Real
estate
is
property
in
the
form
of
land
and
houses.
Its
prices
depend
on
location,
number
bedrooms,
society,
area,
etc.
India
second
world
regarding
population
growth,
so
affordable
housing
becomes
very
necessary.
The
real
business
vast.
So,
customers
or
sellers
need
to
know
estimated
price
This
research
covers
that
area
predicts
prices.
work
uses
k-fold
cross-validation
linear
regression
model.
In
K-fold
cross-validation,
data
divided
into
k
different
subsets
which
are
also
called
fold.
A
model
a
machine
learning
describes
relations
between
dependent
variable
one
more
independent
variables.
used
this
for
Bangalore
state.
results
obtained
from
compared
data.
study
prove
can
accurately
predict
buildings
Furthermore,
accurate
prediction
will
help
sellers,
directly
impact
economy.
International Journal of Neural Systems,
Journal Year:
2024,
Volume and Issue:
34(05)
Published: Feb. 18, 2024
Emotion
recognition
plays
an
essential
role
in
human-human
interaction
since
it
is
a
key
to
understanding
the
emotional
states
and
reactions
of
human
beings
when
they
are
subject
events
engagements
everyday
life.
Moving
towards
human-computer
interaction,
study
emotions
becomes
fundamental
because
at
basis
design
advanced
systems
support
broad
spectrum
application
areas,
including
forensic,
rehabilitative,
educational,
many
others.
An
effective
method
for
discriminating
based
on
ElectroEncephaloGraphy
(EEG)
data
analysis,
which
used
as
input
classification
systems.
Collecting
brain
signals
several
channels
wide
range
produces
cumbersome
datasets
that
hard
manage,
transmit,
use
varied
applications.
In
this
context,
paper
introduces
Empátheia
system,
explores
different
EEG
representation
by
encoding
into
images
prior
their
classification.
particular,
proposed
system
extracts
spatio-temporal
image
encodings,
or
atlases,
from
through
Processing
transfeR
Interaction
States
Mappings
Image-based
eNcoding
(PRISMIN)
framework,
thus
obtaining
compact
signals.
The
atlases
then
classified
architecture,
comprises
branches
convolutional,
recurrent,
transformer
models
designed
tuned
capture
spatial
temporal
aspects
emotions.
Extensive
experiments
were
conducted
Shanghai
Jiao
Tong
University
(SJTU)
Dataset
(SEED)
public
dataset,
where
significantly
reduced
its
size
while
retaining
high
performance.
results
obtained
highlight
effectiveness
approach
suggest
new
avenues
emotion
Sensors,
Journal Year:
2024,
Volume and Issue:
24(22), P. 7125 - 7125
Published: Nov. 6, 2024
EEG-based
Brain-Computer
Interfaces
(BCIs)
have
gained
significant
attention
in
rehabilitation
due
to
their
non-invasive,
accessible
ability
capture
brain
activity
and
restore
neurological
functions
patients
with
conditions
such
as
stroke
spinal
cord
injuries.
This
study
offers
a
comprehensive
bibliometric
analysis
of
global
BCI
research
from
2013
2023.
It
focuses
on
primary
review
articles
addressing
technological
innovations,
effectiveness,
system
advancements
clinical
rehabilitation.
Data
were
sourced
databases
like
Web
Science,
tools
(bibliometrix
R)
used
analyze
publication
trends,
geographic
distribution,
keyword
co-occurrences,
collaboration
networks.
The
results
reveal
rapid
increase
EEG-BCI
research,
peaking
2022,
focus
motor
sensory
EEG
remains
the
most
commonly
method,
contributions
Asia,
Europe,
North
America.
Additionally,
there
is
growing
interest
applying
BCIs
mental
health,
well
integrating
artificial
intelligence
(AI),
particularly
machine
learning,
enhance
accuracy
adaptability.
However,
challenges
remain,
inefficiencies
slow
learning
curves.
These
could
be
addressed
by
incorporating
multi-modal
approaches
advanced
neuroimaging
technologies.
Further
needed
validate
applicability
both
cognitive
rehabilitation,
especially
considering
high
prevalence
cerebrovascular
diseases.
To
advance
field,
expanding
participation,
underrepresented
regions
Latin
America,
essential.
Improving
efficiency
through
AI
integration
also
critical.
Ethical
considerations,
including
data
privacy,
transparency,
equitable
access
technologies,
must
prioritized
ensure
inclusive
development
use
these
technologies
across
diverse
socioeconomic
groups.
