Research Square (Research Square),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Oct. 3, 2023
Abstract
One
of
the
most
disruptive
emergency
situations
century,
as
seen
globally,
is
coronavirus
epidemic
and
its
quick
spread.
Clinical
image
analysis
chest
computed
tomography
(CT)images
can
be
useful
in
prevention
spread
this
virus
by
providing
a
precise
diagnosis.
Detecting
COVID-19
possible
with
use
artificial
intelligence-assisted
analysis.Hence,
deep
learning
based
technique
introduced
research
to
forecast
COVID-19.
The
CT
acquired
from
dataset
pre-processed
using
resizing
normalization
make
input
appropriate
for
further
processing.
Then,
significant
features
will
extracted
convolutional
neural
network
(CNN),
Haralick
Texture
Features,and
Histogram
Oriented
Gradient
(HOG).
Using
attributes
optimal
best
are
chosen
proposed
Chaotic
Fennec
Fox
Optimization
(CFFA)
algorithm.
selected
features,
prediction
devised
Hybrid
Attention
ResidualBiGRUNetwork
(HAR-BiNet),
which
designed
integrating
attention
module,
ResNet_152
Bidirectional
Gated
Recurrent
Unit.The
CFFA-HAR-BiNet
on
accuracy,
specificity,
precision,
recall,
F1-Measure
MSE
values
96.10%,
99.71%,
96.54%,
94.70%,
96.30%,
3.29%
respectively.
COVID,
Journal Year:
2023,
Volume and Issue:
3(1), P. 90 - 123
Published: Jan. 16, 2023
In
the
ongoing
COVID-19
pandemic,
digital
technologies
have
played
a
vital
role
to
minimize
spread
of
COVID-19,
and
control
its
pitfalls
for
general
public.
Without
such
technologies,
bringing
pandemic
under
would
been
tricky
slow.
Consequently,
exploration
status,
devising
appropriate
mitigation
strategies
also
be
difficult.
this
paper,
we
present
comprehensive
analysis
community-beneficial
that
were
employed
fight
pandemic.
Specifically,
demonstrate
practical
applications
ten
major
effectively
served
mankind
in
different
ways
during
crisis.
We
chosen
these
based
on
their
technical
significance
large-scale
adoption
arena.
The
selected
are
Internet
Things
(IoT),
artificial
intelligence(AI),
natural
language
processing(NLP),
computer
vision
(CV),
blockchain
(BC),
federated
learning
(FL),
robotics,
tiny
machine
(TinyML),
edge
computing
(EC),
synthetic
data
(SD).
For
each
technology,
working
mechanism,
context
challenges
from
perspective
COVID-19.
Our
can
pave
way
understanding
roles
COVID-19-fighting
used
future
infectious
diseases
prevent
global
crises.
Moreover,
discuss
heterogeneous
significantly
contributed
addressing
multiple
aspects
when
fed
aforementioned
technologies.
To
best
authors’
knowledge,
is
pioneering
work
transformative
with
broader
coverage
studies
applications.
Research Square (Research Square),
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 16, 2025
Abstract
Background
Given
the
unprecedented
surge
in
COVID-19
infections,
heightened
demand
for
medical
oxygen
prompted
numerous
national
and
global
initiatives
to
bridge
gap
between
supply
demand.
This
was
crucial
ensuring
adequate
treatment
patients
suffering
from
acute
respiratory
distress
syndrome
requiring
therapy.
research
aims
explore
factors
influencing
management
India
during
pandemic
beyond,
examining
both
facilitators
barriers.
Method
Through
a
thorough
review
of
literature,
secondary
research,
interviews
with
key
stakeholders,
critical
affecting
were
identified.
These
then
analyzed
using
modified
total
interpretive
structural
modeling
(m-TISM)
approach
MICMAC
(Matrice
d’
Impacts
croises
multiplication
applique
an
classment)
analysis
comprehend
their
hierarchical
relationships
driving
forces.
Results
The
study
identifies
fourteen
that
act
as
barriers
Covid-19
pandemic.
also
influence
non-pandemic
period.
development
m-TISM
model
gives
us
interrelationships
these
factors,
including
one
itself.
findings
identify
strategic
levers
strengthen
ecosystem
cross-sectoral
collaborations.
Conclusion
provides
insights
into
strengthening
ecosystem,
enabling
policymakers
program
implementers
make
informed
decisions
implement
pre-emptive
measures
address
future
threats
virus
or
similar
crises.
Indian Journal of Science and Technology,
Journal Year:
2024,
Volume and Issue:
17(12), P. 1159 - 1166
Published: March 20, 2024
Objective:
The
importance
of
this
research
article
is
to
evaluate
efficient
model
for
diagnosing
pandemic
COVID-19
positive
cases
in
Telangana
State,
India.
Method:
Neural
Network
models
(Extreme
Learning
Machine
and
Multi-Layer
Perception),
Deep
(Long
Short
Term
Memory-LSTM)
traditional
Auto
Regressive
Integrated
Moving
Average
(ARIMA)
were
applied
the
data
was
converted
from
non-linear
linear
(stationarity)
forecasting
Covid-19
cases.
study
covered
1st.
Dec
2020
30th
May
2021.
80%
train
taken
fit
then
20%
test
used
predict
values.
deviation
between
original
predicted
led
an
error.
Among
these
error
values,
which
had
minimum
errors
considered
as
best
four
models.
Findings:
LSTM
proved
be
most
model,
a
result
least
Root
mean
square
(RMSE
=
71.12)
compared
ARIMA
(258.20),
ELM
(553.67)
MLP
(641.86)
Novelty:
These
methods
succour
forthcoming
days.
This
has
been
suggested
taking
better
preventive
steps
control
Keywords:
COVID19,
ARIMA,
LSTM,
MLP,
Forecasting
