Vehicles,
Journal Year:
2025,
Volume and Issue:
7(1), P. 11 - 11
Published: Jan. 27, 2025
This
study
introduces
a
novel
approach
for
traffic
control
systems
by
using
Large
Language
Models
(LLMs)
as
controllers.
The
utilizes
their
logical
reasoning,
scene
understanding,
and
decision-making
capabilities
to
optimize
throughput
provide
feedback
based
on
conditions
in
real
time.
LLMs
centralize
traditionally
disconnected
processes
can
integrate
data
from
diverse
sources
context-aware
decisions.
also
deliver
tailored
outputs
various
means
such
wireless
signals
visuals
drivers,
infrastructures,
autonomous
vehicles.
To
evaluate
LLMs’
ability
controllers,
this
proposed
four-stage
methodology.
methodology
includes
creation
environment
initialization,
prompt
engineering,
conflict
identification,
fine-tuning.
We
simulated
multi-lane
four-leg
intersection
scenarios
generated
detailed
datasets
enable
detection
Python
simulation
ground
truth.
used
chain-of-thought
prompts
lead
understanding
the
context,
detecting
conflicts,
resolving
them
rules,
delivering
context-sensitive
management
solutions.
evaluated
performance
of
GPT-4o-mini,
Gemini,
Llama
Results
showed
that
fine-tuned
GPT-mini
achieved
83%
accuracy
an
F1-score
0.84.
GPT-4o-mini
model
exhibited
promising
generating
actionable
insights,
with
high
ROUGE-L
scores
across
identification
0.95,
decision
making
0.91,
priority
assignment
0.94,
waiting
time
optimization
0.92.
confirmed
benefits
controller
real-world
applications.
demonstrated
offer
precise
recommendations
drivers
including
yielding,
slowing,
or
stopping
vehicle
dynamics.
demonstrates
transformative
potential
control,
enhancing
efficiency
safety
at
intersections.
The Innovation,
Journal Year:
2024,
Volume and Issue:
5(5), P. 100691 - 100691
Published: Aug. 23, 2024
Public
summary•What
does
AI
bring
to
geoscience?
has
been
accelerating
and
deepening
our
understanding
of
Earth
Systems
in
an
unprecedented
way,
including
the
atmosphere,
lithosphere,
hydrosphere,
cryosphere,
biosphere,
anthroposphere
interactions
between
spheres.•What
are
noteworthy
challenges
As
we
embrace
huge
potential
geoscience,
several
arise
reliability
interpretability,
ethical
issues,
data
security,
high
demand
cost.•What
is
future
The
synergy
traditional
principles
modern
AI-driven
techniques
holds
immense
promise
will
shape
trajectory
geoscience
upcoming
years.AbstractThis
paper
explores
evolution
geoscientific
inquiry,
tracing
progression
from
physics-based
models
data-driven
approaches
facilitated
by
significant
advancements
artificial
intelligence
(AI)
collection
techniques.
Traditional
models,
which
grounded
physical
numerical
frameworks,
provide
robust
explanations
explicitly
reconstructing
underlying
processes.
However,
their
limitations
comprehensively
capturing
Earth's
complexities
uncertainties
pose
optimization
real-world
applicability.
In
contrast,
contemporary
particularly
those
utilizing
machine
learning
(ML)
deep
(DL),
leverage
extensive
glean
insights
without
requiring
exhaustive
theoretical
knowledge.
ML
have
shown
addressing
science-related
questions.
Nevertheless,
such
as
scarcity,
computational
demands,
privacy
concerns,
"black-box"
nature
hinder
seamless
integration
into
geoscience.
methodologies
hybrid
presents
alternative
paradigm.
These
incorporate
domain
knowledge
guide
methodologies,
demonstrate
enhanced
efficiency
performance
with
reduced
training
requirements.
This
review
provides
a
comprehensive
overview
research
paradigms,
emphasizing
untapped
opportunities
at
intersection
advanced
It
examines
major
showcases
advances
large-scale
discusses
prospects
that
landscape
outlines
dynamic
field
ripe
possibilities,
poised
unlock
new
understandings
further
advance
exploration.Graphical
abstract
BioMedInformatics,
Journal Year:
2024,
Volume and Issue:
4(1), P. 837 - 852
Published: March 14, 2024
This
review
explores
the
transformative
integration
of
artificial
intelligence
(AI)
and
healthcare
through
conversational
AI
leveraging
Natural
Language
Processing
(NLP).
