Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(3), P. 1079 - 1079
Published: Jan. 26, 2024
As
the
global
imperative
for
sustainable
development
intensifies,
real
estate
industry
stands
at
intersection
of
environmental
responsibility
and
economic
viability.
This
paper
presents
a
comprehensive
exploration
significance
solutions
within
sector,
employing
advanced
artificial
intelligence
(AI)
algorithms
to
assess
their
impact.
study
focuses
on
integration
AI-powered
tools
in
decision-making
process
analysis.
The
research
methodology
involves
implementation
AI
capable
analyzing
vast
datasets
related
attributes.
By
leveraging
machine
learning
techniques,
algorithm
assesses
energy
efficiency
along
with
other
intrinsic
extrinsic
examines
effectiveness
these
relation
influence
property
prices
framework
based
an
AI-driven
algorithm.
findings
aim
inform
professionals
investors
about
tangible
advantages
integrating
technologies
into
solutions,
promoting
more
informed
responsible
approach
practices.
contributes
growing
interest
connection
sustainability,
AI,
offering
insights
that
can
guide
strategic
decision
making.
implementing
random
forest
method
feature
assessment
original
methodology,
it
has
been
shown
be
useful
tool
from
perspective
price
prediction.
methodology’s
ability
handle
non-linear
relationships
provide
importance
proved
advantageous
comparison
multiple
regression
Language: Английский
Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design
Materials,
Journal Year:
2023,
Volume and Issue:
16(17), P. 5956 - 5956
Published: Aug. 30, 2023
The
design
of
concrete
mixtures
is
crucial
in
technology,
aiming
to
produce
that
meets
specific
quality
and
performance
criteria.
Modern
standards
require
not
only
strength
but
also
eco-friendliness
production
efficiency.
Based
on
the
Three
Equation
Method,
conventional
mix
methods
involve
analytical
laboratory
procedures
are
insufficient
for
contemporary
leading
overengineering
difficulty
predicting
properties.
Machine
learning-based
offer
a
solution,
as
they
have
proven
effective
compressive
design.
This
paper
scrutinises
association
between
computational
complexity
machine
learning
models
their
proficiency
concrete.
study
evaluates
five
deep
neural
network
varying
three
series.
Each
model
trained
tested
series
with
vast
database
recipes
associated
destructive
tests.
findings
suggest
positive
correlation
increased
model's
predictive
ability.
evidenced
by
an
increment
coefficient
determination
(R2)
decrease
error
metrics
(mean
squared
error,
Minkowski
normalized
root
mean
sum
error)
increases.
research
provide
valuable
insights
increasing
technical
feature
prediction
while
acknowledging
this
study's
limitations
suggesting
potential
future
directions.
paves
way
further
refinement
AI-driven
design,
enhancing
efficiency
precision
process.
Language: Английский
Human-Machine Synergy in Real Estate Similarity Concept
Real Estate Management and Valuation,
Journal Year:
2023,
Volume and Issue:
32(2), P. 13 - 30
Published: Nov. 27, 2023
Abstract
The
issue
of
similarity
in
the
real
estate
market
is
a
widely
recognized
aspect
analysis,
yet
it
remains
underexplored
scientific
research.
This
study
aims
to
address
this
gap
by
introducing
concept
Property
Cognitive
Information
System
(PCIS),
which
offers
an
innovative
approach
analyzing
market.
PCIS
introduces
non-classical
and
alternative
solutions,
departing
from
conventional
data
analysis
practices
commonly
employed
Moreover,
delves
into
integration
artificial
intelligence
(AI)
PCIS.
paper
highlights
value
added
PCIS,
specifically
discussing
validity
using
automatic
ML-based
solutions
objectify
results
synergistic
processing
Furthermore,
article
establishes
set
essential
assumptions
recommendations
that
contribute
well-defined
interpretable
notion
context
human-machine
analyses.
By
exploring
intricacies
through
AI-based
research
seeks
broaden
understanding
applicability
techniques
domain.
Language: Английский