Artificial Intelligence Review,
Год журнала:
2021,
Номер
54(6), С. 4653 - 4684
Опубликована: Апрель 23, 2021
In
an
overwhelming
demand
scenario,
such
as
the
SARS-CoV-2
pandemic,
pressure
over
health
systems
may
outburst
their
predicted
capacity
to
deal
with
extreme
situations.
Therefore,
in
order
successfully
face
a
emergency,
scientific
evidence
and
validated
models
are
needed
provide
real-time
information
that
could
be
applied
by
any
center,
especially
for
high-risk
populations,
transplant
recipients.
We
have
developed
hybrid
prediction
model
whose
accuracy
relative
several
alternative
configurations
has
been
through
battery
of
clustering
techniques.
Using
hospital
admission
data
from
cohort
hospitalized
patients,
our
Data
Envelopment
Analysis
(DEA)-Artificial
Neural
Network
(ANN)
extrapolates
progression
towards
severe
COVID-19
disease
96.3%,
outperforming
competing
model,
logistic
regression
(65.5%)
random
forest
(44.8%).
this
regard,
DEA-ANN
allows
us
categorize
evolution
patients
values
analyses
performed
at
admission.
Our
help
guiding
management
identification
key
predictors
permit
sustainable
resources
patient-centered
model.The
online
version
contains
supplementary
material
available
10.1007/s10462-021-10008-0.
Computer Networks,
Год журнала:
2021,
Номер
193, С. 108074 - 108074
Опубликована: Апрель 8, 2021
Technology
is
continually
undergoing
a
constituent
development
caused
by
the
appearance
of
billions
new
interconnected
"things"
and
their
entrenchment
in
our
daily
lives.
One
underlying
versatile
technologies,
namely
wearables,
able
to
capture
rich
contextual
information
produced
such
devices
use
it
deliver
legitimately
personalized
experience.
The
main
aim
this
paper
shed
light
on
history
wearable
provide
state-of-the-art
review
market.
Moreover,
provides
an
extensive
diverse
classification
based
various
factors,
discussion
wireless
communication
architectures,
data
processing
aspects,
market
status,
as
well
variety
other
actual
technology.
Finally,
survey
highlights
critical
challenges
existing/future
solutions.
IEEE Access,
Год журнала:
2020,
Номер
8, С. 186821 - 186839
Опубликована: Янв. 1, 2020
The
novel
coronavirus
(COVID-19),
declared
by
the
World
Health
Organization
(WHO)
as
a
global
pandemic,
has
brought
with
it
changes
to
general
way
of
life.
Major
sectors
world
industry
and
economy
have
been
affected
Internet
Things
(IoT)
management
framework
is
no
exception
in
this
regard.
This
article
provides
an
up
date
survey
on
how
pandemic
such
COVID-19
IoT
technologies.
It
looks
at
contributions
that
associated
sensor
technologies
made
towards
virus
tracing,
tracking
spread
mitigation.
challenges
deployment
hardware
face
rapidly
spreading
looked
into
part
review
article.
effects
evolution
architectures
also
addressed,
leading
likely
outcomes
future
implementations.
In
general,
insight
advancement
sensor-based
E-health
pandemics.
answers
question
shaped
networks.
Physics Education,
Год журнала:
2023,
Номер
58(3), С. 035027 - 035027
Опубликована: Апрель 4, 2023
The
latest
AI
language
modules
can
produce
original,
high
quality
full
short-form
($300$-word)
Physics
essays
within
seconds.
These
technologies
such
as
ChatGPT
and
davinci-003
are
freely
available
to
anyone
with
an
internet
connection.
In
this
work,
we
present
evidence
of
generated
achieving
first-class
grades
on
essay
writing
assessment
from
accredited,
current
university
module.
requires
students
answer
five
open-ended
questions
a
short,
$300$-word
each.
Fifty
answers
were
create
ten
submissions
that
independently
marked
by
separate
markers.
achieved
average
mark
$71
\pm
2
\%$,
in
strong
agreement
the
module
5
%$.
A
typical
submission
would
therefore
most-likely
be
awarded
First
Class,
highest
classification
at
UK
universities.
Plagiarism
detection
software
returned
plagiarism
score
between
$2
1$%
(Grammarly)
$7
2$%
(TurnitIn).
We
argue
these
results
indicate
MLPs
represent
significant
threat
fidelity
method
courses.
Neural Computing and Applications,
Год журнала:
2024,
Номер
36(21), С. 12655 - 12699
Опубликована: Май 13, 2024
Abstract
Artificial
neural
networks
(ANN),
machine
learning
(ML),
deep
(DL),
and
ensemble
(EL)
are
four
outstanding
approaches
that
enable
algorithms
to
extract
information
from
data
make
predictions
or
decisions
autonomously
without
the
need
for
direct
instructions.
ANN,
ML,
DL,
EL
models
have
found
extensive
application
in
predicting
geotechnical
geoenvironmental
parameters.
This
research
aims
provide
a
comprehensive
assessment
of
applications
addressing
forecasting
within
field
related
engineering,
including
soil
mechanics,
foundation
rock
environmental
geotechnics,
transportation
geotechnics.
Previous
studies
not
collectively
examined
all
algorithms—ANN,
EL—and
explored
their
advantages
disadvantages
engineering.
categorize
address
this
gap
existing
literature
systematically.
