Advancing Geographic Information Systems With Machine Learning
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 253 - 270
Опубликована: Март 6, 2025
A
Geographic
Information
System
(GIS)
is
a
technological
tool
that
allows
for
the
capture,
storage,
analysis,
and
visualization
of
geographically
referenced
data.
These
systems
integrate
various
forms
spatial
non-spatial
data,
facilitating
analysis
geographic
phenomena
patterns.The
integration
Machine
Learning
(ML)
into
Systems
has
revolutionized
way
geospatial
data
analyzed
used.
Learning,
with
its
ability
to
learn
from
large
volumes
make
accurate
predictions,
complements
analytical
capabilities
GIS,
allowing
extraction
complex
patterns
performance
advanced
predictions
were
not
previously
possible.
The
purpose
this
chapter
explore
applications
empowered
by
use
machine
learning,
highlighting
their
impact
on
environmental
management.
Язык: Английский
Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer
Diagnostics,
Год журнала:
2025,
Номер
15(6), С. 761 - 761
Опубликована: Март 18, 2025
Background:
Melanoma
is
a
highly
aggressive
form
of
skin
cancer,
necessitating
early
and
accurate
detection
for
effective
treatment.
This
study
aims
to
develop
novel
classification
system
melanoma
that
integrates
Convolutional
Neural
Networks
(CNNs)
feature
extraction
the
Aquila
Optimizer
(AO)
dimension
reduction,
improving
both
computational
efficiency
accuracy.
Methods:
The
proposed
method
utilized
CNNs
extract
features
from
images,
while
AO
was
employed
reduce
dimensionality,
enhancing
performance
model.
effectiveness
this
hybrid
approach
evaluated
on
three
publicly
available
datasets:
ISIC
2019,
ISBI
2016,
2017.
Results:
For
2019
dataset,
model
achieved
97.46%
sensitivity,
98.89%
specificity,
98.42%
accuracy,
97.91%
precision,
97.68%
F1-score,
99.12%
AUC-ROC.
On
2016
it
reached
98.45%
98.24%
97.22%
97.84%
97.62%
98.97%
2017,
results
were
98.44%
98.86%
97.96%
98.12%
97.88%
99.03%
outperforms
existing
advanced
techniques,
with
4.2%
higher
6.2%
improvement
in
5.8%
increase
specificity.
Additionally,
reduced
complexity
by
up
37.5%.
Conclusions:
deep
learning-Aquila
(DL-AO)
framework
offers
efficient
detection,
making
suitable
deployment
resource-constrained
environments
such
as
mobile
edge
computing
platforms.
integration
DL
metaheuristic
optimization
significantly
enhances
robustness,
detection.
Язык: Английский
Isolation Forest for Environmental Monitoring: A Data-Driven Approach to Land Management
Environments,
Год журнала:
2025,
Номер
12(4), С. 116 - 116
Опубликована: Апрель 10, 2025
This
paper
examines
land
management
technologies
to
enhance
environmental
monitoring
more
efficiently.
The
study
highlights
the
interactions
between
human
activities
and
systems
with
a
data-driven
approach.
There
are
many
pressures,
such
as
pollution,
degradation,
habitat
loss,
negatively
impacting
soil
health.
methodology
proposed
improves
status
assessments
in
response
evolving
pressures
by
utilizing
satellite
imagery
predictive
modeling.
integration
of
Sentinel-2
imagery,
calculation
various
spectral
indices
(NDVI,
NBR,
NDMI,
EVI,
SAVI)
at
different
time
intervals,
application
Isolation
Forest
algorithm
employed
this
determine
specific
area
that
is
affected
issue.
chosen
was
favored
due
its
superior
performance
handling
high-dimensionality
data,
enhanced
computational
efficiency,
provision
interpretable
results,
insensitivity
disparities
class
distribution.
analyzes
two
separate
cases
scales.
first
involves
wildfire
identification
achieving
an
overall
accuracy
98%.
second
focuses
on
expansion
areas
pre-existing
quarries
95%.
NBR
proved
most
effective
delineating
burned
areas,
whereas
EVI
generated
remarkable
results
quarry
case
study.
approach
provides
scalable
tool
for
monitoring,
supporting
sustainable
policies,
strengthening
ecosystem
resilience.
Язык: Английский
INTEGRATING GEOSPATIAL TECHNOLOGIES AND MACHINE LEARNING FOR MONITORING AND ASSESSING ENVIRONMENTAL IMPACTS OF MINING ACTIVITIES IN THE SOUTH EAST OF NIGERIA: A STRUCTURED REVIEW
FUDMA Journal of Sciences,
Год журнала:
2025,
Номер
9(1), С. 406 - 422
Опубликована: Апрель 16, 2025
The
environmental
impacts
of
mining
activities
in
Southeast
Nigeria
pose
significant
challenges
and
threats
to
the
local
ecosystems
communities.
Research
reveals
that
impact
these
had
hitherto
been
poorly
monitored
or
assessed
due
inefficient
manual
approach
used.
Now,
there
are
currently
gaps
literature
on
potential
advanced
technologies
for
sustainable
management
vis-à-vis
practices
Nigeria.
This
research
therefore
seeks
bridge
this
gap
has
adopted
a
review
method
synthesize
existing
knowledge
determine
prospects
integration
geospatial
machine
learning
monitoring
managing
support
improved
decision-making.
adhered
PRISMA
guidelines,
which
involved
an
initial
evaluation
550
articles
eventually
resulted
64
relevant
materials
used
study.
findings
indicate
led
severe
land
degradation,
deforestation,
water
contamination,
adversely
affecting
biodiversity
livelihoods.
study
also
revealed
hold
great
monitoring,
assessment,
as
whole
call
urgent
policy
considerations
from
stakeholders
governments.
Язык: Английский
Evaluation of Cluster Algorithms for Radar-Based Object Recognition in Autonomous and Assisted Driving
Sensors,
Год журнала:
2024,
Номер
24(22), С. 7219 - 7219
Опубликована: Ноя. 12, 2024
Perception
systems
for
assisted
driving
and
autonomy
enable
the
identification
classification
of
objects
through
a
concentration
sensors
installed
in
vehicles,
including
Radio
Detection
Ranging
(RADAR),
camera,
Light
(LIDAR),
ultrasound,
HD
maps.
These
ensure
reliable
robust
navigation
system.
Radar,
particular,
operates
with
electromagnetic
waves
remains
effective
under
variety
weather
conditions.
It
uses
point
cloud
technology
to
map
front
you,
making
it
easy
group
these
points
associate
them
real-world
objects.
Numerous
clustering
algorithms
have
been
developed
can
be
integrated
into
radar
identify,
investigate,
track
In
this
study,
we
evaluate
several
determine
their
suitability
application
automotive
systems.
Our
analysis
covered
current
methods,
mathematical
process
presented
comparison
table
between
algorithms,
Hierarchical
Clustering,
Affinity
Propagation
Balanced
Iterative
Reducing
Clustering
using
Hierarchies
(BIRCH),
Density-Based
Spatial
Applications
Noise
(DBSCAN),
Mini-Batch
K-Means,
K-Means
Mean
Shift,
OPTICS,
Spectral
Gaussian
Mixture.
We
found
that
DBSCAN
are
particularly
suitable
applications,
based
on
performance
indicators
assess
efficiency.
However,
shows
better
compared
others.
Furthermore,
our
findings
highlight
choice
significantly
impacts
effectiveness
object
recognition
methods.
Язык: Английский