Research Briefs on Information and Communication Technology Evolution,
Journal Year:
2023,
Volume and Issue:
9, P. 98 - 115
Published: Oct. 16, 2023
The
integration
of
deep
learning
(DL)
techniques
with
geographical
information
system
(GIS)
offers
a
promising
avenue
for
gaining
novel
insights
into
environmental
phenomena
by
using
the
capabilities
spatial,
temporal,
and
spectral
resolutions,
as
well
data
integration.
these
two
technologies
can
result
in
development
highly
efficient
assessing
conditions
analyzing
interplay
between
texture,
size,
pattern,
process.
This
viewpoint
has
gained
appeal
across
various
academic
disciplines.
GIS
heavily
relies
on
processors,
especially
tasks
such
3D
computations,
map
rendering,
route
calculation.
In
contrast,
DL
capability
to
efficiently
analyze
vast
quantities
data.
garnered
significant
attention
recent
times
due
its
potential
delivering
wide
range
outcomes.
Moreover,
there
is
clear
evidence
increasing
utilization
fields,
including
GIS.
objective
this
study
provide
an
overview
application
field
paper
presents
concise
review
fundamental
ideas
that
are
pertinent
GIS,
focus
most
current
research
findings.
present
investigates
uses
technology
remote
sensing
diverse
domains,
mapping,
hydrological
modeling,
disaster
management,
transportation
planning.
contemporary
framework
approaches
proposes
avenues
further
research.
Information,
Journal Year:
2024,
Volume and Issue:
15(5), P. 280 - 280
Published: May 14, 2024
This
research
paper
presents
a
comprehensive
study
on
optimizing
the
critical
artificial
intelligence
(AI)
factors
influencing
cost
management
in
civil
engineering
projects
using
multi-criteria
decision-making
(MCDM)
approach.
The
problem
addressed
revolves
around
need
to
effectively
manage
costs
endeavors
amidst
growing
complexity
of
and
increasing
integration
AI
technologies.
methodology
employed
involves
utilization
three
MCDM
tools,
specifically
Delphi,
interpretive
structural
modeling
(ISM),
Cross-Impact
Matrix
Multiplication
Applied
Classification
(MICMAC).
A
total
17
factors,
categorized
into
eight
broad
groups,
were
identified
analyzed.
Through
application
different
techniques,
relative
importance
interrelationships
among
these
determined.
key
findings
reveal
role
certain
such
as
risk
mitigation
components,
processes.
Moreover,
hierarchical
structure
generated
through
ISM
influential
via
MICMAC
provide
insights
for
prioritizing
strategic
interventions.
implications
this
extend
informing
decision-makers
domain
about
effective
strategies
leveraging
their
practices.
By
adopting
systematic
approach,
stakeholders
can
enhance
project
outcomes
while
resource
allocation
mitigating
financial
risks.
Water,
Journal Year:
2024,
Volume and Issue:
16(19), P. 2748 - 2748
Published: Sept. 27, 2024
Groundwater
salinization
poses
a
critical
threat
to
sustainable
development
in
arid
and
semi-arid
rurbanizing
regions,
exemplified
by
Kerman
Province,
Iran.
This
region
experiences
groundwater
ecosystem
degradation
as
result
of
the
rapid
conversion
rural
agricultural
land
urban
areas
under
chronic
drought
conditions.
study
aims
enhance
Pollution
Risk
(GwPR)
mapping
integrating
DRASTIC
index
with
machine
learning
(ML)
models,
including
Random
Forest
(RF),
Boosted
Regression
Trees
(BRT),
Generalized
Linear
Model
(GLM),
Support
Vector
Machine
(SVM),
Multivariate
Adaptive
Splines
(MARS),
alongside
hydrogeochemical
investigations,
promote
water
management
Province.
The
RF
model
achieved
highest
accuracy
an
Area
Under
Curve
(AUC)
0.995
predicting
GwPR,
outperforming
BRT
(0.988),
SVM
(0.977),
MARS
(0.951),
GLM
(0.887).
RF-based
map
identified
new
high-vulnerability
zones
northeast
northwest
showed
expanded
moderate
vulnerability
zone,
covering
48.46%
area.
Analysis
revealed
exceedances
WHO
standards
for
total
hardness
(TH),
sodium,
sulfates,
chlorides,
electrical
conductivity
(EC)
these
areas,
indicating
contamination
from
mineralized
aquifers
unsustainable
practices.
findings
underscore
model’s
effectiveness
prediction
highlight
need
stricter
monitoring
management,
regulating
extraction
improving
use
efficiency
riverine
aquifers.
EPJ Web of Conferences,
Journal Year:
2025,
Volume and Issue:
318, P. 04013 - 04013
Published: Jan. 1, 2025
Dysfunctions
of
the
patent
supply
and
demand
market
have
a
negative
impact
on
sustainability
national
innovation
system,
which
stimulates
growth
prices
for
knowledge-intensive
products.
It
is
necessary
to
establish
relationship
between
fiscal
decisions
regarding
transactions
prospects
development
commercialization
results
intellectual
activity.
One
most
promising
methods
improving
accuracy
system
analysis
big
semi-structured
transaction
data
use
decision
trees.
Existing
based
error
backpropagation
method
are
quite
slow,
their
accelerated
versions
lose
in
training
accuracy.
To
effectively
solve
problem
forecasting
cost
hub
transactions,
parametric
algorithms
been
developed
response
bias
with
additional
predicative
structures
model
successive
geometric
transformations.
The
optimal
number
tree
predicates
has
established
taking
into
account
computational
efforts
transactions.
Based
evolutionary
computing,
values
parameters
mining
established.
Mathematics,
Journal Year:
2025,
Volume and Issue:
13(7), P. 1156 - 1156
Published: March 31, 2025
This
study
introduces
the
GWO-FNN
model,
an
improvement
of
fuzzy
neural
network
(FNN)
architecture
that
aims
to
balance
high
performance
with
improved
interpretability
in
artificial
intelligence
(AI)
systems.
The
model
leverages
Grey
Wolf
Optimizer
(GWO)
fine-tune
consequents
rules
and
uses
mutual
information
(MI)
initialize
weights
input
layer,
resulting
greater
classification
accuracy
transparency.
A
distinctive
aspect
is
its
capacity
transform
logical
neurons
hidden
layer
into
comprehensible
rules,
thereby
elucidating
reasoning
behind
outputs.
model’s
were
rigorously
evaluated
through
statistical
methods,
benchmarks,
real-world
dataset
testing.
These
evaluations
demonstrate
strong
capability
extract
clearly
express
intricate
patterns
within
data.
By
combining
advanced
rule
mechanisms
a
comprehensive
framework,
contributes
meaningful
advancement
interpretable
AI
approaches.
