspects
integrating
LLMs
into
the
process
–
divergence
stage
idea
generation,
and
convergence
evaluation
selection
ideas.
We
devised
a
collaborative
group-AI
Brainwriting
ideation
framework,
which
incorporated
an
LLM
enhancement
group
process,
evaluated
generation
resulted
solution
space.
To
assess
potential
using
in
we
design
engine
compared
it
to
ratings
assigned
by
three
expert
six
novice
evaluators.
Our
findings
suggest
that
could
enhance
both
its
outcome.
also
provide
evidence
can
support
evaluation.
conclude
discussing
HCI
education
practice.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 35796 - 35812
Published: Jan. 1, 2024
The
field
of
drug
discovery
has
experienced
a
remarkable
transformation
with
the
advent
artificial
intelligence
(AI)
and
machine
learning
(ML)
technologies.
However,
as
these
AI
ML
models
are
becoming
more
complex,
there
is
growing
need
for
transparency
interpretability
models.
Explainable
Artificial
Intelligence
(XAI)
novel
approach
that
addresses
this
issue
provides
interpretable
understanding
predictions
made
by
In
recent
years,
been
an
increasing
interest
in
application
XAI
techniques
to
discovery.
This
review
article
comprehensive
overview
current
state-of-the-art
discovery,
including
various
methods,
their
challenges
limitations
also
covers
target
identification,
compound
design,
toxicity
prediction.
Furthermore,
suggests
potential
future
research
directions
aims
provide
state
its
transform
field.
Advances in computational intelligence and robotics book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 123 - 176
Published: Jan. 18, 2024
Given
the
inherent
risks
in
medical
decision-making,
professionals
carefully
evaluate
a
patient's
symptoms
before
arriving
at
plausible
diagnosis.
For
AI
to
be
widely
accepted
and
useful
technology,
it
must
replicate
human
judgment
interpretation
abilities.
XAI
attempts
describe
data
underlying
black-box
approach
of
deep
learning
(DL),
machine
(ML),
natural
language
processing
(NLP)
that
explain
how
judgments
are
made.
This
chapter
provides
survey
most
recent
methods
employed
imaging
related
fields,
categorizes
lists
types
XAI,
highlights
used
make
topics
more
interpretable.
Additionally,
focuses
on
challenging
issues
applications
guides
development
better
deep-learning
system
explanations
by
applying
principles
analysis
pictures
text.
BMJ,
Journal Year:
2025,
Volume and Issue:
unknown, P. e081554 - e081554
Published: Feb. 5, 2025
Despite
major
advances
in
artificial
intelligence
(AI)
research
for
healthcare,
the
deployment
and
adoption
of
AI
technologies
remain
limited
clinical
practice.
This
paper
describes
FUTURE-AI
framework,
which
provides
guidance
development
trustworthy
tools
healthcare.
The
Consortium
was
founded
2021
comprises
117
interdisciplinary
experts
from
50
countries
representing
all
continents,
including
scientists,
researchers,
biomedical
ethicists,
social
scientists.
Over
a
two
year
period,
guideline
established
through
consensus
based
on
six
guiding
principles—fairness,
universality,
traceability,
usability,
robustness,
explainability.
To
operationalise
set
30
best
practices
were
defined,
addressing
technical,
clinical,
socioethical,
legal
dimensions.
recommendations
cover
entire
lifecycle
healthcare
AI,
design,
development,
validation
to
regulation,
deployment,
monitoring.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 61829 - 61854
Published: Jan. 1, 2023
Over
the
decades,
Artificial
Intelligence
(AI)
and
machine
learning
has
become
a
transformative
solution
in
many
sectors,
services,
technology
platforms
wide
range
of
applications,
such
as
smart
healthcare,
financial,
political,
surveillance
systems.
In
large
amount
data
is
generated
about
diverse
aspects
our
life.
Although
utilizing
AI
real-world
applications
provides
numerous
opportunities
for
societies
industries,
it
raises
concerns
regarding
privacy.
Data
used
an
system
are
cleaned,
integrated,
processed
throughout
life
cycle.
Each
these
stages
can
introduce
unique
threats
to
individual's
privacy
have
impact
on
ethical
processing
protection
data.
this
paper,
we
examine
risks
different
phases
cycle
review
existing
privacy-enhancing
solutions.
