Frontiers in Artificial Intelligence,
Journal Year:
2024,
Volume and Issue:
7
Published: June 17, 2024
Introduction
Computerized
sentiment
detection,
based
on
artificial
intelligence
and
computer
vision,
has
become
essential
in
recent
years.
Thanks
to
developments
deep
neural
networks,
this
technology
can
now
account
for
environmental,
social,
cultural
factors,
as
well
facial
expressions.
We
aim
create
more
empathetic
systems
various
purposes,
from
medicine
interpreting
emotional
interactions
social
media.
Methods
To
develop
technology,
we
combined
authentic
images
databases,
including
EMOTIC
(ADE20K,
MSCOCO),
EMODB_SMALL,
FRAMESDB,
train
our
models.
developed
two
sophisticated
algorithms
learning
techniques,
DCNN
VGG19.
By
optimizing
the
hyperparameters
of
models,
analyze
context
body
language
improve
understanding
human
emotions
images.
merge
26
discrete
categories
with
three
continuous
dimensions
identify
context.
The
proposed
pipeline
is
completed
by
fusing
Results
adjusted
parameters
outperform
previous
methods
capturing
different
contexts.
Our
study
showed
that
Sentiment_recognition_model
VGG19_contexte
increased
mAP
42.81%
44.12%,
respectively,
surpassing
results
studies.
Discussion
This
groundbreaking
research
could
significantly
contextual
emotion
recognition
implications
these
promising
are
far-reaching,
extending
diverse
fields
such
robotics,
affective
computing,
human-machine
interaction,
human-robot
communication.
Sensors,
Journal Year:
2023,
Volume and Issue:
23(14), P. 6459 - 6459
Published: July 17, 2023
Drowsy
driving
can
significantly
affect
performance
and
overall
road
safety.
Statistically,
the
main
causes
are
decreased
alertness
attention
of
drivers.
The
combination
deep
learning
computer-vision
algorithm
applications
has
been
proven
to
be
one
most
effective
approaches
for
detection
drowsiness.
Robust
accurate
drowsiness
systems
developed
by
leveraging
learn
complex
coordinate
patterns
using
visual
data.
Deep
algorithms
have
emerged
as
powerful
techniques
because
their
ability
automatically
from
given
inputs
feature
extractions
raw
Eye-blinking-based
was
applied
in
this
study,
which
utilized
analysis
eye-blink
patterns.
In
we
used
custom
data
model
training
experimental
results
were
obtained
different
candidates.
blinking
eye
mouth
region
coordinates
applying
landmarks.
rate
eye-blinking
changes
shape
analyzed
measuring
landmarks
with
real-time
fluctuation
representations.
An
performed
real
time
proved
existence
a
correlation
between
yawning
closed
eyes,
classified
drowsy.
95.8%
accuracy
drowsy-eye
detection,
97%
open-eye
0.84%
0.98%
right-sided
falling,
100%
left-sided
falling.
Furthermore,
proposed
method
allowed
analysis,
where
threshold
served
separator
into
two
classes,
“Open”
“Closed”
states.
Multimedia Systems,
Journal Year:
2024,
Volume and Issue:
30(3)
Published: April 6, 2024
Abstract
In
recent
years,
emotion
recognition
has
received
significant
attention,
presenting
a
plethora
of
opportunities
for
application
in
diverse
fields
such
as
human–computer
interaction,
psychology,
and
neuroscience,
to
name
few.
Although
unimodal
methods
offer
certain
benefits,
they
have
limited
ability
encompass
the
full
spectrum
human
emotional
expression.
contrast,
Multimodal
Emotion
Recognition
(MER)
delivers
more
holistic
detailed
insight
into
an
individual's
state.
However,
existing
multimodal
data
collection
approaches
utilizing
contact-based
devices
hinder
effective
deployment
this
technology.
We
address
issue
by
examining
potential
contactless
techniques
MER.
our
tertiary
review
study,
we
highlight
unaddressed
gaps
body
literature
on
Through
rigorous
analysis
MER
studies,
identify
modalities,
specific
cues,
open
datasets
with
unique
modality
combinations.
This
further
leads
us
formulation
comparative
schema
mapping
requirements
given
scenario
combination.
Subsequently,
discuss
implementation
Contactless
(CMER)
systems
use
cases
help
which
serves
evaluation
blueprint.
Furthermore,
paper
also
explores
ethical
privacy
considerations
concerning
employment
proposes
key
principles
addressing
concerns.
The
investigates
current
challenges
future
prospects
field,
offering
recommendations
research
development
CMER.
