Electronics,
Journal Year:
2022,
Volume and Issue:
11(14), P. 2250 - 2250
Published: July 18, 2022
The
COVID-19
pandemic
disrupted
people’s
livelihoods
and
hindered
global
trade
transportation.
During
the
pandemic,
World
Health
Organization
mandated
that
masks
be
worn
to
protect
against
this
deadly
virus.
Protecting
one’s
face
with
a
mask
has
become
standard.
Many
public
service
providers
will
encourage
clients
wear
properly
in
foreseeable
future.
On
other
hand,
monitoring
individuals
while
standing
alone
one
location
is
exhausting.
This
paper
offers
solution
based
on
deep
learning
for
identifying
over
faces
places
minimize
coronavirus
community
transmission.
main
contribution
of
proposed
work
development
real-time
system
determining
whether
person
webcam
wearing
or
not.
ensemble
method
makes
it
easier
achieve
high
accuracy
considerable
strides
toward
enhancing
detection
speed.
In
addition,
implementation
transfer
pretrained
models
stringent
testing
an
objective
dataset
led
highly
dependable
inexpensive
solution.
findings
provide
validity
application’s
potential
use
real-world
settings,
contributing
reduction
Compared
existing
methodologies,
delivers
improved
accuracy,
specificity,
precision,
recall,
F-measure
performance
three-class
outputs.
These
metrics
include
recall.
An
appropriate
balance
kept
between
number
necessary
parameters
time
needed
conclude
various
models.
Electronics,
Journal Year:
2021,
Volume and Issue:
10(21), P. 2689 - 2689
Published: Nov. 3, 2021
In
the
last
few
years,
intensive
research
has
been
done
to
enhance
artificial
intelligence
(AI)
using
optimization
techniques.
this
paper,
we
present
an
extensive
review
of
neural
networks
(ANNs)
based
algorithm
techniques
with
some
famous
techniques,
e.g.,
genetic
(GA),
particle
swarm
(PSO),
bee
colony
(ABC),
and
backtracking
search
(BSA)
modern
developed
lightning
(LSA)
whale
(WOA),
many
more.
The
entire
set
such
is
classified
as
algorithms
on
a
population
where
initial
randomly
created.
Input
parameters
are
initialized
within
specified
range,
they
can
provide
optimal
solutions.
This
paper
emphasizes
enhancing
network
via
by
manipulating
its
tuned
or
training
obtain
best
structure
pattern
dissolve
problems
in
way.
includes
results
for
improving
ANN
performance
PSO,
GA,
ABC,
BSA
respectively,
parameters,
number
neurons
hidden
layers
learning
rate.
obtained
net
used
solving
energy
management
virtual
power
plant
system.
Algorithms,
Journal Year:
2020,
Volume and Issue:
13(10), P. 249 - 249
Published: Oct. 1, 2020
Several
outbreak
prediction
models
for
COVID-19
are
being
used
by
officials
around
the
world
to
make
informed
decisions
and
enforce
relevant
control
measures.
Among
standard
global
pandemic
prediction,
simple
epidemiological
statistical
have
received
more
attention
authorities,
these
popular
in
media.
Due
a
high
level
of
uncertainty
lack
essential
data,
shown
low
accuracy
long-term
prediction.
Although
literature
includes
several
attempts
address
this
issue,
generalization
robustness
abilities
existing
need
be
improved.
This
paper
presents
comparative
analysis
machine
learning
soft
computing
predict
as
an
alternative
susceptible–infected–recovered
(SIR)
susceptible-exposed-infectious-removed
(SEIR)
models.
wide
range
investigated,
two
showed
promising
results
(i.e.,
multi-layered
perceptron,
MLP;
adaptive
network-based
fuzzy
inference
system,
ANFIS).
Based
on
reported
here,
due
highly
complex
nature
variation
its
behavior
across
nations,
study
suggests
effective
tool
model
outbreak.
provides
initial
benchmarking
demonstrate
potential
future
research.
further
that
genuine
novelty
can
realized
integrating
SEIR
Mathematics,
Journal Year:
2020,
Volume and Issue:
8(6), P. 890 - 890
Published: June 2, 2020
Several
epidemiological
models
are
being
used
around
the
world
to
project
number
of
infected
individuals
and
mortality
rates
COVID-19
outbreak.
