Assessment of Hybrid Fiber Reinforced Graphene Nano-Engineered Concrete Composites: From Experimental Testing to Explainable Machine Learning Modeling
Journal of Materials Research and Technology,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 1, 2025
Язык: Английский
Experimental assessment and hybrid machine learning-based feature importance analysis with the optimization of compressive strength of waste glass powder-modified concrete
Materials Today Communications,
Год журнала:
2025,
Номер
unknown, С. 112081 - 112081
Опубликована: Март 1, 2025
Язык: Английский
Microstructural assessment and supervised machine learning-aided modeling to explore the potential of quartz powder as an alternate binding material in concrete
Case Studies in Construction Materials,
Год журнала:
2025,
Номер
unknown, С. e04568 - e04568
Опубликована: Март 1, 2025
Язык: Английский
Experimental Assessment and Machine Learning Quantification of Structural Eco-Cellular Lightweight Concrete Incorporating Waste Marble Powder and Silica Fume
Journal of Building Engineering,
Год журнала:
2025,
Номер
unknown, С. 112557 - 112557
Опубликована: Апрель 1, 2025
Язык: Английский
Enhancing malware detection with feature selection and scaling techniques using machine learning models
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 17, 2025
Abstract
The
increasing
prevalence
of
malware
presents
a
critical
challenge
to
cybersecurity,
emphasizing
the
need
for
robust
detection
methods.
This
study
uses
binary
tabular
classification
dataset
evaluate
impact
feature
selection,
scaling,
and
machine
learning
(ML)
models
on
detection.
methodology
involves
experimenting
with
three
scaling
techniques
(no
normalization,
min-max
scaling),
selection
methods
Linear
Discriminant
Analysis
(LDA),
Principal
Component
(PCA)),
twelve
ML
models,
including
traditional
algorithms
ensemble
A
publicly
available
11,598
samples
139
features
is
utilized,
model
performance
assessed
using
metrics
such
as
accuracy,
precision,
recall,
F1-score,
AUC-ROC.
Results
reveal
that
Light
Gradient
Boosting
Machine
(LGBM)
achieves
highest
accuracy
97.16%
when
PCA
either
or
normalization
are
applied.
Additionally,
consistently
outperform
demonstrating
their
effectiveness
in
enhancing
These
findings
offer
valuable
insights
into
optimizing
preprocessing
strategies
developing
reliable
efficient
systems.
Язык: Английский
A survey and partial dependency analysis to assess residential solid waste recycling awareness in Saudi Arabia
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 7, 2025
Rapid
industrialization,
rise
in
population,
and
urbanization
have
led
to
severe
environmental
degradation
health
concerns
for
inhabitants
due
regular
household
waste
(RHW).
Implementing
sustainable
management
practices,
such
as
recycling,
is
an
imminent
need
Saudi
Arabia
other
nations.
Yet,
the
analysis
of
awareness
regarding
RHW
recycling
its
influencing
elements
Kingdom
(KSA)
has
rarely
been
conducted.
Efficient
home
currently
a
major
concern,
particularly
economically
developing
countries,
inappropriate
disposal
results
financial
losses
detrimental
effects
on
environment
public
health.
The
objective
this
study
assess
level
among
households
RHW,
issues
associated
with
improper
disposal,
their
readiness
participate
RHW.
Therefore,
we
conducted
two-stage
analytic
investigation
that
included
total
909
from
different
areas
Arabia.
In
addition
questionnaire
responses,
partial
dependency
(PDP)
was
also
using
two
supervised
machine
learning
algorithms,
Multi-Layer
Perceptron
(MLP)
Decision
Tree
(DT),
evaluate
how
sociodemographic
factors
influence
awareness.
Based
results,
most
respondents
are
knowledgeable
worried
about
adverse
solid
waste.
Most
motivated
support
large-scale
program,
provided
enough
facilities
available.
Also,
PDP
revealed
age,
gender,
salary,
marital
status
significantly
impact
recycling.
Finally,
considering
rising
amount
produced
by
authorities
must
implement
program
address
harmful
promote
development
world.
Язык: Английский
Evaluating and Analyzing the Innovative Branding and Marketing Strategies of International Brand: A Study of Kellogg’s Pringles
Westcliff International Journal of Applied Research,
Год журнала:
2025,
Номер
9(1), С. 30 - 42
Опубликована: Март 7, 2025
Due
to
the
rise
of
industrialization
and
world
trade,
numerous
global
companies
are
venturing
into
food
marketing
industry,
which
is
seeing
rapid
growth
intense
competition
worldwide.