Focusing
on
Large
Models
(LLMs),
this
paper
navigates
various
sections,
commencing
with
an
overview
AI’s
significance
in
role
AI.
It
delves
into
fundamental
NLP
techniques,
emphasizing
their
facilitation
seamless
conversations.
Examining
evolution
LLMs
within
frameworks,
discusses
key
models
used
healthcare,
exploring
advantages
implementation
challenges.
Practical
applications
conversations,
from
patient-centric
utilities
like
diagnosis
treatment
suggestions
to
provider
support
systems,
are
detailed.
Ethical
legal
considerations,
including
patient
privacy,
ethical
implications,
regulatory
compliance,
addressed.
The
concludes
by
spotlighting
current
challenges,
envisaging
future
trends,
highlighting
potential
reshaping
interactions.
Education Sciences,
Journal Year:
2024,
Volume and Issue:
14(8), P. 814 - 814
Published: July 25, 2024
This
paper
investigates
the
integration
of
ChatGPT
into
educational
environments,
focusing
on
its
potential
to
enhance
personalized
learning
and
ethical
concerns
it
raises.
Through
a
systematic
literature
review,
interest
analysis,
case
studies,
research
scrutinizes
application
in
diverse
contexts,
evaluating
impact
teaching
practices.
The
key
findings
reveal
that
can
significantly
enrich
education
by
offering
dynamic,
experiences
real-time
feedback,
thereby
boosting
efficiency
learner
engagement.
However,
study
also
highlights
significant
challenges,
such
as
biases
AI
algorithms
may
distort
content
inability
replicate
emotional
interpersonal
dynamics
traditional
teacher–student
interactions.
acknowledges
fast-paced
evolution
technologies,
which
render
some
obsolete,
underscoring
need
for
ongoing
adapt
strategies
accordingly.
provides
balanced
analysis
opportunities
challenges
education,
emphasizing
considerations
strategic
insights
responsible
technologies.
These
are
valuable
educators,
policymakers,
researchers
involved
digital
transformation
education.
Eng—Advances in Engineering,
Journal Year:
2024,
Volume and Issue:
5(3), P. 1266 - 1297
Published: July 3, 2024
The
explosion
of
data
volume
in
the
digital
age
has
completely
changed
corporate
and
industrial
environments.
In-depth
analysis
large
datasets
to
support
strategic
decision-making
innovation
is
main
focus
this
paper’s
exploration
big
management
engineering.
A
thorough
examination
basic
elements
approaches
necessary
for
efficient
use—data
collecting,
storage,
processing,
analysis,
visualization—is
given
paper.
With
real-life
case
studies
from
several
sectors
complement
our
cutting-edge
methods
management,
we
present
useful
applications
results.
This
document
lists
difficulties
handling
data,
such
as
guaranteeing
scalability,
governance,
quality.
It
also
describes
possible
future
study
paths
deal
with
these
issues
promote
ongoing
creativity.
results
stress
need
combine
technology
industry
standards
improve
based
on
data.
Through
an
machine
learning,
real-time
predictive
analytics,
paper
offers
insightful
information
companies
hoping
use
a
advantage.
Lastly,
presents
cases
different
discusses
trends
utilization
by
emerging
technologies.
Policy and Society,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 3, 2025
Abstract
The
rapid
and
widespread
diffusion
of
generative
artificial
intelligence
(AI)
has
unlocked
new
capabilities
changed
how
content
services
are
created,
shared,
consumed.
This
special
issue
builds
on
the
2021
Policy
Society
governance
AI
by
focusing
legal,
organizational,
political,
regulatory,
social
challenges
governing
AI.
introductory
article
lays
foundation
for
understanding
underscores
its
key
risks,
including
hallucination,
jailbreaking,
data
training
validation
issues,
sensitive
information
leakage,
opacity,
control
challenges,
design
implementation
risks.