We
four
categories
risk,
including
(i)
risk
identification,
(ii)
making
inaccurate
decision,
(iii)
non-transparency
systems,
(iv)
non-compliance
with
regulations
best
practices.
then
examined
potential
each
phase,
evaluated
concerns,
reviewed
technologies,
requirements,
process
solutions
countermeasure
risks.
also
some
policies
need
compliance
available
AI-based
The
main
contribution
survey
examining
challenges
solutions,
technology,
process,
legislation
entire
phase
cycle,
open
been
identified.
BMJ Health & Care Informatics,
Journal Year:
2023,
Volume and Issue:
30(1), P. e100920 - e100920
Published: Dec. 1, 2023
The
integration
of
artificial
intelligence
(AI)
into
healthcare
is
progressively
becoming
pivotal,
especially
with
its
potential
to
enhance
patient
care
and
operational
workflows.
This
paper
navigates
through
the
complexities
potentials
AI
in
healthcare,
emphasising
necessity
explainability,
trustworthiness,
usability,
transparency
fairness
developing
implementing
models.
It
underscores
'black
box'
challenge,
highlighting
gap
between
algorithmic
outputs
human
interpretability,
articulates
pivotal
role
explainable
enhancing
accountability
applications
healthcare.
discourse
extends
ethical
considerations,
exploring
biases
dilemmas
that
may
arise
application,
a
keen
focus
on
ensuring
equitable
use
across
diverse
global
regions.
Furthermore,
explores
concept
responsible
advocating
for
balanced
approach
leverages
AI's
capabilities
enhanced
delivery
ensures
ethical,
transparent
accountable
technology,
particularly
clinical
decision-making
care.
Technological Forecasting and Social Change,
Journal Year:
2024,
Volume and Issue:
202, P. 123326 - 123326
Published: March 16, 2024
Sentiment
analysis
has
demonstrated
its
value
in
a
range
of
high-stakes
domains.
From
financial
markets
to
supply
chain
management,
logistics,
and
technology
legitimacy
assessment,
sentiment
offers
insights
into
public
sentiment,
actionable
data,
improved
decision
forecasting.
This
study
contributes
this
growing
body
research
by
offering
novel
multi-view
deep
learning
approach
that
incorporates
non-textual
features
like
emojis.
The
proposed
considers
both
textual
emoji
views
as
distinct
emotional
information
for
the
classification
model,
results
acknowledge
their
individual
combined
contributions
analysis.
Comparative
with
baseline
classifiers
reveals
incorporating
significantly
enriches
analysis,
enhancing
accuracy,
F1-score,
execution
time
model.
Additionally,
employs
LIME
explainable
provide
model's
decision-making
process,
enabling
businesses
understand
factors
driving
customer
sentiment.
present
literature
on
text
context
social
media
provides
an
innovative
analytics
method
extract
valuable
from
electronic
word
mouth
(eWOM),
which
can
help
them
stay
ahead
competition
rapidly
evolving
digital
landscape.
In
addition,
findings
paper
have
important
implications
policy
development
communication
monitoring.
Recognizing
importance
emojis
expression
inform
policies
helping
better
tailor
solutions
address
concerns
public.
Production Planning & Control,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 12
Published: Feb. 27, 2024
Explainable
artificial
intelligence
(XAI)
has
been
instrumental
in
enabling
the
process
of
making
informed
decisions.
The
emergence
various
supply
chain
(SC)
platforms
modern
times
altered
nature
SC
interactions,
resulting
a
notable
degree
uncertainty.
This
study
aims
to
conduct
thorough
analysis
existing
literature
on
decision
support
systems
(DSSs)
and
their
incorporation
XAI
functionalities
within
domain
SC.
Our
revealed
influence
decision-making
field
utilizes
SHapley
Additive
exPlanations
(SHAP)
technique
online
data
using
Python
machine
learning
(ML)
process.
Explanatory
algorithms
are
specifically
crafted
augment
lucidity
ML
models
by
furnishing
rationales
for
prognostications
they
produce.
present
establish
measurable
standards
identifying
constituents
DSSs
that
context
assessed
prior
research
with
regards
ability
make
predictions,
utilization
dataset,
number
variables
examined,
development
capability,
validation
decision-making,
emphasizes
domains
necessitate
additional
exploration
concerning
intelligent
under
conditions