Neural Computing and Applications,
Год журнала:
2024,
Номер
36(20), С. 12483 - 12503
Опубликована: Апрель 22, 2024
Abstract
Visual
inspection
of
defective
tires
post-production
is
vital
for
human
safety,
as
faulty
can
lead
to
explosions,
accidents,
and
loss
life.
With
the
advancement
technology,
transfer
learning
(TL)
plays
an
influential
role
in
many
computer
vision
applications,
including
tire
defect
detection
problem.
However,
automatic
difficult
two
reasons.
The
first
presence
complex
anisotropic
multi-textured
rubber
layers.
Second,
there
no
standard
X-ray
image
dataset
use
detection.
In
this
study,
a
TL-based
model
proposed
using
new
from
global
company.
First,
we
collected
labeled
consisting
3366
images
20,000
qualified
tires.
Although
covers
15
types
defects
arising
different
design
patterns,
our
primary
focus
on
binary
classification
detect
or
absence
defects.
This
challenging
was
split
into
70,
15,
15%
training,
validation,
testing,
respectively.
Then,
nine
common
pre-trained
models
were
fine-tuned,
trained,
tested
dataset.
These
are
Xception,
InceptionV3,
VGG16,
VGG19,
ResNet50,
ResNet152V2,
DenseNet121,
InceptionResNetV2,
MobileNetV2.
results
show
that
fine-tuned
DenseNet21
InceptionNet
achieve
compatible
with
literature.
Moreover,
Xception
outperformed
compared
TL
literature
methods
terms
recall,
precision,
accuracy,
F1
score.
it
achieved
testing
73.7,
88,
80.2,
94.75%
score,
respectively,
validation
73.3,
90.24,
80.9,
95%
IEEE Transactions on Systems Man and Cybernetics Systems,
Год журнала:
2024,
Номер
54(4), С. 2192 - 2204
Опубликована: Янв. 9, 2024
Multivariate
time
series
classification
(MTSC)
based
on
deep
learning
(DL)
has
attracted
increasingly
more
research
attention.
The
performance
of
a
DL-based
MTSC
algorithm
is
heavily
dependent
the
quality
learned
representations
providing
semantic
information
for
downstream
tasks,
e.g.,
classification.
Hence,
model's
representation
ability
critical
enhancing
its
performance.
This
article
proposes
densely
knowledge-aware
network
(DKN)
MTSC.
DKN's
feature
extractor
consists
residual
multihead
convolutional
(ResMulti)
and
transformer-based
(Trans),
called
ResMulti-Trans.
ResMulti
five
blocks
capturing
local
patterns
data
while
Trans
three
transformer
extracting
global
data.
Besides,
to
enable
dense
mutual
supervision
between
lower-and
higher-level
information,
this
adapts
dual
self-distillation
(DDSD)
mining
rich
regularizations
relationships
hidden
in
Experimental
results
show
that
compared
with
5
state-of-the-art
variants,
proposed
DDSD
obtains
13/4/13
terms
"win"/"tie"/"lose"
gains
lowest-AVG_rank
score.
In
particular,
pure
ResMulti-Trans,
DKN
20/1/9
regarding
win/tie/lose.
Last
but
not
least,
overweighs
18
existing
algorithms
10
UEA2018
datasets
achieves
Recent
studies
state
that,
for
a
person
with
autism
spectrum
disorder,
learning
and
improvement
is
often
seen
in
environments
where
technological
tools
are
involved.
A
robot
an
excellent
tool
to
be
used
therapy
teaching.
It
can
transform
teaching
methods,
not
just
the
classrooms
but
also
in-house
clinical
practices.
With
rapid
advancement
deep
techniques,
robots
became
more
capable
of
handling
human
behaviour.
In
this
paper,
we
present
cost-efficient,
socially
designed
called
‘Tinku’,
developed
assist
special
needs
children.
‘Tinku’
low
cost
full
features
has
ability
produce
human-like
expressions.
Its
design
inspired
by
widely
accepted
animated
character
‘WALL-E’.
capabilities
include
offline
speech
processing
computer
vision—we
light
object
detection
models,
such
as
Yolo
v3-tiny
single
shot
detector
(SSD)—for
obstacle
avoidance,
non-verbal
communication,
expressing
emotions
anthropomorphic
way,
etc.
uses
onboard
technique
localize
objects
scene
information
semantic
perception.
We
have
several
lessons
training
using
these
features.
sample
lesson
about
brushing
discussed
show
robot’s
capabilities.
Tinku
cute,
loaded
lots
features,
management
all
processes
mind-blowing.
supervision
experts
its
condition
application
taken
care
of.
small
survey
on
appearance
discussed.
More
importantly,
it
tested
children
acceptance
technology
compatibility
terms
voice
interaction.
helps
autistic
kids
state-of-the-art
models.
Autism
Spectral
disorders
being
increasingly
identified
today’s
world.
The
that
prone
interact
comfortably
than
instructor.
To
fulfil
demand,
presented
cost-effective
solution
form
some
common
autism-affected
child.
Technology in Society,
Год журнала:
2024,
Номер
78, С. 102662 - 102662
Опубликована: Июль 17, 2024
This
paper
explores
the
effects
of
integrating
Generative
Artificial
Intelligence
(GAI)
into
decision-making
processes
within
organizations,
employing
a
quasi-experimental
pretest-posttest
design.
The
study
examines
synergistic
interaction
between
Human
(HI)
and
GAI
across
four
group
scenarios
three
global
organizations
renowned
for
their
cutting-edge
operational
techniques.
research
progresses
through
several
phases:
identifying
problems,
collecting
baseline
data
on
decision-making,
implementing
AI
interventions,
evaluating
outcomes
post-intervention
to
identify
shifts
in
performance.
results
demonstrate
that
effectively
reduces
human
cognitive
burdens
mitigates
heuristic
biases
by
offering
data-driven
support
predictive
analytics,
grounded
System
2
reasoning.
is
particularly
valuable
complex
situations
characterized
unfamiliarity
information
overload,
where
intuitive,
1
thinking
less
effective.
However,
also
uncovers
challenges
related
integration,
such
as
potential
over-reliance
technology,
intrinsic
'out-of-the-box'
without
contextual
creativity.
To
address
these
issues,
this
proposes
an
innovative
strategic
framework
HI-GAI
collaboration
emphasizes
transparency,
accountability,
inclusiveness.
Results in Engineering,
Год журнала:
2024,
Номер
21, С. 102002 - 102002
Опубликована: Март 1, 2024
The
efficient
design
of
heat
sinks
is
a
severe
challenge
in
thermo-fluid
engineering.
A
creative
and
innovative
way
applying
lateral
perforations
to
parallel
finned
sinks.
significance
achieving
an
optimal
for
perforated
(PFHSs)
has
inspired
the
present
authors
introduce
novel
hybrid
designing
approach
that
combines
computational
fluid
dynamics
(CFD),
machine
learning
(ML),
multi-objective
optimization
(MOO),
multi-criteria
decision-making
(MCDM).
variables
considered
include
size
(0.25<φ
<
0.5)
shape
(square,
circular,
hexagonal)
perforations,
as
well
airflow
Reynolds
number
(2000