It
then
examines
AI,
such
as
governance,
intellectual
property
concerns,
bias
amplification,
privacy
violations,
misinformation,
fraud,
societal
impacts,
power
imbalances,
limited
public
engagement,
sector
need
international
cooperation.
highlights
a
comprehensive
framework
to
govern
emphasizing
adaptive,
participatory,
proactive
approaches.
articles
in
this
stress
urgency
developing
innovative
inclusive
approaches
ensure
that
development
is
aligned
with
values.
They
explore
adaptation
laws,
propose
complexity-based
approach
responsible
analyze
dominance
Big
Tech
exacerbated
developments
affects
policy
processes,
highlight
shortcomings
technocratic
broader
stakeholder
participation,
regulatory
frameworks
informed
safety
research
learning
from
other
industries,
impacts
Computers,
Journal Year:
2025,
Volume and Issue:
14(3), P. 93 - 93
Published: March 6, 2025
Machine
learning
(ML)
and
deep
(DL),
subsets
of
artificial
intelligence
(AI),
are
the
core
technologies
that
lead
significant
transformation
innovation
in
various
industries
by
integrating
AI-driven
solutions.
Understanding
ML
DL
is
essential
to
logically
analyse
applicability
identify
their
effectiveness
different
areas
like
healthcare,
finance,
agriculture,
manufacturing,
transportation.
consists
supervised,
unsupervised,
semi-supervised,
reinforcement
techniques.
On
other
hand,
DL,
a
subfield
ML,
comprising
neural
networks
(NNs),
can
deal
with
complicated
datasets
health,
autonomous
systems,
finance
industries.
This
study
presents
holistic
view
technologies,
analysing
algorithms
application’s
capacity
address
real-world
problems.
The
investigates
application
which
techniques
implemented.
Moreover,
highlights
latest
trends
possible
future
avenues
for
research
development
(R&D),
consist
developing
hybrid
models,
generative
AI,
incorporating
technologies.
aims
provide
comprehensive
on
serve
as
reference
guide
researchers,
industry
professionals,
practitioners,
policy
makers.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(12), P. 5068 - 5068
Published: June 11, 2024
With
the
deepening
of
research
on
Large
Language
Models
(LLMs),
significant
progress
has
been
made
in
recent
years
development
Multimodal
(LMMs),
which
are
gradually
moving
toward
Artificial
General
Intelligence.
This
paper
aims
to
summarize
from
LLMs
LMMs
a
comprehensive
and
unified
way.
First,
we
start
with
outline
various
conceptual
frameworks
key
techniques.
Then,
focus
architectural
components,
training
strategies,
fine-tuning
guidance,
prompt
engineering
LMMs,
present
taxonomy
latest
vision–language
LMMs.
Finally,
provide
summary
both
perspective,
make
an
analysis
status
large-scale
models
view
globalization,
offer
potential
directions
for
models.
Journal of Network and Computer Applications,
Journal Year:
2024,
Volume and Issue:
231, P. 103989 - 103989
Published: Aug. 2, 2024
The
metaverse
is
a
nascent
concept
that
envisions
virtual
universe,
collaborative
space
where
individuals
can
interact,
create,
and
participate
in
wide
range
of
activities.
Privacy
the
critical
concern
as
evolves
immersive
experiences
become
more
prevalent.
privacy
problem
refers
to
challenges
concerns
surrounding
personal
information
data
within
Virtual
Reality
(VR)
environments
shared
VR
becomes
accessible.
Metaverse
will
harness
advancements
from
various
technologies
such
Artificial
Intelligence
(AI),
Extended
(XR)
Mixed
(MR)
provide
personalized
services
its
users.
Moreover,
enable
experiences,
relies
on
collection
fine-grained
user
leads
issues.
Therefore,
before
potential
be
fully
realized,
related
must
addressed.
This
includes
safeguarding
users'
control
over
their
data,
ensuring
security
information,
protecting
in-world
actions
interactions
unauthorized
sharing.
In
this
paper,
we
explore
future
metaverses
are
expected
face,
given
reliance
AI
for
tracking
users,
creating
XR
MR
facilitating
interactions.
thoroughly
analyze
technical
solutions
differential
privacy,
Homomorphic
Encryption,
Federated
Learning
discuss
sociotechnical
issues
regarding
privacy.