Our
study
resource
researchers
practitioners
field
recognition,
well
those
intrigued
broader
outcomes
rapidly
progressing
Sensors,
Journal Year:
2023,
Volume and Issue:
23(16), P. 7078 - 7078
Published: Aug. 10, 2023
Fire
incidents
occurring
onboard
ships
cause
significant
consequences
that
result
in
substantial
effects.
Fires
on
can
have
extensive
and
severe
wide-ranging
impacts
matters
such
as
the
safety
of
crew,
cargo,
environment,
finances,
reputation,
etc.
Therefore,
timely
detection
fires
is
essential
for
quick
responses
powerful
mitigation.
The
study
this
research
paper
presents
a
fire
technique
based
YOLOv7
(You
Only
Look
Once
version
7),
incorporating
improved
deep
learning
algorithms.
architecture,
with
an
E-ELAN
(extended
efficient
layer
aggregation
network)
its
backbone,
serves
basis
our
system.
Its
enhanced
feature
fusion
makes
it
superior
to
all
predecessors.
To
train
model,
we
collected
4622
images
various
ship
scenarios
performed
data
augmentation
techniques
rotation,
horizontal
vertical
flips,
scaling.
Our
through
rigorous
evaluation,
showcases
capabilities
recognition
improve
maritime
safety.
proposed
strategy
successfully
achieves
accuracy
93%
detecting
minimize
catastrophic
incidents.
Objects
having
visual
similarities
may
lead
false
prediction
by
but
be
controlled
expanding
dataset.
However,
model
utilized
real-time
detector
challenging
environments
small-object
detection.
Advancements
models
hold
potential
enhance
measures,
exhibits
potential.
Experimental
results
proved
method
used
protection
monitoring
port
areas.
Finally,
compared
performance
those
recently
reported
fire-detection
approaches
employing
widely
matrices
test
classification
achieved.
Engineering Applications of Artificial Intelligence,
Journal Year:
2023,
Volume and Issue:
130, P. 107708 - 107708
Published: Dec. 14, 2023
The
aim
of
this
paper
is
to
investigate
emotion
recognition
using
a
multimodal
approach
that
exploits
convolutional
neural
networks
(CNNs)
with
multiple
input.
Multimodal
approaches
allow
different
modalities
cooperate
in
order
achieve
generally
better
performances
because
features
are
extracted
from
pieces
information.
In
work,
the
facial
frames,
optical
flow
computed
consecutive
and
Mel
Spectrograms
(from
word
melody)
videos
combined
together
ways
understand
which
modality
combination
works
better.
Several
experiments
run
on
models
by
first
considering
one
at
time
so
good
accuracy
results
found
each
modality.
Afterward,
concatenated
create
final
model
allows
inputs.
For
datasets
used
BAUM-1
((Bahçeşehir
University
Affective
Database
-
1)
RAVDESS
(Ryerson
Audio–Visual
Emotional
Speech
Song),
both
collect
two
distinguished
sets
based
intensity
expression,
acted/strong
or
spontaneous/normal,
providing
representations
following
emotional
states
will
be
taken
into
consideration:
angry,
disgust,
fearful,
happy
sad.
proposed
shown
through
some
confusion
matrices,
demonstrating
than
compared
proposals
literature.
best
achieved
dataset
about
95%,
while
it
95.5%.
Frontiers in Psychiatry,
Journal Year:
2025,
Volume and Issue:
15
Published: Jan. 7, 2025
Empathy
is
a
cornerstone
in
psychotherapy
for
building
trust,
connection,
and
understanding
between
therapist
client.
Studies
meta-analyses
continue
to
support
that
empathy
significantly
correlates
with
positive
therapeutic
outcomes
(Elliott
et
al.,
2018;Garfield
&
Bergin,
1971;Watson
2014).
However,
not
the
sole
pathway
psychological
change.
Constructs
such
as
validation,
autonomy
support,
attunement,
authentic
curiosity
also
contribute
recovery
mental
well-being
(Soto,
2017).
There
are
recent
development
importance
of
some
non-interpersonal
methods,
including
training
mindfulness,
expressive
writing,
focusing,
computer-aided
cognitive
bias
modification;
these,
too
have
produced
changes
favorable
outcome.
(Schnur
Montgomery,
2010).Given
this
multi-psychological
framework,
how
essential
core
construct
from
which
interventions
take
part
remains
moot
debate.
The
role
powerful
influential
but
only
whole
net
mechanisms
(Voutilainen
2018).
This
paper
discusses
special
significance
change,
its
limitations,
risks
associated
misrepresentations
by
AI.
