IEEE Access,
Год журнала:
2024,
Номер
12, С. 62341 - 62357
Опубликована: Янв. 1, 2024
The
identification
of
suitable
feature
subsets
from
High-Dimensional
Low-Sample-Size
(HDLSS)
data
is
paramount
importance
because
this
dataset
often
contains
numerous
redundant
and
irrelevant
features,
leading
to
poor
classification
performance.
However,
the
selection
an
optimal
subset
a
vast
space
creates
significant
computational
challenge.
In
domain
HDLSS
data,
conventional
methods
face
challenges
in
achieving
balance
between
reducing
number
features
preserving
high
accuracy.
Addressing
these
issues,
study
introduces
effective
framework
that
employs
filter
wrapper-based
strategy
specifically
designed
address
inherent
data.
adopts
multi-step
approach
where
ensemble
integrates
five
ranking
approaches:
Chi-square
(χ
2
),
Gini
index
(GI),
F-score,
Mutual
Information
(MI),
Symmetric
uncertainty
(SU)
identify
top-ranking
features.
subsequent
stage,
search
method
utilized,
which
Differential
Evaluation
(DE)
metaheuristic
algorithm
as
strategy.
fitness
during
assessed
based
on
weighted
combination
error
rate
Support
Vector
Machine
(SVM)
classifier
cardinality
subset.
datasets,
now
with
reduced
dimensionality,
are
subsequently
employed
build
models
SVM,
K-Nearest
Neighbors
(KNN),
Logistic
Regression
(LR).The
proposed
was
evaluated
13
datasets
assess
its
efficacy
selecting
appropriate
improving
Classification
Accuracy
(ACC)
analog
Area
Under
Curve
(AUC).The
produces
smaller
(ranging
2
9
for
all
datasets),
while
maintaining
commendable
average
AUC
ACC
(between
98%
100%).
comparative
results
demonstrate
outperforms
both
non-feature
approaches
terms
ACC.
Furthermore,
when
compared
several
other
state-of-the-art
approaches,
exhibits
ACS ES&T Water,
Год журнала:
2021,
Номер
1(12), С. 2531 - 2540
Опубликована: Ноя. 19, 2021
Wastewater
treatment
plants
(WWTPs)
can
account
for
up
to
1%
of
a
country's
energy
consumption.
Meanwhile,
WWTPs
have
high
energy-saving
potential.
To
achieve
this,
it
is
necessary
establish
appropriate
consumption
models
WWTPs.
Several
recent
been
developed
using
logarithmic,
exponential,
or
linear
functions.
However,
the
behavior
non-linear
and
difficult
fit
with
simple
functions,
particularly
non-numerical
variables.
Thus,
traditional
modeling
methods
cannot
effectively
describe
relationship
between
water
in
Therefore,
machine
learning
method
was
adopted
this
study
investigate
WWTPs;
novel
model
variable
(discharge
standard)
random
forest
algorithm.
The
also
predict
after
upgrading
discharge
standards.
We
found
that
unit
electricity
exhibited
an
average
increase
17%
effluent
standard
increased
from
class
I
B
A
(as
per
China's
classification).
correlation
coefficient
0.702.
provide
better
understanding
efficiency
Environmental Science & Technology,
Год журнала:
2021,
Номер
56(2), С. 984 - 994
Опубликована: Дек. 23, 2021
The
goal
of
this
research
was
to
identify
functional
groups
that
determine
rates
micropollutant
(MP)
biotransformations
performed
by
wastewater
microbial
communities.
To
meet
goal,
we
a
series
incubation
experiments
seeded
with
four
independent
communities
and
spiked
them
mixture
40
structurally
diverse
MPs.
We
collected
samples
over
time
used
high-resolution
mass
spectrometry
estimate
biotransformation
rate
constants
for
each
MP
in
experiment
propose
structures
46
products.
then
developed
random
forest
models
classify
the
based
on
presence
specific
or
observed
biotransformations.
extracted
classification
importance
metrics
from
model
compared
across
Our
analysis
revealed
30
define
as
either
promoters,
inhibitors,
structural
features
can
be
biotransformed
uncharacterized
community,
are
not
rate-determining.
experimental
data
provide
novel
insights
into
more
accurately
predict
inform
design
new
chemical
products
may
readily
biodegradable
during
treatment.
