Image clustering algorithm and psychological perception in historical building colour rating research: A case study of Guangzhou, China
Tianyi Fan,
No information about this author
Xiaoxiang Tang,
No information about this author
Kerun Li
No information about this author
et al.
Frontiers of Architectural Research,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 1, 2025
Language: Английский
Exploration of Strategies for Enhancing the Quality of Urban Space Based on Multi-Source Data Fusion
Silin Yang,
No information about this author
Luyao Xiang,
No information about this author
Yu Yan
No information about this author
et al.
Buildings,
Journal Year:
2025,
Volume and Issue:
15(8), P. 1258 - 1258
Published: April 11, 2025
This
article,
via
empirical
studies,
investigates
the
influences
of
facility
accessibility,
correlation,
and
resident
satisfaction
on
urban
spatial
quality.
It
is
discovered
that
these
three
elements
are
positively
correlated
with
Excellent
accessibility
rational
layout
can
elevate
quality,
reflects
outcome
environmental
optimization.
On
this
basis,
article
puts
forward
strategies
intensifying
infrastructure
construction,
using
multi-source
data
to
optimize
transportation
system,
implementing
humanistic
care
promoting
community
interaction,
digital
intelligent
management
city,
paying
attention
cultural
aesthetics
aim
offering
theoretical
support
practical
guidance
for
enhancing
facilitating
sustainable
development
improvement
residents’
quality
life.
Language: Английский
An analysis of spatial vitality distribution and formation mechanisms in historical urban areas based on multi-source big data: A case study of Changsha
Yun Long,
No information about this author
Sheng Jiao,
No information about this author
Yan Yu
No information about this author
et al.
Frontiers of Architectural Research,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 1, 2025
Language: Английский
The application of artificial intelligence in the revitalization of intangible cultural heritage helps the cultural industry succeed
Qingxiang Zhu,
No information about this author
Xiaobin Liu
No information about this author
Journal of Computational Methods in Sciences and Engineering,
Journal Year:
2025,
Volume and Issue:
unknown
Published: May 2, 2025
The
integration
of
artificial
intelligence
(AI)
in
revitalizing
intangible
cultural
heritage
(ICH)
necessitates
solutions
to
enhance
participation
and
preserve
culture,
thereby
contributing
the
growth
industry.
objective
this
research
is
design
an
AI-driven
model
utilizing
Adaptive
Donkey
Smuggler
Algorithm-mutated
Malleable
Long
Short-Term
Memory
(ADS-MLSTM)
network
recognition,
preservation,
revitalization
ICH,
supporting
industry
sustainability.
Data
were
collected
from
multiple
ICH
archives,
including
digital
representations
heritage.
This
data
underwent
preprocessing
steps
such
as
noise
reduction
cleaning
ensure
robustness
against
diverse
ICH.
Utilizing
Term
Frequency-Inverse
Document
Frequency
(TF-IDF)
method,
features
extracted
efficiently.
ADS
MLSTM
algorithms
proposed
ADS-MLSTM
demonstrates
superior
performance,
achieving
a
precision
98.70%,
mean
squared
error
(MSE)
0.73,
recall
98.27%,
F1-score
98.80%,
accuracy
99%,
root
(RMSE)
0.57,
further
highlighting
its
effectiveness.
incorporation
deep
learning
significantly
enhanced
model’s
effectiveness,
leading
better
results
recognizing
elements.
AI
plays
essential
role
recovering
assets,
particularly
through
model.
By
improving
recognition
fostering
user
interaction,
approaches
contribute
industry,
offering
innovative
solution
for
preserving
promoting
Language: Английский
Cooperative transportation of an object with a nonholonomic constraint by a swarm robot
ROBOMECH Journal,
Journal Year:
2025,
Volume and Issue:
12(1)
Published: May 24, 2025
Language: Английский
Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning
S. H. Chen,
No information about this author
Sainan Lin,
No information about this author
Yao Yao
No information about this author
et al.
Land,
Journal Year:
2024,
Volume and Issue:
13(12), P. 2108 - 2108
Published: Dec. 5, 2024
Women
face
disadvantages
in
urban
public
spaces
due
to
their
physiological
characteristics.
However,
limited
attention
has
been
given
assessing
safety
perceptions
from
a
female
perspective
and
identifying
the
factors
that
influence
these
perceptions.
Despite
advancements
machine
learning
(ML)
techniques,
efficiently
accurately
quantifying
remains
challenge.
This
study,
using
Wuhan
as
case
proposes
method
for
ranking
street
women
by
combining
RankNet
with
Gist
features.
Fully
Convolutional
Network-8s
(FCN-8s)
was
employed
extract
built
environment
features,
while
Ordinary
Least
Squares
(OLS)
regression
Geographically
Weighted
Regression
(GWR)
were
used
explore
relationship
between
features
women’s
The
results
reveal
following
key
findings:
(1)
perception
rankings
align
its
multi-center
pattern,
significant
differences
observed
central
area.
(2)
Built
significantly
perceptions,
Sky
View
Factor,
Green
Index,
Roadway
Visibility
identified
most
impactful
factors.
Factor
positive
effect
on
whereas
other
exhibit
negative
effects.
(3)
of
varies
spatially,
allowing
study
area
be
classified
into
three
types:
sky-
road-dominant,
building-dominant,
greenery-dominant
regions.
Finally,
this
targeted
strategies
creating
safer
more
female-friendly
spaces.
Language: Английский