It
postulates
AI's
strengths
may
be
better
utilized
enhancement
non-empathic
pathways
hence
provides
an
alternative
focus
AI
health
care.Empathy
has
traditionally
been
regarded
backbone
relationship.
multicomponent
concept
involving
emotional
resonance
or
sharing
feelings,
perspectivetaking
another's
viewpoint,
compassionate
action
taking
steps
alleviate
distress
(Jordan,
2000).
These
dimensions
enable
offer
environment
noncritical
safe.
there
emerging
body
research
questions
whether
determinant
Instead,
other
factors
equally,
if
more,
important
(Garrote-Caparrós
2023;Schnur
2010).
For
example,
validation
confirms
client's
feelings
experiences
effort
establish
sense
trust
reduce
isolation.
Similarly,
promoting
support-for
instance,
encouraging
clients
responsibility
their
own
healing
process-promotes
long-term
aligns
modern
models
centered
around
client
(Steiger
therapist's
attunement-approach,
he
himself
state,
improves
rapport.
conveyed
interest
exploratory
promote
self-reflection
insight
(Feiner-Homer,
2016;Seikkula
2015).Beyond
interpersonal
mechanisms,
very
empathy-free
An
example
mindfulness-based
interventions:
MBSR
proved
successful
reducing
stress
enhancing
regulation
mood
(Ghawadra
2019).Experiences
writing
about
events
active
processing
consequences
(Mordechay
2019).
On
hand,
even
spots
corrects
negative
thinking
so
address
symptoms
anxiety
depression
(Hallion
Ruscio,
2011).
Gendlin's
focusing
training,
emphasizes
awareness
processing,
tested
found
effective
intervention
(Hinterkopf,
1983).
underpin,
together,
multifaceted
nature
change
emphasize
needs
augment
rather
try
replace
pathways.Artificial
feature
AI,
whereby
it
able
recognize
then
simulate
empathic
responses
based
on
data
text,
tone,
facial
expressions
(Asada,
2015).
While
indeed
great
achievement
technology,
lacks
depth,
intentionality,
cultural
sensitivity,
ingredients
(Tubadji
Huang,
2023;Zhang
2024).
limitations
appear
most
manifestly
three
areas:
First,
struggles
contextual
since
cannot
holistic
individual's
life
experiences,
because
unable
meaning
context,
would
first
limit
faces.
second
one
insensitivity,
algorithms
emotion
recognition
quick
misinterpret
simplify
cues
across
different
contexts.
Finally,
resonance,
draw
lived
service
deeper
connections
clients.
relying
emulating
care.Despite
these
setbacks,
argued
through
simulation
empathy,
democratizes
care
insofar
increases
access
services
(Balasubramanian
2023).
systems
can
provide
immediate
serve
entry
points
those
who
feel
uneasy
traditional
therapy
(Lopes
Poorly
aligned
over-and-over
robotic
could
alienate
destroy
any
might
needed
relationship
(McParlin
2022).
Given
risks,
perhaps
should
shift
than
trying
emulate
by,
giving
real-time,
feedback
generating
personalized
insights
human
therapists.Future
priorities
must
addressed
securing
position
developing
responsible
health.
Development
multi-modal
combine
evidence
speech,
expressions,
physiological
signals
approach
go
emotions
holistically
(Mamieva
With
effectiveness,
empirical
study
hybrid
assessed
regarding
impacts
satisfaction
will
immensely
useful
trusting
informing
user-friendly
system
designs
while
perceptions
therapy.
Furthermore,
refinement
ethical
guidelines,
requirement
challenges
privacy,
transparency,
consent
(Alfano
Lastly,
roles
well
investigated
include
monitoring
progress,
personalization
treatment
plans,
supporting
mindfulness
potential
extend
utility
minimizing
risks.While
psychotherapy,
neither
nor
irreplaceable
Evidence
underlines
effective,
training.
risk
misinterpreting
focused
models.
Coupled
developments,
enhance
delivery
without
losing
human-centered
approach.
In
future,
envisaged
act
substitute
ally
solving
rapidly
increasing
demand
services.
Future Internet,
Journal Year:
2023,
Volume and Issue:
15(9), P. 297 - 297
Published: Sept. 1, 2023
In
the
rapidly
evolving
landscape
of
internet
usage,
ensuring
robust
cybersecurity
measures
has
become
a
paramount
concern
across
diverse
fields.
Among
numerous
cyber
threats,
denial
service
(DoS)
and
distributed
(DDoS)
attacks
pose
significant
risks,
as
they
can
render
websites
servers
inaccessible
to
their
intended
users.
Conventional
intrusion
detection
methods
encounter
substantial
challenges
in
effectively
identifying
mitigating
these
due
widespread
nature,
intricate
patterns,
computational
complexities.
