International Journal of Online and Biomedical Engineering (iJOE),
Journal Year:
2023,
Volume and Issue:
19(12), P. 49 - 61
Published: Aug. 31, 2023
This
paper
introduces
an
innovative
technique
for
creating
a
cough
detection
system
that
relies
on
speech
recognition
algorithms.
The
strategy
utilizes
the
Kaldi
platform,
which
is
open
source
and
incorporates
hybrid
of
Gaussian
Mixture
Model-based
Hidden
Markov
Models
(GMM-HMM)
through
straightforward
monophone
training
model.
Additionally,
study
examines
effectiveness
two
different
feature
extraction
approaches,
Mel
Frequency
Cepstral
Coefficient
(MFCC)
Perceptual
Linear
Prediction
(PLP).
proposed
can
function
as
collection
tool
gathering
natural
spontaneous
data
from
conversations
or
continuous
speech.
also
compares
CMU
Sphinx4
toolkits,
concluding
Kaldi’s
use
GMM-HMM
outperforms
Sphinx4.
2022 9th International Conference on Computing for Sustainable Global Development (INDIACom),
Journal Year:
2024,
Volume and Issue:
unknown, P. 488 - 492
Published: Feb. 28, 2024
Incorporating
generative
artificial
intelligence
(AI)
into
design
and
art
has
upended
established
creative
paradigms,
sparking
discussions
on
the
validity
of
AI-generated
development
non-fungible
token
(NFT)
marketplaces.
The
US
Copyright
Office
rendered
a
significant
decision
in
February
2023
that
highlights
contentious
nature
AI
work
need
human
intervention
its
commercialization.
This
paper
traces
neural
networks
examines
how
it
affected
visual
arts.
We
investigate
idea
autonomously
creating
digital
NFT
style
utilizing
adversarial
(GANs),
with
striking
results.
Our
links
deep
learning
blockchain,
enabling
to
find
place
market.
Sci,
Journal Year:
2023,
Volume and Issue:
6(1), P. 2 - 2
Published: Dec. 23, 2023
Current
advancements
in
the
technology
of
Internet
Things
(IoT)
have
led
to
proliferation
various
applications
healthcare
sector
that
use
IoT.
Recently,
it
has
been
shown
voice
signal
data
respiratory
system
(i.e.,
breathing,
coughing,
and
speech)
can
be
processed
through
machine
learning
techniques
detect
different
diseases
this
such
as
COVID-19,
considered
an
ongoing
global
pandemic.
Therefore,
paper
presents
a
new
IoT
framework
for
identification
COVID-19
based
on
breathing
samples.
Using
devices,
samples
were
captured
transmitted
cloud,
where
they
analyzed
using
naïve
Bayes
(NB)
algorithm.
In
addition,
performance
NB
algorithm
was
assessed
accuracy,
sensitivity,
specificity,
precision,
F-Measure,
G-Mean.
The
experimental
findings
showed
proposed
achieved
82.97%
75.86%
94.44%
95.65%
84.61%
84.64%
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Oct. 24, 2024
In
response
to
the
pressing
requirement
for
precise
and
easily
accessible
COVID-19
detection
methods,
we
present
Cough2COVID-19
framework,
which
is
cost-effective,
non-intrusive,
widely
accessible.
The
conventional
diagnostic
notably
PCR
test,
are
encumbered
by
limitations
such
as
cost
invasiveness.
Consequently,
exploration
of
alternative
solutions
has
gained
momentum.
Our
innovative
approach
employs
a
multi-layer
ensemble
deep
learning
(MLEDL)
framework
that
capitalizes
on
cough
audio
signals
achieve
heightened
efficiency
in
detection.
This
study
introduces
effectively
addressing
these
challenges
through
AI-driven
analysis.
Additionally,
this
proposed
CoughFeatureRanker
algorithm,
delves
into
robustness
pivotal
features
embedded
within
audios.
algorithm
selects
most
prominent
based
their
optimal
discriminatory
performance
from
15
detect
COVID-19.
effectiveness
scrutinized,
confirming
its
favorable
influence
accuracy
achieves
remarkable
outcomes
signals,
boasting
specificity
98%,
sensitivity
97%,
an
AUC
score
0.981.
asserts
supremacy
non-invasive
screening
exhaustive
comparison
with
cutting-edge
methodologies.
groundbreaking
innovation
holds
potential
enhance
urban
resilience
transforming
disease
diagnosis,
offering
significant
curtailing
transmission
risks
facilitating
timely
interventions
ongoing
battle
against
pandemic.
Ingénierie des systèmes d information,
Journal Year:
2023,
Volume and Issue:
28(2), P. 275 - 282
Published: April 30, 2023
This
paper
describes
the
clustering
technique
for
provinces-territories
in
Morocco
and
countries
of
world
at
risk
COVID-19
epidemic.Based
on
this
proposed
method,
we
have
used
Moroccan
dataset,
August
18,
2021,
with
higher
new
death
number.The
dataset
is
based
from
Worldometer
November
25,
2021.In
study,
employed
K-Means
algorithm,
Elbow
-Silhouette
Methods
statistics
analysis
using
'Confirmed
-Death'
two-dimensional
data
prefectures
-provinces
'Confirmed-Death-Recovered'
three-dimensional
countries.Our
results
show
that,
method
generated
3
prefectureprovincial
groups
Morocco,
similar
types
cases,
able
to
group
into
4
clusters,
-Death
-Recovered'
cases.Our
study
can
be
considered
as
a
model
all
countries,
COVID-19,
help
political
leaders
health
authorities
make
right
decisions.
