Enhanced user interaction in operating systems through machine learning language models
Опубликована: Июнь 13, 2024
With
the
large
language
model
showing
human-like
logical
reasoning
and
understanding
ability,
whether
agents
based
on
can
simulate
interaction
behavior
of
real
users,
so
as
to
build
a
reliable
virtual
recommendation
A/B
test
scene
help
application
research
is
an
urgent,
important
economic
value
problem.
The
combination
design
machine
learning
provide
more
efficient
personalized
user
experience
for
products
services.
This
service
meet
specific
needs
users
improve
satisfaction
loyalty.
Second,
interactive
system
understand
user's
views
product
by
providing
good
interface
experience,
then
use
algorithms
optimize
product.
iterative
optimization
process
continuously
quality
performance
changing
users.
At
same
time,
designers
need
consider
how
these
tools
be
combined
with
systems
experience.
paper
explores
potential
applications
models,
in
operating
systems.
By
integrating
technologies,
intelligent
services
provided
promote
continuous
improvement
products.
great
both
applications.
Язык: Английский
Evaluation of Impact of Image Augmentation Techniques on Two Tasks: Window Detection and Window States Detection
Results in Engineering,
Год журнала:
2024,
Номер
unknown, С. 103571 - 103571
Опубликована: Ноя. 1, 2024
Язык: Английский
Wearable device for personalized EMG feedback-based treatments
Results in Engineering,
Год журнала:
2024,
Номер
23, С. 102472 - 102472
Опубликована: Июнь 25, 2024
Curative
effects
of
electromyography
(EMG)
feedback
in
treatment
various
conditions
and/or
recovery
after
injuries
have
been
earlier
reported.
However,
wider
application
on
EMG
is
somehow
limited
due
to
the
overall
price
such
systems
and
availability
outside
specialized
centers.
Development
a
personalized
device
for
would
be
great
importance
home
stroke
or
injuries,
achieving
better
success
fitness
improving
biofeedback-based
treatments
as
urinary
incontinence.
Despite
extensive
research
signal
collection,
there
lack
focus
in-situ
analysis
that
considers
intensity
duration
muscle
activities.
This
gap
presents
motivation
our
research.
In
this
paper,
we
present
methodology
realization
wearable,
rechargeable
battery-powered,
small-sized
(90
mm
×
60
mm)
electronic
recording
two
channels
(12-bits
resolution,
sampling
frequency
up
1.6
kHz)
with
Bluetooth
Low
Energy
connectivity
smartphone.
An
average
current
consumption
20.5
mA
was
experimentally
determined,
suggesting
multiday
continuous
functionality
possible.
Advancing
state
art,
propose
cross-correlation-based
algorithm
dynamical
computing
evaluation
activation
levels.
can
determine
if
follows
predefined
profile
contractions/relaxations
(as
needed
treatment)
indicate
muscles
specific
exercise
were
not
engaged
proper
time
intensity.
The
performed
simulation
showed
proposed
approach
exhibited
shorter
processing
compared
Morlet
Wavelet
Transform
Dynamic
Time
Warping.
Finally,
experimental
work
five
human
volunteers
demonstrated
reliability
acquisition
processing.
Therefore,
main
contribution
cost-effective,
small-sized,
customizable
system
an
efficient
collection
Язык: Английский
A survey on hand gesture recognition based on surface electromyography: Fundamentals, methods, applications, challenges and future trends
Applied Soft Computing,
Год журнала:
2024,
Номер
166, С. 112235 - 112235
Опубликована: Сен. 11, 2024
Язык: Английский
Exploring the synergy of human-robot teaming, digital twins, and machine learning in Industry 5.0: a step towards sustainable manufacturing
Journal of Intelligent Manufacturing,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 19, 2025
Язык: Английский
Using machine learning algorithms for grasp strength recognition in rehabilitation planning
Results in Engineering,
Год журнала:
2023,
Номер
21, С. 101660 - 101660
Опубликована: Дек. 14, 2023
The
augmentation
of
individuals'
quality
life,
particularly
those
with
disabilities,
can
be
achieved
through
state-of-the-art
artificial
intelligence
solutions.
Machine
learning
algorithms,
known
for
their
ability
to
acquiring
knowledge
and
identify
significant
characteristics
from
diverse
datasets,
play
a
crucial
role.
In
this
investigation,
we
focused
on
classifying
various
weights
commonly
encountered
in
daily
activities
based
electromyography
(EMG)
readings,
using
multiple
distinct
machine
algorithms.
This
endeavor
involved
collection
substantial
data
cohort,
wherein
participants
assumed
arm
configurations
while
manipulating
three
objects
(specifically,
pen,
bottle,
weighty
object)
or
no
object
at
all.
sample
encompassed
50
physically
capable
healthy
participants,
an
equal
distribution
25
males
females.
muscular
activity
was
measured
utilizing
the
MYO
armband,
advanced
eight-channel
EMG
device
positioned
forearm.
After
preprocessing
data,
several
algorithms
has
been
employed
analyze
dataset.
Notably,
outcomes
demonstrate
that
K-Nearest
Neighbors
(KNN),
Random
Forest
(RF),
Decision
Tree
(DT)
emerge
as
optimal
methodologies
grip
strength
estimation,
achieving
impressive
accuracy
rates
99.23
%,
99.08
98.62
respectively.
experimental
supplementary
materials
are
available
https://github.com/arshiaeskandari/EMG-Dataset.
Язык: Английский
Hybrid Convolution based Efficient Net-based Hand Gesture Recognition Framework with Optimized Algorithm
International Journal of Pattern Recognition and Artificial Intelligence,
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 29, 2024
The
difficulties
in
communication
and
hearing
are
an
important
concern
for
deaf–dumb
people,
which
stop
access
to
their
essential
basic
needs.
Many
findings
have
been
made
address
sign
languages
even
though
this
challenging
problem
is
not
still
solved.
methods
aimed
propose
vision-based
classifiers
through
identical
pattern
investigation
tasks
by
obtaining
the
difficult
handcraft
feature
descriptions
of
gestures
from
gathered
images.
However,
efficacy
all
those
models
less
performing
with
a
huge
signbook
captured
uncontrolled
complex
background
conditions.
So,
effective
Indian
Sign
Language
(ISL)
classification
method
developed
advanced
deep
learning
approach.
At
first,
hand
gesture
images
obtained
data
source.
Only
image
hand,
complicated
background,
extracted
image.
features
using
Scale-Invariant
Feature
Transform
(SIFT)
Multiscale
Vision
Transformer
(MVT).
Then,
fed
Hybrid
Convolution-based
EfficientNet
(HCEN)
model.
hyper-parameters
HCEN
model
tuned
implemented
Adaptive
Political
Optimizer
(APO)
algorithm.
recognized
signs
suggested
Various
experiments
conducted
determine
performance
learning-based
recognition
Язык: Английский