International Journal of Applied Engineering and Management Letters,
Год журнала:
2024,
Номер
unknown, С. 67 - 100
Опубликована: Фев. 28, 2024
Purpose:
This
paper
aims
to
discover
the
dynamic
landscape
of
Information
Communication
and
Computation
Technologies
(ICCT)
within
agriculture
environmental
information
management,
elucidating
their
evolutionary
trajectory
resonance
Society
5.0
principles
in
fostering
innovative
solutions.
By
scrutinizing
core
technologies
constituting
ICCT
these
sectors,
it
endeavours
shed
light
on
potential
for
integration
framework
5.0,
contemplating
both
possibilities
challenges
inherent
this
convergence.
Methodology:
exploratory
chapter
delves
into
evolving
its
pivotal
emphasis
address
complex
management.
Analysis/Results:
The
provides
a
background
evolution
establishes
rationale
exploring
role
advancing
Agricultural
Environmental
Systems
transformative
societal
framework.
are
explored
through
IoT
applications
precision
agriculture,
impact
blockchain
agricultural
supply
chains,
utilization
remote
sensing
Earth
observation
systems
along
with
data
analytics
insights.
further
investigates
systems,
unveiling
how
support
smart
farming
practices,
citizen
engagement
decision-making,
sustainable
resource
Case
studies
highlight
successful
implementations
underscoring
best
practices
lessons
learned.
Emerging
trends
science
explored,
providing
insights
future
developments.
Originality/Value:
Through
lens
case
showcasing
implementations,
seeks
distill
key
insights,
while
also
conducting
forward-looking
assessment
emerging
applications,
thus
contributing
deeper
understanding
shaping
paradigms
context
future.
Type
Paper:
Exploratory
analysis.
Environmental Science & Technology,
Год журнала:
2022,
Номер
56(12), С. 7544 - 7552
Опубликована: Май 12, 2022
Environmental
health
sciences
(EHS)
span
many
diverse
disciplines.
Within
the
EHS
community,
National
Institute
of
Health
Sciences
Superfund
Research
Program
(SRP)
funds
multidisciplinary
research
aimed
to
address
pressing
and
complex
issues
on
how
people
are
exposed
hazardous
substances
their
related
consequences
with
goal
identifying
strategies
reduce
exposures
protect
human
health.
While
disentangling
interrelationships
that
contribute
environmental
effects
over
course
life
remains
difficult,
advances
in
data
science
sharing
offer
a
path
forward
explore
across
disciplines
reveal
new
insights.
Multidisciplinary
SRP-funded
teams
well-positioned
examine
best
integrate
domains
multifaceted
problems.
As
such,
SRP
supported
collaborative
projects
designed
foster
enhance
interoperability
reuse
streams.
This
perspective
synthesizes
those
experiences
as
landscape
view
challenges
identified
while
working
increase
FAIR-ness
(Findable,
Accessible,
Interoperable,
Reusable)
opportunities
them.
Environmental Science & Technology Letters,
Год журнала:
2023,
Номер
10(11), С. 1052 - 1058
Опубликована: Май 23, 2023
Per-
and
polyfluoroalkyl
substances
(PFASs)
are
a
class
of
environmental
contaminants
that
originate
from
various
sources.
The
unique
chemical
fingerprints
associated
with
many
commercial
products
industrial
applications
make
PFASs
ideal
candidates
for
machine
learning
(ML)-assisted
forensics.
Here,
we
propose
novel
use
PFAS
in
fish
tissue
surface
water
systems
to
classify
exposure
multiple
sources
using
proof-of-concept
demonstration.
Three
supervised
ML
classification
techniques
(k-nearest
neighbors
(KNN),
decision
trees,
support
vector
machines)
implementing
two
predictive
features
used
literature-reported
(n
=
1057).
importance
additional
was
explored
brute
force
optimization
multifeature
KNN
algorithm.
multiclass
considered
aqueous
film-forming
foam-impacted
water,
paper
industry
wastewater,
diffuse
sources,
or
undergoing
long-range
transport.
optimized
classifiers
demonstrated
85%–94%
accuracy
this
first
known
also
79%–92%
set
independent
external
validation
data
192).
Our
results
demonstrate
may
be
an
effective
means
source
tracking
systems.
code
is
provided
guidance
on
best
practices
ML-assisted
