Advances in natural fiber polymer and PLA composites through artificial intelligence and machine learning integration
Md. Helal Uddin,
Mohammed Huzaifa Mulla,
Tarek Abedin
и другие.
Journal of Polymer Research,
Год журнала:
2025,
Номер
32(3)
Опубликована: Фев. 24, 2025
Язык: Английский
Machine Learning and Deep Learning Strategies for Chinese Hamster Ovary Cell Bioprocess Optimization
Fermentation,
Год журнала:
2024,
Номер
10(5), С. 234 - 234
Опубликована: Апрель 27, 2024
The
use
of
machine
learning
and
deep
has
become
prominent
within
various
fields
bioprocessing
for
countless
modeling
prediction
tasks.
Previous
reviews
have
emphasized
applications
in
bioprocessing,
including
biomanufacturing.
This
comprehensive
review
highlights
many
the
different
multivariate
analysis
techniques
that
been
utilized
Chinese
hamster
ovary
cell
biomanufacturing,
specifically
due
to
their
rising
significance
industry.
Applications
other
industries
are
also
briefly
discussed.
Язык: Английский
Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application
EAI Endorsed Transactions on Energy Web,
Год журнала:
2024,
Номер
11
Опубликована: Июнь 5, 2024
INTRODUCTION:
Cloud
computing,
a
still
emerging
technology,
allows
customers
to
pay
for
services
based
on
usage.
It
provides
internet-based
services,
whilst
virtualization
optimizes
PC’s
available
resources.
OBJECTIVES:
The
foundation
of
cloud
computing
is
the
data
center,
comprising
networked
computers,
cables,
electricity
components,
and
various
other
elements
that
host
store
corporate
data.
In
centres,
high
performance
has
always
been
critical
concern,
but
this
often
comes
at
cost
increased
energy
consumption.
METHODS:
most
problematic
factor
reducing
power
consumption
while
maintaining
service
quality
balance
system
efficiency
use.
Our
proposed
approach
requires
comprehensive
understanding
usage
patterns
within
environment.
RESULTS:
We
examined
trends
demonstrate
with
application
right
optimization
principles
models,
significant
savings
can
be
made
in
centers.
During
prediction
phase,
tablet
optimization,
its
97
%
accuracy
rate,
enables
more
accurate
future
forecasts.
CONCLUSION:
Energy
major
concern
To
handle
incoming
requests
fewest
resources
possible,
given
increasing
demand
widespread
adoption
it
essential
maintain
effective
efficient
center
strategies.
Язык: Английский
Estimating best nanomaterial for energy harvesting through reinforcement learning DQN coupled with fuzzy PROMETHEE under road-based conditions
Sekar Kidambi Raju,
Ganesh Karthikeyan Varadarajan,
Amal H. Alharbi
и другие.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 14, 2024
Energy
harvesters
based
on
nanomaterials
are
getting
more
and
popular,
but
their
way
to
commercial
availability,
some
crucial
issues
still
need
be
solved.
The
objective
of
the
study
is
select
an
appropriate
nanomaterial.
Using
features
Reinforcement
Deep
Q-Network
(DQN)
in
conjunction
with
Fuzzy
PROMETHEE,
proposed
model,
we
present
this
work
a
hybrid
fuzzy
approach
selecting
materials
for
vehicle-environmental-hazardous
substance
(EHS)
combination
that
operates
roadways
under
traffic
conditions.
DQN
able
accumulate
useful
experience
operating
dynamic
environment,
accordingly
deliver
highest
energy
output
at
same
time
bring
consideration
factors
such
as
durability,
cost,
environmental
impact.
PROMETHEE
allows
participation
human
experts
during
decision-making
process,
going
beyond
quantitative
data
typically
learned
by
through
inclusion
qualitative
preferences.
Instead,
method
unites
strength
individual
approaches,
result
providing
highly
resistant
adjustable
material
selection
real
EHS.
pointed
out
can
give
high
efficiency
reference
years
service,
price,
effects.
model
provides
95%
accuracy
computational
300
s,
application
hypothesis
practical
testing
chosen
showed
selected
harvest
fluctuating
conditions
proved
concept
True
Vehicle
Environmental
High-risk
Substance
scenarios.
Язык: Английский
AI-Driven Green Campus: Solar Panel Fault Detection Using ResNet-50 for Solar-Hydrogen System in Universities
Опубликована: Июль 1, 2024
Язык: Английский
A Comprehensive Study of Machine Learning Models and Computer Vision Techniques for Renewable Energy Forecasting
Advances in environmental engineering and green technologies book series,
Год журнала:
2024,
Номер
unknown, С. 29 - 41
Опубликована: Май 1, 2024
This
project
aims
to
develop
a
method
for
wind
turbine
blade
(WTB)
inspection
using
machine
learning
and
computer
vision
that
would
allow
early
detection
diagnosis
of
structural
faults
in
WTBs,
aiding
condition-based
maintenance
the
industry.
At
present,
industry
relies
on
use
manual
inspections
blades
fault
diagnosis.
The
drones
has
been
proven
bridges
dams
is
process
being
implemented
OSW
However,
current
methods
require
huge
volumes
data
labour-intensive
pre-processing.
utilise
methods,
reduce
human
input
required
WTBs.
will
consist
developing
set
novel
algorithms
can
achieve
high
accuracies
classification
from
limited
datasets
introduce
prior
knowledge
into
process.
Язык: Английский