Advancing
accurate
prediction
is
utmost
importance
take
proper
actions.
Due
lack
essential
data
uncertainty,
have
been
challenged
regarding
delivery
higher
accuracy
for
long-term
prediction.
As
an
alternative
susceptible-infected-resistant
(SIR)-based
models,
this
study
proposes
a
hybrid
machine
learning
approach
predict
COVID-19,
we
exemplify
its
potential
using
from
Hungary.
The
methods
adaptive
network-based
fuzzy
inference
system
(ANFIS)
multi-layered
perceptron-imperialist
competitive
algorithm
(MLP-ICA)
proposed
time
series
rate.
that
by
late
May,
outbreak
total
morality
will
drop
substantially.
validation
performed
9
days
with
promising
results,
which
confirms
model
accuracy.
It
expected
maintains
as
long
no
significant
interruption
occurs.
This
paper
provides
initial
benchmarking
demonstrate
future
research.
GCB Bioenergy,
Journal Year:
2021,
Volume and Issue:
13(5), P. 774 - 802
Published: Feb. 18, 2021
Abstract
Bioenergy
is
widely
considered
a
sustainable
alternative
to
fossil
fuels.
However,
large‐scale
applications
of
biomass‐based
energy
products
are
limited
due
challenges
related
feedstock
variability,
conversion
economics,
and
supply
chain
reliability.
Artificial
intelligence
(AI),
an
emerging
concept,
has
been
applied
bioenergy
systems
in
recent
decades
address
those
challenges.
This
paper
reviewed
164
articles
published
between
2005
2019
that
different
AI
techniques
systems.
review
focuses
on
identifying
the
unique
capabilities
various
addressing
bioenergy‐related
research
improving
performance
Specifically,
we
characterized
studies
by
their
input
variables,
output
techniques,
dataset
size,
performance.
We
examined
throughout
life
cycle
identified
four
areas
which
mostly
applied,
including
(1)
prediction
biomass
properties,
(2)
process
conversion,
pathways
technologies,
(3)
biofuel
properties
end‐use
systems,
(4)
modeling
optimization.
Based
review,
particularly
useful
generating
data
hard
be
measured
directly,
traditional
models
end‐uses,
overcoming
computing
for
design
For
future
research,
efforts
needed
develop
standardized
practical
procedures
selecting
determining
training
samples,
enhance
collection,
documentation,
sharing
across
areas,
explore
potential
supporting
development
from
holistic
perspectives.
International Journal of Chemical Engineering,
Journal Year:
2021,
Volume and Issue:
2021, P. 1 - 12
Published: July 30, 2021
The
consumption
of
fossil
fuels
has
exponentially
increased
in
recent
decades,
despite
significant
air
pollution,
environmental
deterioration
challenges,
health
problems,
and
limited
resources.
Biofuel
can
be
used
instead
fuel
due
to
benefits
availability
produce
various
energy
sorts
like
electricity,
power,
heating
or
sustain
transportation
fuels.
Biodiesel
production
is
an
intricate
process
that
requires
identifying
unknown
nonlinear
relationships
between
the
system
input
output
data;
therefore,
accurate
swift
modeling
instruments
machine
learning
(ML)
artificial
intelligence
(AI)
are
necessary
design,
handle,
control,
optimize,
monitor
system.
Among
biodiesel
methods,
provides
better
predictions
with
highest
accuracy,
inspired
by
brain’s
autolearning
self-improving
capability
solve
study’s
complicated
questions;
it
beneficial
for
(trans)
esterification
processes,
physicochemical
properties,
monitoring
systems
real-time.
Machine
applications
phase
include
quality
optimization
estimation,
conditions,
quantity.
Emissions
composition
temperature
estimation
motor
performance
analysis
investigate
phase.
Fatty
methyl
acid
ester
stands
as
parameter,
parameters
oil
catalyst
type,
methanol-to-oil
ratio,
concentration,
reaction
time,
domain,
frequency.
This
paper
will
present
a
review
discuss
ML
technology
advantages,
disadvantages,
production,
mainly
focused
on
recently
published
articles
from
2010
2021,
make
decisions
model,
monitor,
forecast
production.
