Frontiers in Science and Engineering,
Год журнала:
2024,
Номер
4(8), С. 21 - 30
Опубликована: Авг. 21, 2024
Artificial
Intelligence
(AI)
is
poised
to
revolutionize
the
architectural
design
and
energy
management
of
green
buildings,
offering
significant
advancements
in
sustainability
efficiency.
This
paper
explores
transformative
impact
AI
on
improving
efficiency
reducing
carbon
emissions
commercial
buildings.
By
leveraging
algorithms,
architects
can
optimize
building
performance
through
advanced
environmental
analysis,
automation
repetitive
tasks,
real-time
data-driven
decision-making.
facilitates
precise
consumption
forecasting
integration
renewable
sources,
enhancing
overall
Our
study
demonstrates
that
reduce
CO2
by
approximately
8%
19%,
respectively,
typical
mid-size
office
buildings
2050
compared
conventional
methods.
Further,
combination
with
policies
low-emission
production
projected
yield
reductions
up
40%
90%
emissions.
provides
a
systematic
approach
for
quantifying
AI's
benefits
across
various
types
climate
zones,
valuable
insights
decision-makers
construction
industry.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 8, 2025
Abstract
Deep
learning-based
medical
image
analysis
has
shown
strong
potential
in
disease
categorization,
segmentation,
detection,
and
even
prediction.
However,
high-stakes
complex
domains
like
healthcare,
the
opaque
nature
of
these
models
makes
it
challenging
to
trust
predictions,
particularly
uncertain
cases.
This
sort
uncertainty
can
be
crucial
analysis;
diabetic
retinopathy
is
an
example
where
slight
errors
without
indication
confidence
have
adverse
impacts.
Traditional
deep
learning
rely
on
single-point
limiting
their
ability
provide
measures
essential
for
robust
clinical
decision-making.
To
solve
this
issue,
Bayesian
approximation
approaches
evolved
are
gaining
market
traction.
In
work,
we
implemented
a
transfer
approach,
building
upon
DenseNet-121
convolutional
neural
network
detect
retinopathy,
followed
by
extensions
trained
model.
techniques,
including
Monte
Carlo
Dropout,
Mean
Field
Variational
Inference,
Deterministic
were
applied
represent
posterior
predictive
distribution,
allowing
us
evaluate
model
predictions.
Our
experiments
combined
dataset
(APTOS
2019
+
DDR)
with
pre-processed
images
showed
that
Bayesian-augmented
outperforms
state-of-the-art
test
accuracy,
achieving
97.68%
Dropout
model,
94.23%
91.44%
We
also
measure
how
certain
predictions
are,
using
entropy
standard
deviation
metric
each
approach.
evaluated
both
AUC
accuracy
scores
at
multiple
data
retention
levels.
addition
overall
performance
boosts,
results
highlight
does
not
only
improve
classification
detection
but
reveals
beneficial
insights
about
estimation
help
build
more
trustworthy
decision-making
solutions.
In
this
study,
we
explored
the
application
of
deep
learning
techniques
for
credit
card
fraud
detection,
aiming
to
improve
performance
and
reliability
anomaly
detection
methods
in
financial
transactions.
We
first
utilized
Isolation
Forest
algorithm,
achieving
a
accuracy
26%
top
1000
Subsequently,
experimented
with
Autoencoder
an
unsupervised
neural
network
model,
which
enhanced
33.6%
best
case
despite
some
fluctuations.
The
results
demonstrate
models'
strong
feature
extraction
capability
adaptability,
highlighting
their
potential
surpass
traditional
methods.
However,
high
imbalance
dataset,
only
0.17%
transactions
being
fraudulent,
poses
significant
challenge.
This
study
underscores
necessity
further
experimentation
optimization
structures
hyperparameters
achieve
more
stable
efficient
detection.
findings
provide
valuable
insights
reference
points
future
research
using
methodologies.
PeerJ Computer Science,
Год журнала:
2025,
Номер
11, С. e2787 - e2787
Опубликована: Март 31, 2025
Segmenting
brain
tumors
is
a
critical
task
in
medical
imaging
that
relies
on
advanced
deep-learning
methods.
However,
effectively
handling
complex
tumor
regions
requires
more
comprehensive
and
strategies
to
overcome
challenges
such
as
computational
complexity,
the
gradient
vanishing
problem,
variations
size
visual
impact.
To
these
challenges,
this
research
presents
novel
computationally
efficient
method
termed
lightweight
Inception
U-Net
(LIU-Net)
for
accurate
segmentation
task.
LIU-Net
balances
model
complexity
load
provide
consistent
performance
uses
blocks
capture
features
at
different
scales,
which
makes
it
relatively
lightweight.
Its
capability
efficiently
precisely
segment
tumors,
especially
challenging-to-detect
regions,
distinguishes
from
existing
models.
This
Inception-style
convolutional
block
assists
capturing
multiscale
while
preserving
spatial
information.
Moreover,
proposed
utilizes
combination
of
Dice
loss
Focal
handle
class
imbalance
issue.
The
was
evaluated
benchmark
BraTS
2021
dataset,
where
generates
remarkable
outcomes
with
score
0.8121
enhancing
(ET)
region,
0.8856
whole
(WT)
0.8444
core
(TC)
region
test
set.
evaluate
robustness
architecture,
cross-validated
an
external
cohort
2020
dataset.
obtained
0.8646
ET
0.9027
WT
0.9092
TC
These
results
highlight
effectiveness
integrating
into
making
promising
candidate
image
segmentation.
