To
address
the
shortcomings
of
Heating,
Ventilation,
and
Air
Conditioning
systems
(HVAC)
with
low
energy
efficiency,
this
paper
introduces
application
effectiveness
deep
reinforcement
learning
in
HVAC
systems.
Deep
mainly
includes
model-based
algorithms
model-free
algorithms.
Model-based
require
a
large
amount
system
environment
knowledge,
which
is
usually
difficult
to
obtain,
while
do
not
need
knowledge
model
environment,
so
high
research
value.
The
applications
domain
are
reviewed
divided
into
three
categories:
value
function-based
methods,
policy
gradient-based
actor-critic-based
categories
also
described
detail.
Finally,
current
control
summarized,
future
directions
prospected.
Heliyon,
Год журнала:
2023,
Номер
9(12), С. e22844 - e22844
Опубликована: Ноя. 24, 2023
The
crucial
aspect
of
the
medical
sector
is
healthcare
in
today's
modern
society.
To
analyze
a
massive
quantity
information,
system
necessary
to
gain
additional
perspectives
and
facilitate
prediction
diagnosis.
This
device
should
be
intelligent
enough
patient's
state
health
through
social
activities,
individual
behavior
analysis.
Health
Recommendation
System
(HRS)
has
become
an
essential
mechanism
for
care.
In
this
sense,
efficient
networks
are
critical
decision-making
processes.
fundamental
purpose
maintain
that
sensitive
information
can
shared
only
at
right
moment
while
guaranteeing
effectiveness
data,
authenticity,
security,
legal
concerns.
As
some
people
use
media
recognize
their
problems,
recommendation
systems
need
generate
findings
like
diagnosis
recommendations,
insurance,
passageway-based
care
strategies,
homeopathic
remedies
associated
with
status.
New
studies
aimed
vast
numbers
by
integrating
multidisciplinary
data
from
various
sources
addressed,
which
also
decreases
burden
costs.
article
presents
recommended
HRS
using
deep
learning
Restricted
Boltzmann
Machine
(RBM)-Coevolutionary
Neural
Network
(CNN)
provides
insights
on
how
mining
techniques
could
used
introduce
effective
engine
highlights
pharmaceutical
industry's
ability
translate
either
conventional
scenario
towards
more
personalized.
We
developed
our
proposed
TensorFlow
Python.
evaluate
suggested
method's
performance
distinct
error
quantities
compared
alternative
methods
dataset.
Furthermore,
approach's
accuracy,
precision,
recall,
F-measure
were
current
methods.
SINERGI,
Год журнала:
2023,
Номер
27(3), С. 451 - 451
Опубликована: Сен. 18, 2023
Cancer
subjugates
a
community
that
lacks
proper
care.
It
remains
apparent
research
studies
enhance
novel
benchmarks
in
developing
computer-assisted
tool
for
prognosis
radiology
yet
an
indication
of
illness
detection
should
be
recognized
by
the
pathologist.
In
bone
cancer
(BC),
Identification
malignancy
out
BC’s
histopathological
image
(HI)
difficult
because
intricate
structure
tissue
(BTe)
specimen.
This
study
proffers
new
approach
to
diagnosing
BC
feature
extraction
alongside
classification
employing
deep
learning
frameworks.
this,
input
is
processed
and
segmented
Tsallis
Entropy
noise
elimination,
rescaling,
smoothening.
The
features
are
excerpted
Efficient
Net-based
Convolutional
Neural
Network
(CNN)
Feature
Extraction.
ROI
will
employed
precise
atypical
portions
surrounding
affected
area.
Next,
classifying
accurate
spotting
grading
BTe
as
typical
augmented
XGBoost
Whale
optimization
(WOA).
HIs
gathering
prevailing
scales
patients
acquired
texture
characteristics
such
images
remaining
training
testing
(NN).
These
outcomes
exhibit
NN
possesses
hit
ratio
99.48
percent
while
this
occurs
BT
classification.
