Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose
Food Research International,
Год журнала:
2024,
Номер
186, С. 114401 - 114401
Опубликована: Апрель 21, 2024
Язык: Английский
Application of artificial intelligence in drug design: A review
Computers in Biology and Medicine,
Год журнала:
2024,
Номер
179, С. 108810 - 108810
Опубликована: Июль 10, 2024
Язык: Английский
Artificial Intelligence aided pharmaceutical engineering: Development of hybrid machine learning models for prediction of nanomedicine solubility in supercritical solvent
Journal of Molecular Liquids,
Год журнала:
2024,
Номер
397, С. 124127 - 124127
Опубликована: Янв. 26, 2024
Язык: Английский
Hybrid Semi-mechanistic and Machine Learning Solubility Regression Modeling for Crystallization Process Development
Crystal Growth & Design,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 10, 2025
Solubility
regression
modeling
is
foundational
for
several
chemical
engineering
applications,
particularly
crystallization
process
development.
Traditionally,
these
models
rely
on
parametric
semimechanistic
approaches
such
as
the
Van't
Hoff
Jouyban-Acree
(VH-JA)
cosolvency
model.
Although
generally
provide
narrow
prediction
intervals,
they
can
exhibit
increased
bias
when
dealing
with
significant
solute
heat
capacities
or
complex
mixture
effects.
This
study
explores
machine
learning,
including
Random
Forests,
Support
Vector
Machines,
Gaussian
Process
Regression,
and
Neural
Networks,
potential
alternatives.
While
most
learning
offered
a
lower
training
error,
it
was
observed
that
their
predictive
quality
quickly
deteriorates
further
from
data.
Hence,
hybrid
approach
explored
to
leverage
low
of
variance
VH-JA
model
through
heterogeneous
locally
weighted
bagging
ensembles.
Key
methodology
quantifying,
tracking,
minimizing
uncertainty
using
ensemble.
illustrated
case
solubility
ketoconazole
in
binary
mixtures
2-propanol
water.
The
optimal
ensemble,
comprising
58%
stepwise
42%
models,
reduced
root-mean-squared
error
maximum
absolute
percentage
by
≈30%
compared
full
VH-JA,
while
preserving
comparable
interval.
Язык: Английский
Development of advanced hybrid mechanistic-artificial intelligence computational model for learning of numerical data of flow in porous membranes
Engineering Applications of Artificial Intelligence,
Год журнала:
2023,
Номер
126, С. 106910 - 106910
Опубликована: Авг. 15, 2023
Язык: Английский
Artificial intelligence modeling and simulation of membrane-based separation of water pollutants via ozone Process: Evaluation of separation
Thermal Science and Engineering Progress,
Год журнала:
2024,
Номер
51, С. 102627 - 102627
Опубликована: Май 8, 2024
Язык: Английский
Numerical Analysis of Gas Hold-Up of Two-Phase Ebullated Bed Reactor
ChemEngineering,
Год журнала:
2023,
Номер
7(5), С. 101 - 101
Опубликована: Окт. 20, 2023
Due
to
the
significant
increase
in
heavy
feedstocks
being
transported
refineries
and
hydrocracking
process,
significance
of
adopting
an
ebullated
bed
reactor
has
been
reemphasized
recent
years.
The
predictive
modelling
gas
hold-up
two-phase
was
performed
using
10
machine
learning
methods
based
on
support
vector
(SVM)
Gaussian
process
regression
(GPR)
this
study.
In
reactor,
impacts
three
features,
namely
liquid
velocity,
recycling
ratio,
were
examined.
velocity
most
impact
predicted
hold-up,
according
feature
analysis.
rotational-quadratic,
squared-exponential,
Matern
5/2,
exponential
kernel
functions
integrated
with
GPR
models
linear,
quadratic,
cubic,
fine,
medium,
coarse
SVM
model
well
during
training
testing,
exception
fine
model,
whose
R2
is
very
low.
According
>
0.9
low
RMSE
MAE
values,
5/2
best.
Язык: Английский
Prediction of Student Perception Towards Current Educational Design Using Feature Selection and Machine Learning Techniques
Опубликована: Ноя. 28, 2023
Student
perception
is
a
vital
part
of
the
educational
process
since
it
has
direct
influence
on
motivation,
engagement,
and
overall
academic
achievement.
Prior
to
COVID-19,
majority
education
had
traditional
approach,
therefore
there
were
few
factors
that
may
affect
students.
However,
was
significant
during
outbreak
entirely
transformed
into
online
learning
eventually
hybrid
form.
Therefore,
crucial
for
institutions
comprehend
these
characteristics
in
order
give
pupils
nurturing
enriching
atmosphere.
The
objective
article
identify
factor
highest
students'
current
system.
Feature
analysis
used
identifying
parameter
plays
great
role
levels.
Based
information
gathered
through
questionnaires,
machine
methods
are
forecast
degree
student
perception.
Thus,
this
research
enables
make
data-driven
choices
enhance
environment.
Язык: Английский