Artificial
Intelligence
(AI)
is
poised
to
revolutionize
the
architectural
design
and
energy
management
of
green
buildings,
offering
significant
advancements
in
sustainability
efficiency.
This
paper
explores
transformative
impact
AI
on
improving
efficiency
reducing
carbon
emissions
commercial
buildings.
By
leveraging
algorithms,
architects
can
optimize
building
performance
through
advanced
environmental
analysis,
automation
repetitive
tasks,
real-time
data-driven
decision-making.
facilitates
precise
consumption
forecasting
integration
renewable
sources,
enhancing
overall
Our
study
demonstrates
that
reduce
CO2
by
approximately
8%
19%,
respectively,
typical
mid-size
office
buildings
2050
compared
conventional
methods.
Further,
combination
with
policies
low-emission
production
projected
yield
reductions
up
40%
90%
emissions.
provides
a
systematic
approach
for
quantifying
AI's
benefits
across
various
types
climate
zones,
valuable
insights
decision-makers
construction
industry.
With
the
widespread
application
of
artificial
intelligence
technology
in
various
industries,
users'
attention
to
privacy
and
data
security
has
increased
significantly.
Federated
learning,
as
a
new
paradigm
combining
privacy-enhanced
computing
intelligence,
resolves
contradiction
between
open
sharing.
This
paper
presents
benefits
federated
learning
terms
privacy,
real-time
processing,
model
robustness,
compliance
cross-industry
applications.
At
same
time,
when
combined
with
Edge
AI
technology,
promotes
decentralisation
intelligent
systems,
improving
protection
accuracy.
also
discusses
cases
medical
field,
through
local
processing
training,
effectively
protecting
user
realizing
sharing
optimization,
promoting
development
intelligence.
Scientific Journal of Technology,
Год журнала:
2024,
Номер
6(8), С. 9 - 21
Опубликована: Авг. 21, 2024
The
application
of
AI
technology
in
urban
planning
covers
multiple
levels,
such
as
data
analysis,
decision
support,
and
automated
planning.
Urban
research
relies
on
to
understand
summarize
the
law
growth
improve
analysis
evolution
trend
space.
Planning
design
use
explore
relevant
factors
affecting
development
their
weights
discuss
critical
role
green
building
sustainable
construction
industry.
With
increase
global
energy
consumption
carbon
emissions,
traditional
methods
can
no
longer
meet
environmental
protection
requirements
efficient
resources.
As
a
solution,
has
been
paid
more
attention
adopted
by
people.
These
technologies
focus
not
only
efficiency
impact
buildings
but
also
resource
utilization
load
over
entire
life
cycle
driven
machine
learning.
This
paper
details
basic
principles
applications
technologies,
including
AI-driven
reduction
negative
impacts,
improvement
occupant
health,
resources,
optimization
indoor
quality.
focuses
LEED
assessment
system
developed
U.S.
Green
Building
Council
advancing
practices.
In
addition,
analyzes
vital
points
water
design,
learning-driven
wind
environment
optimization,
solar
application,
practical
cases
these
scale.
This
paper
discusses
the
application
of
artificial
intelligence
in
imaging
omics,
especially
cancer
research.
Imaging
omics
enables
detailed
analysis
spatial
and
temporal
heterogeneity
tumours
through
high-throughput
extraction
quantitative
features
from
medical
images
such
as
MRI,
PET,
CT.
focuses
on
applying
PARKS
systems
to
automate
recognition,
segmentation,
image
features,
significantly
enhancing
capabilities
clinical
decision
support
(CDSS).
The
future
direction
is
establish
a
robust
network
infrastructure
for
radiology
Medication-led
Health
care
(RLHC)
facilitate
development
personalised
treatment
protocols,
improve
diagnostic
accuracy,
prognosis
assessment,
recommendations
by
uploading
shared
database
comparing
them
with
historical
images.
This
paper
explores
the
application
of
machine
learning
(ML)
in
predicting
functional
recovery
patients
with
ischemic
stroke.
As
technology
advances,
ML
shows
significant
potential
field
stroke
medicine,
especially
areas
big
data
analytics
and
personalized
medicine.
Studies
have
shown
that
algorithms
can
improve
accuracy
image
analysis,
subtype
classification,
risk
assessment,
treatment
guidance,
prognosis
prediction.
However,
widespread
use
still
faces
challenges
such
as
standardization,
model
validation,
privacy,
bias.
reviews
current
status
stroke,
discusses
faced,
looks
forward
to
future
development
direction,
aiming
promote
practical
diagnosis
quality
life
patients.
This
paper
explores
the
application
of
machine
learning
(ML)
in
predicting
functional
recovery
patients
with
ischemic
stroke.
As
technology
advances,
ML
shows
significant
potential
field
stroke
medicine,
especially
areas
big
data
analytics
and
personalized
medicine.
Studies
have
shown
that
algorithms
can
improve
accuracy
image
analysis,
subtype
classification,
risk
assessment,
treatment
guidance,
prognosis
prediction.
However,
widespread
use
still
faces
challenges
such
as
standardization,
model
validation,
privacy,
bias.
reviews
current
status
stroke,
discusses
faced,
looks
forward
to
future
development
direction,
aiming
promote
practical
diagnosis
quality
life
patients.