However,
by
harnessing
power
deep
learning-based
techniques,
our
proposed
dense
channel-spatial
attention
model
exhibits
exceptional
accuracy
detecting
classifying
DoS
DDoS
attacks.
The
successful
implementation
framework
addresses
posed
imbalanced
data
its
potential
for
real-world
applications.
By
leveraging
mechanism,
precisely
identify
classify
attacks,
bolstering
defenses
servers.
high
rates
achieved
different
datasets
reinforce
robustness
approach,
underscoring
efficacy
enhancing
capabilities.
As
result,
holds
promise
scenarios,
contributing
ongoing
efforts
safeguard
against
threats
an
increasingly
interconnected
digital
landscape.
Comparative
analysis
with
current
reveals
superior
performance
model.
We
99.38%,
99.26%,
99.43%
Bot-IoT,
CICIDS2017,
UNSW_NB15
datasets,
respectively.
These
remarkable
results
demonstrate
capability
approach
accurately
detect
various
types
assaults.
inherent
strengths
learning,
such
pattern
recognition
feature
extraction,
overcomes
limitations
traditional
methods,
efficiency
systems.
Sensors,
Journal Year:
2023,
Volume and Issue:
23(14), P. 6640 - 6640
Published: July 24, 2023
Understanding
and
identifying
emotional
cues
in
human
speech
is
a
crucial
aspect
of
human-computer
communication.
The
application
computer
technology
dissecting
deciphering
emotions,
along
with
the
extraction
relevant
characteristics
from
speech,
forms
significant
part
this
process.
objective
study
was
to
architect
an
innovative
framework
for
emotion
recognition
predicated
on
spectrograms
semantic
feature
transcribers,
aiming
bolster
performance
precision
by
acknowledging
conspicuous
inadequacies
extant
methodologies
rectifying
them.
To
procure
invaluable
attributes
detection,
investigation
leveraged
two
divergent
strategies.
Primarily,
wholly
convolutional
neural
network
model
engaged
transcribe
spectrograms.
Subsequently,
cutting-edge
Mel-frequency
cepstral
coefficient
abstraction
approach
adopted
integrated
Speech2Vec
encoding.
These
dual
underwent
individual
processing
before
they
were
channeled
into
long
short-term
memory
comprehensive
connected
layer
supplementary
representation.
By
doing
so,
we
aimed
sophistication
efficacy
our
detection
model,
thereby
enhancing
its
potential
accurately
recognize
interpret
speech.
proposed
mechanism
rigorous
evaluation
process
employing
distinct
databases:
RAVDESS
EMO-DB.
outcome
displayed
predominant
when
juxtaposed
established
models,
registering
impressive
accuracy
94.8%
dataset
commendable
94.0%
EMO-DB
dataset.
This
superior
underscores
system
realm
recognition,
as
it
outperforms
current
frameworks
metrics.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(10), P. 4199 - 4199
Published: May 15, 2024
Emotion
detection
holds
significant
importance
in
facilitating
human–computer
interaction,
enhancing
the
depth
of
engagement.
By
integrating
this
capability,
we
pave
way
for
forthcoming
AI
technologies
to
possess
a
blend
cognitive
and
emotional
understanding,
bridging
divide
between
machine
functionality
human
complexity.
This
progress
has
potential
reshape
how
machines
perceive
respond
emotions,
ushering
an
era
empathetic
intuitive
artificial
systems.
The
primary
research
challenge
involves
developing
models
that
can
accurately
interpret
analyze
emotions
from
both
auditory
textual
data,
whereby
data
require
optimizing
CNNs
detect
subtle
intense
fluctuations
speech,
necessitate
access
large,
diverse
datasets
effectively
capture
nuanced
cues
written
language.
paper
introduces
novel
approach
multimodal
emotion
recognition,
seamlessly
speech
text
modalities
infer
states.
Employing
CNNs,
meticulously
using
Mel
spectrograms,
while
BERT-based
model
processes
component,
leveraging
its
bidirectional
layers
enable
profound
semantic
comprehension.
outputs
are
combined
attention-based
fusion
mechanism
optimally
weighs
their
contributions.
proposed
method
here
undergoes
meticulous
testing
on
two
distinct
datasets:
Carnegie
Mellon
University’s
Multimodal
Opinion
Sentiment
Intensity
(CMU-MOSEI)
dataset
Lines
Dataset
(MELD).
results
demonstrate
superior
efficacy
compared
existing
frameworks,
achieving
accuracy
88.4%
F1-score
87.9%
CMU-MOSEI
dataset,
notable
weighted
(WA)
67.81%
F1
(WF1)
score
66.32%
MELD
dataset.
comprehensive
system
offers
precise
several
advancements
field.