Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy
JOIV International Journal on Informatics Visualization,
Год журнала:
2024,
Номер
8(1), С. 55 - 55
Опубликована: Март 16, 2024
Integrating
machine
learning
(ML)
and
artificial
intelligence
(AI)
with
renewable
energy
sources,
including
biomass,
biofuels,
engines,
solar
power,
can
revolutionize
the
industry.
Biomass
biofuels
have
benefited
significantly
from
implementing
AI
ML
algorithms
that
optimize
feedstock,
enhance
resource
management,
facilitate
biofuel
production.
By
applying
insight
derived
data
analysis,
stakeholders
improve
entire
supply
chain
-
biomass
conversion,
fuel
synthesis,
agricultural
growth,
harvesting
to
mitigate
environmental
impacts
accelerate
transition
a
low-carbon
economy.
Furthermore,
in
combustion
systems
engines
has
yielded
substantial
improvements
efficiency,
emissions
reduction,
overall
performance.
Enhancing
engine
design
control
techniques
produces
cleaner,
more
efficient
minimal
impact.
This
contributes
sustainability
of
power
generation
transportation.
are
employed
analyze
vast
quantities
photovoltaic
systems'
design,
operation,
maintenance.
The
ultimate
goal
is
increase
output
system
efficiency.
Collaboration
among
academia,
industry,
policymakers
imperative
expedite
sustainable
future
harness
potential
energy.
these
technologies,
it
possible
establish
ecosystem,
which
would
benefit
generations.
Язык: Английский
An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation
Van Vu,
Phuoc Tai Le,
Thi Mai Thom
и другие.
JOIV International Journal on Informatics Visualization,
Год журнала:
2024,
Номер
8(1), С. 158 - 158
Опубликована: Март 31, 2024
This
review
article
looks
at
the
developing
field
of
artificial
intelligence
and
machine
learning
in
maritime
marine
environment
management.
The
industry
is
increasingly
interested
applying
advanced
AI
ML
technologies
to
solve
sustainability,
efficiency,
regulatory
compliance
issues.
paper
examines
applications
using
a
deep
literature
case
study
analysis.
Modeling
ship
fuel
consumption,
which
impacts
operating
expenses,
top
responsibility.
demonstrates
that
approaches
such
as
Random
Forest
Tweedie
models
can
estimate
use.
Statistical
analysis
model
beats
regarding
accuracy
consistency.
For
training
testing
datasets,
has
high
R2
values
0.9997
0.9926,
indicating
solid
match.
Low
Root
Mean
Square
Error
(RMSE)
average
absolute
relative
deviation
(AARD)
suggest
accurately
reflects
use
variability.
While
still
performing
well,
lower
higher
RMSE
AARD
values,
suggesting
reduced
precision
consumption
prediction.
These
findings
provide
light
on
potential
Advanced
analytics
enables
decision-makers
analyze
patterns
better,
increase
operational
decrease
environmental
impact,
thus
improving
sustainability.
Язык: Английский
Optimization of Vegetable Production in Hydroculture Environments Using Artificial Intelligence: A Literature Review
Sustainability,
Год журнала:
2025,
Номер
17(7), С. 3103 - 3103
Опубликована: Март 31, 2025
This
review
analyzes
the
role
of
artificial
intelligence
(AI)
and
automation
in
optimizing
vegetable
production
within
hydroculture
systems.
Methods:
Following
PRISMA
methodology,
this
study
examines
research
on
IoT-based
monitoring
AI
techniques,
particularly
Deep
Neural
Networks
(DNNs),
K-Nearest
Neighbors
(KNNs),
Fuzzy
Logic
(FL),
Convolutional
(CNNs),
Decision
Trees
(DTs).
Additionally,
Recurrent
(RNNs)
Long
Short-Term
Memory
(LSTM)
models
were
analyzed
due
to
their
effectiveness
processing
temporal
data
improving
predictive
capabilities
nutrient
optimization.
These
have
demonstrated
high
precision
managing
key
parameters
such
as
pH,
temperature,
electrical
conductivity,
dosing
enhance
crop
growth.
The
selection
criteria
focused
peer-reviewed
studies
from
2020
2024,
emphasizing
automation,
efficiency,
sustainability,
real-time
monitoring.
After
filtering
out
duplicates
non-relevant
papers,
72
IEEE,
SCOPUS,
MDPI,
Google
Scholar
databases
analyzed,
focusing
applicability
production.
Results:
Among
evaluated,
(DNNs)
achieved
97.5%
accuracy
growth
predictions,
while
(FL)
a
3%
error
rate
solution
adjustments,
ensuring
reliable
decision-making.
CNNs
most
effective
for
disease
pest
detection,
reaching
99.02%,
contributing
reduced
pesticide
use
improved
plant
health.
Random
Forest
(RF)
Support
Vector
Machines
(SVMs)
up
water
consumption
irrigation
promoting
sustainable
resource
management.
LSTM
RNN
long-term
predictions
absorption,
hydroponic
system
control.
Hybrid
integrating
machine
learning
deep
techniques
showed
promise
enhancing
automation.
Conclusion:
AI-driven
optimization
improves
management,
health
monitoring,
leading
higher
yields
sustainability.
Despite
its
benefits,
challenges
availability,
model
standardization,
implementation
costs
persist.
Future
should
focus
accessibility,
interoperability,
real-world
validation
expand
adoption
smart
agriculture.
Furthermore,
integration
be
further
explored
adaptability
improve
resilience
environments.
Язык: Английский
AIoT based Soil Nutrient Analysis and Recommendation System for Crops using Machine Learning
Smart Agricultural Technology,
Год журнала:
2025,
Номер
unknown, С. 100924 - 100924
Опубликована: Апрель 1, 2025
Язык: Английский
Analysis of Soil Viability Monitoring System for In-House Plantation Growth Using an Internet of Things Approach
Pertanika journal of science & technology,
Год журнала:
2024,
Номер
32(6), С. 2591 - 2608
Опубликована: Окт. 23, 2024
Houseplant
cultivation
has
become
increasingly
popular,
allowing
individuals
to
bring
nature
into
their
homes.
However,
successful
indoor
gardening
requires
careful
monitoring
of
soil
parameters
ensure
optimal
plant
growth.
To
address
this
need,
sensor
technology
and
Internet
Things
(IoT)
devices
are
utilized
monitor
temperature
moisture
levels,
which
play
crucial
roles
in
Various
factors
sensed
collected
using
an
IoT-based
microcontroller,
with
data
transmission
facilitated
by
a
Message
Queue
Telemetry
Transport
(MQTT)
broker.
Visualization
the
is
achieved
through
Node-RED
programming
tool,
simplifying
dashboard
creation
for
easy
monitoring.
Furthermore,
stored
MySQL
server,
enabling
further
analysis
SQL
queries.
The
day
divided
four
quarters
six-hour
intervals,
collection
sensors.
resulting
information
on
facilitates
informed
decision-making
enhance
conditions
Experimentation
revealed
reduction
3°C
during
daytime
due
air
conditioning
operation,
while
content
remains
consistently
between
60
65%
early
mornings
late
evenings.
Additionally,
emphasis
placed
remote
management
IoT
systems,
growth
even
when
access
limited.
Overall,
offers
promising
approach
optimizing
practices
minimizing
environmental
resource
consumption.
Язык: Английский