This
review
article
extensively
studies
Kellogg's
Pringles,
a
leading
brand
in
snack
industry.
It
sets
stage
with
an
introduction
management,
it
discusses
acquisition
later
followed
by
company
overview
that
encompasses
firm's
history,
products,
market
position.
The
SWOT
analysis
indicates
Pringles'
strengths,
weaknesses,
opportunities,
threats,
such
as
identity,
worldwide
presence,
competitive
arena.
Moreover,
PESTEL
looks
external
forces
affecting
operations,
regulatory,
economic,
technological
factors.
study
delved
strategy,
utilizing
mix
elements:
product,
price,
promotion,
distribution.
Through
in-depth
analysis,
research
focused
on
how
Pringles
positioned
within
industry
and,
more
importantly,
creates
maintains
its
advantage.
Monumental
achievements
demonstrate
company's
focus
product
innovation,
dynamic
pricing
strategies,
one-of-a-kind
promotional
campaigns,
wide
has
underlined
brand's
ability
employ
these
factors
maintaining
supremacy
recommended
ways
increasing
strategy
future.
Keywords:
Branding,
Kellogg,
Язык: Английский
Predicting the strengths of basalt fiber reinforced concrete mixed with fly ash using AML and Hoffman and Gardener techniques
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 9, 2025
Basalt
fiber-reinforced
concrete
(BFRC)
mixed
with
fly
ash,
combined
advanced
machine
learning
techniques,
offers
a
practical,
cost-effective,
and
less
time-consuming
alternative
to
traditional
experimental
methods.
Conventional
approaches
evaluating
mechanical
properties,
such
as
compressive
splitting
tensile
strengths,
typically
require
sophisticated
equipment,
meticulous
sample
preparation,
extended
testing
periods.
These
methods
demand
substantial
financial
resources,
specialized
labor,
considerable
time
for
data
collection
analysis.
The
integration
of
provides
transformative
solution
by
enabling
accurate
prediction
properties
minimal
data.
from
literature
analysis
were
used
121
records
collected
experimentally
tested
basalt
fiber
reinforced
samples
measuring
the
strengths
concrete.
Eleven
(11)
critical
factors
have
been
considered
constituents
studied
predict
Fc-Compressive
strength
(MPa)
Fsp-Splitting
(MPa),
which
are
output
parameters.
divided
into
training
set
(96
=
80%)
validation
(25
20%)
following
requirements
partitioning
sustainable
application.
Seven
(7)
selected
techniques
applied
in
prediction.
Further,
performance
evaluation
indices
compare
models'
abilities
lastly,
Hoffman
Gardener's
technique
was
evaluate
sensitivity
parameters
on
strengths.
At
end
exercise,
results
collated.
In
predicting
(Fc),
AdaBoost
similarly
excels,
matching
XGBoosting's
R2
0.98
same
MAE
values.
This
shows
effectiveness
boosting
predictive
modeling
estimation.
For
(Fsp),
also
outperforms
most
models,
achieving
an
0.96
phases.
Its
exceptionally
low
0.124
MPa
underscores
its
excellent
generalization
capabilities.
Overall,
XGBoosting
consistently
demonstrate
superior
both
predictions,
followed
closely
KNN.
models
benefit
ensemble
that
efficiently
handle
non-linear
patterns
noise.
SVR
performs
admirably,
whereas
GEP
GMDHNN
exhibit
weaker
capabilities
due
limitations
handling
complex
dynamics.
analysis,
method
proves
instrumental
identifying
key
drivers
concrete,
guiding
informed
decision-making
material
optimization
construction
practices.
Язык: Английский
Comparative Performance Evaluation of Quartz and Snail Shell Powders Modified Concrete: Mechanical, Machine Learning, and Microstructural Assessments
Journal of Materials Research and Technology,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 1, 2025
Язык: Английский
Combined Influence of Crushed Brick Powder and Recycled Concrete Aggregate on the Mechanical, Durability and Microstructural Properties of Eco-Concrete: An Experimental and Machine Learning-Based Evaluation
Journal of Materials Research and Technology,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 1, 2025
Язык: Английский