An
dataset
relevant
was
gathered
Web
Science
subjected
an
analysis
based
on
approach,
primary
focus
objectives,
year
publication,
geographical
distribution,
results.
Additionally,
study
included
co-occurrence
keyword
covered
techniques,
systematic
reviews,
review
articles
data,
sourced
Scopus
database
through
Elsevier
Journal,
were
then
visualized
using
VOS
Viewer
further
examination.
The
results
demonstrated
ANN
is
widely
utilized
despite
proven
potential
methods
engineering
due
real-world
laboratory
civil
engineers
often
encounter.
However,
when
it
comes
behavior
scenarios,
techniques
outperform
three
other
methods.
discussed
here
assist
understanding
benefits
geo
area.
enables
practitioners
select
most
suitable
creating
certainty
resilient
ecosystem.
Artificial
intelligence
(AI)
has
been
applied
widely
in
our
daily
lives
a
variety
of
ways
with
numerous
successful
stories.
AI
also
contributed
to
dealing
the
coronavirus
disease
(COVID-19)
pandemic,
which
happening
around
globe.
This
paper
presents
survey
methods
being
used
various
applications
fight
against
COVID-19
outbreak
and
outlines
crucial
roles
research
this
unprecedented
battle.
We
touch
on
number
areas
where
plays
as
an
essential
component,
from
medical
image
processing,
data
analytics,
text
mining
natural
language
Internet
Things,
computational
biology
medicine.
A
summary
related
sources
that
are
available
for
purposes
is
presented.
Research
directions
exploring
potentials
enhancing
its
capabilities
power
battle
thoroughly
discussed.
highlight
13
groups
problems
pandemic
point
out
promising
tools
can
be
solve
those
problems.
It
envisaged
study
will
provide
researchers
wider
community
overview
current
status
motivate
harnessing
COVID-19.
SIGSPATIAL Special,
Год журнала:
2020,
Номер
12(1), С. 6 - 15
Опубликована: Июнь 3, 2020
The
past
several
years
have
witnessed
a
huge
surge
in
the
use
of
social
media
platforms
during
mass
convergence
events
such
as
health
emergencies,
natural
or
human-induced
disasters.
These
non-traditional
data
sources
are
becoming
vital
for
disease
forecasts
and
surveillance
when
preparing
epidemic
pandemic
outbreaks.
In
this
paper,
we
present
GeoCoV19,
large-scale
Twitter
dataset
containing
more
than
524
million
multilingual
tweets
posted
over
period
90
days
since
February
1,
2020.
Moreover,
employ
gazetteer-based
approach
to
infer
geolocation
tweets.
We
postulate
that
largescale,
multilingual,
geolocated
can
empower
research
communities
evaluate
how
societies
collectively
coping
with
unprecedented
global
crisis
well
develop
computational
methods
address
challenges
identifying
fake
news,
understanding
communities'
knowledge
gaps,
building
forecast
models,
among
others.
Sensors,
Год журнала:
2021,
Номер
21(20), С. 6828 - 6828
Опубликована: Окт. 14, 2021
Wearable
sensing
technologies
are
having
a
worldwide
impact
on
the
creation
of
novel
business
opportunities
and
application
services
that
benefiting
common
citizen.
By
using
these
technologies,
people
have
transformed
way
they
live,
interact
with
each
other
their
surroundings,
daily
routines,
how
monitor
health
conditions.
We
review
recent
advances
in
area
wearable
focusing
aspects
such
as
sensor
communication
infrastructures,
service
security,
privacy.
also
use
consumer
wearables
during
coronavirus
disease
19
(COVID-19)
pandemic
caused
by
severe
acute
respiratory
syndrome
2
(SARS-CoV-2),
we
discuss
open
challenges
must
be
addressed
to
further
improve
efficacy
systems
future.
Neural Computing and Applications,
Год журнала:
2022,
Номер
35(7), С. 5251 - 5275
Опубликована: Ноя. 1, 2022
Feature
selection
(FS)
is
one
of
the
basic
data
preprocessing
steps
in
mining
and
machine
learning.
It
used
to
reduce
feature
size
increase
model
generalization.
In
addition
minimizing
dimensionality,
it
also
enhances
classification
accuracy
reduces
complexity,
which
are
essential
several
applications.
Traditional
methods
for
often
fail
optimal
global
solution
due
large
search
space.
Many
hybrid
techniques
have
been
proposed
depending
on
merging
strategies
individually
as
a
FS
problem.
This
study
proposes
modified
hunger
games
algorithm
(mHGS),
solving
optimization
problems.
The
main
advantages
mHGS
resolve
following
drawbacks
that
raised
original
HGS;
(1)
avoiding
local
search,
(2)
problem
premature
convergence,
(3)
balancing
between
exploitation
exploration
phases.
has
evaluated
by
using
IEEE
Congress
Evolutionary
Computation
2020
(CEC'20)
test
ten
medical
chemical
datasets.
dimensions
up
20000
features
or
more.
results
compared
variety
well-known
methods,
including
improved
multi-operator
differential
evolution
(IMODE),
gravitational
algorithm,
grey
wolf
optimization,
Harris
Hawks
whale
slime
mould
search.
experimental
suggest
can
generate
effective
without
increasing
computational
cost
improving
convergence
speed.
SVM
performance.