Desalination and Water Treatment,
Год журнала:
2024,
Номер
319, С. 100411 - 100411
Опубликована: Май 24, 2024
Desalination
represents
an
effective
method
for
alleviating
water
scarcity,
applying
algorithmic
techniques
to
predict
the
performance
of
reverse
osmosis
(RO)
desalination
plants,
Modified
Grey
Wolf
Optimizer
(MGWO)
based
Artificial
Neural
Networks
(ANN)
can
membrane
distillation
(MD)
equipment.
Four
experimental
inputs
are
selected:
feed
salt
concentration(35-140
g/h),
flow
rate(400-600
L/h),
evaporator
inlet
temperature
(60-80℃),
and
condenser
(20-30℃).
The
permeate
flux
(L/h
m2)
is
selected
as
output.
Ten
prediction
models
were
proposed
compared
with
existing
(ANN,
WOA-ANN,
GWO-ANN).
results
showed
that
MGWO-ANN
model-5
best
regression
results:
R2=99.3%,
mean
square
error
(MSE)=0.004.
This
model
outperformed
ANN
(R2=98.8%,
MSE=0.060),
WOA-ANN
(R2=99.1%,
MSE=0.005)
GWO-ANN
(R2=98.9%,
MSE=0.007).
Model-5
has
a
single
hidden
layer
(H=1),
13
nodes
(n=13),
10
search
agents
(SA=10),
75%-20%-05%
dataset
division.
Its
residual
within
acceptable
limits
(spanning
-0.1
0.2).
Optimizing
number
(n)
(SA)
improve
training
efficiency
accuracy
model,
capable
more
accurately
predicting
plants.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Апрель 24, 2024
Abstract
Nature-inspired
metaheuristic
algorithms
are
important
components
of
artificial
intelligence,
and
increasingly
used
across
disciplines
to
tackle
various
types
challenging
optimization
problems.
This
paper
demonstrates
the
usefulness
such
for
solving
a
variety
problems
in
statistics
using
nature-inspired
algorithm
called
competitive
swarm
optimizer
with
mutated
agents
(CSO-MA).
was
proposed
by
one
authors
its
superior
performance
relative
many
competitors
had
been
demonstrated
earlier
work
again
this
paper.
The
main
goal
is
show
typical
algorithmi,
like
CSO-MA,
efficient
tackling
different
statistics.
Our
applications
new
include
finding
maximum
likelihood
estimates
parameters
single
cell
generalized
trend
model
study
pseudotime
bioinformatics,
estimating
commonly
Rasch
education
research,
M-estimates
Cox
regression
Markov
renewal
model,
performing
matrix
completion
tasks
impute
missing
data
two
compartment
selecting
variables
optimally
an
ecology
problem
China.
To
further
demonstrate
flexibility
metaheuristics,
we
also
find
optimal
design
car
refueling
experiment
auto
industry
logistic
multiple
interacting
factors.
In
addition,
that
metaheuristics
can
sometimes
outperform
Neural Networks,
Год журнала:
2023,
Номер
170, С. 349 - 363
Опубликована: Ноя. 9, 2023
Visual
images
observed
by
humans
can
be
reconstructed
from
their
brain
activity.
However,
the
visualization
(externalization)
of
mental
imagery
is
challenging.
Only
a
few
studies
have
reported
successful
imagery,
and
visualizable
been
limited
to
specific
domains
such
as
human
faces
or
alphabetical
letters.
Therefore,
visualizing
for
arbitrary
natural
stands
significant
milestone.
In
this
study,
we
achieved
enhancing
previous
method.
Specifically,
demonstrated
that
visual
image
reconstruction
method
proposed
in
seminal
study
Shen
et
al.
(2019)
heavily
relied
on
low-level
information
decoded
could
not
efficiently
utilize
semantic
would
recruited
during
imagery.
To
address
limitation,
extended
Bayesian
estimation
framework
introduced
assistance
into
it.
Our
successfully
both
seen
(i.e.,
those
eye)
imagined
Quantitative
evaluation
showed
our
identify
highly
accurately
compared
chance
accuracy
(seen:
90.7%,
imagery:
75.6%,
accuracy:
50.0%).
contrast,
only
64.3%,
50.4%).
These
results
suggest
provide
unique
tool
directly
investigating
subjective
contents
illusions,
hallucinations,
dreams.
Biomimetics,
Год журнала:
2024,
Номер
9(3), С. 130 - 130
Опубликована: Фев. 21, 2024
In
numerous
scientific
disciplines
and
practical
applications,
addressing
optimization
challenges
is
a
common
imperative.
Nature-inspired
algorithms
represent
highly
valuable
pragmatic
approach
to
tackling
these
complexities.
This
paper
introduces
Dendritic
Growth
Optimization
(DGO),
novel
algorithm
inspired
by
natural
branching
patterns.
DGO
offers
solution
for
intricate
problems
demonstrates
its
efficiency
in
exploring
diverse
spaces.
The
has
been
extensively
tested
with
suite
of
machine
learning
algorithms,
deep
metaheuristic
the
results,
both
before
after
optimization,
unequivocally
support
proposed
algorithm’s
feasibility,
effectiveness,
generalizability.
Through
empirical
validation
using
established
datasets
like
diabetes
breast
cancer,
consistently
enhances
model
performance
across
various
domains.
Beyond
working
experimental
analysis,
DGO’s
wide-ranging
applications
learning,
logistics,
engineering
solving
real-world
have
highlighted.
study
also
considers
implications
implementing
multiple
scenarios.
As
remains
crucial
research
industry,
emerges
as
promising
avenue
innovation
problem
solving.
Artificial Intelligence Review,
Год журнала:
2024,
Номер
57(8)
Опубликована: Июль 12, 2024
Abstract
Accurate
and
rapid
disease
detection
is
necessary
to
manage
health
problems
early.
Rapid
increases
in
data
amount
dimensionality
caused
challenges
many
disciplines,
with
the
primary
issues
being
high
computing
costs,
memory
low
accuracy
performance.
These
will
arise
since
Machine
Learning
(ML)
classifiers
are
mostly
used
these
fields.
However,
noisy
irrelevant
features
have
an
impact
on
ML
accuracy.
Therefore,
choose
best
subset
of
decrease
data,
Metaheuristics
(MHs)
optimization
algorithms
applied
Feature
Selection
(FS)
using
various
modalities
medical
imaging
or
datasets
different
dimensions.
The
review
starts
by
giving
a
general
overview
approaches
AI
algorithms,
followed
MH
for
healthcare
applications,
analysis
MHs
boosted
wide
range
research
databases
as
source
access
numerous
field
publications.
final
section
this
discusses
facing
application
development.
