The
blood
diagnosis
of
diabetes
mellitus
(DM)
is
accurate,
but
invasive.
Attenuated
Total
Reflectance
by
Fourier
Transform
Infrared
Spectroscopy
(ATR-FTIR)
a
green
technology
adopted
in
the
detection
several
diseases
resulting
non-invasive
and
accurate
diagnosis.
analysis
ATR-FTIR
data
using
deep
learning
techniques
like
Convolutional
Neural
Network
(CNN)
promising.
However,
challenges
to
find
optimized
architectures
are
barely
explored
literature.
In
this
paper,
we
propose
an
Evolutionary
Architecture
Search
technique
able
CNN
for
salivary
spectra
type
2
DM
Genetic
Algorithm
as
optimization
approach.
Climate,
Journal Year:
2024,
Volume and Issue:
12(8), P. 119 - 119
Published: Aug. 10, 2024
This
study
addresses
the
critical
issue
of
drought
zoning
in
Canada
using
advanced
deep
learning
techniques.
Drought,
exacerbated
by
climate
change,
significantly
affects
ecosystems,
agriculture,
and
water
resources.
Canadian
Drought
Monitor
(CDM)
data
provided
government
ERA5-Land
daily
were
utilized
to
generate
a
comprehensive
time
series
mean
monthly
precipitation
air
temperature
for
199
sample
locations
from
1979
2023.
These
processed
Google
Earth
Engine
(GEE)
environment
used
develop
Convolutional
Neural
Network
(CNN)
model
estimate
CDM
values,
thereby
filling
gaps
historical
data.
The
CanESM5
model,
as
assessed
IPCC
Sixth
Assessment
Report,
was
employed
under
four
change
scenarios
predict
future
conditions.
Our
CNN
forecasts
values
up
2100,
enabling
accurate
zoning.
results
reveal
significant
trends
changes,
indicating
areas
most
vulnerable
droughts,
while
shows
slow
increasing
trend.
analysis
indicates
that
extreme
scenarios,
certain
regions
may
experience
increase
frequency
severity
necessitating
proactive
planning
mitigation
strategies.
findings
are
policymakers
stakeholders
designing
effective
management
adaptation
programs.
The
blood
diagnosis
of
diabetes
mellitus
(DM)
is
accurate,
but
invasive.
Attenuated
Total
Reflectance
by
Fourier
Transform
Infrared
Spectroscopy
(ATR-FTIR)
a
green
technology
adopted
in
the
detection
several
diseases
resulting
non-invasive
and
accurate
diagnosis.
analysis
ATR-FTIR
data
using
deep
learning
techniques
like
Convolutional
Neural
Network
(CNN)
promising.
However,
challenges
to
find
optimized
architectures
are
barely
explored
literature.
In
this
paper,
we
propose
an
Evolutionary
Architecture
Search
technique
able
CNN
for
salivary
spectra
type
2
DM
Genetic
Algorithm
as
optimization
approach.
