Plant Biotechnology Journal,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 14, 2025
Summary
Genomic
selection
(GS)
is
a
new
breeding
strategy.
Generally,
traditional
methods
are
used
for
predicting
traits
based
on
the
whole
genome.
However,
prediction
accuracy
of
these
models
remains
limited
because
they
cannot
fully
reflect
intricate
nonlinear
interactions
between
genotypes
and
traits.
Here,
novel
single
nucleotide
polymorphism
(SNP)
feature
extraction
technique
Pearson‐Collinearity
Selection
(PCS)
firstly
presented
improves
across
several
known
models.
Furthermore,
gene
network
model
(NetGP)
deep
learning
approach
designed
phenotypic
prediction.
It
utilizes
transcriptomic
dataset
(Trans),
genomic
multi‐omics
(Trans
+
SNP).
The
NetGP
demonstrated
better
performance
compared
to
other
in
predictions,
predictions
predictions.
performed
than
independent
or
Prediction
evaluations
using
plants'
data
showed
good
generalizability
NetGP.
Taken
together,
our
study
not
only
offers
effective
tool
plant
but
also
points
avenues
future
research.
Future Internet,
Journal Year:
2024,
Volume and Issue:
16(1), P. 32 - 32
Published: Jan. 19, 2024
With
the
rapid
advancements
and
notable
achievements
across
various
application
domains,
Machine
Learning
(ML)
has
become
a
vital
element
within
Internet
of
Things
(IoT)
ecosystem.
Among
these
use
cases
is
IoT
security,
where
numerous
systems
are
deployed
to
identify
or
thwart
attacks,
including
intrusion
detection
(IDSs),
malware
(MDSs),
device
identification
(DISs).
Learning-based
(ML-based)
security
can
fulfill
several
objectives,
detecting
authenticating
users
before
they
gain
access
system,
categorizing
suspicious
activities.
Nevertheless,
ML
faces
challenges,
such
as
those
resulting
from
emergence
adversarial
attacks
crafted
mislead
classifiers.
This
paper
provides
comprehensive
review
body
knowledge
about
defense
mechanisms,
with
particular
focus
on
three
prominent
systems:
IDSs,
MDSs,
DISs.
The
starts
by
establishing
taxonomy
context
IoT.
Then,
methodologies
employed
in
generation
described
classified
two-dimensional
framework.
Additionally,
we
describe
existing
countermeasures
for
enhancing
against
attacks.
Finally,
explore
most
recent
literature
vulnerability
ML-based
Minerals,
Journal Year:
2025,
Volume and Issue:
15(1), P. 71 - 71
Published: Jan. 13, 2025
This
study
aims
to
improve
the
efficiency
of
mineral
exploration
by
introducing
a
novel
application
Deep
Convolutional
Generative
Adversarial
Networks
(DCGANs)
augment
geological
evidence
layers.
By
training
DCGAN
model
with
existing
geological,
geochemical,
and
remote
sensing
data,
we
have
synthesized
new,
plausible
layers
that
reveal
unrecognized
patterns
correlations.
approach
deepens
understanding
controlling
factors
in
formation
deposits.
The
implications
this
research
are
significant
could
success
rate
projects
providing
more
reliable
comprehensive
data
for
decision-making.
predictive
map
created
using
proposed
feature
augmentation
technique
covered
all
known
deposits
only
18%
area.
Computers,
Journal Year:
2024,
Volume and Issue:
14(1), P. 1 - 1
Published: Dec. 24, 2024
The
advancement
of
artificial
intelligence
(AI)
technologies,
including
generative
pre-trained
transformers
(GPTs)
and
models
for
text,
image,
audio,
video
creation,
has
revolutionized
content
generation,
creating
unprecedented
opportunities
critical
challenges.
This
paper
systematically
examines
the
characteristics,
methodologies,
challenges
associated
with
detecting
synthetic
across
multiple
modalities,
to
safeguard
digital
authenticity
integrity.
Key
detection
approaches
reviewed
include
stylometric
analysis,
watermarking,
pixel
prediction
techniques,
dual-stream
networks,
machine
learning
models,
blockchain,
hybrid
approaches,
highlighting
their
strengths
limitations,
as
well
accuracy,
independent
accuracy
80%
analysis
up
92%
using
modalities
in
approaches.
effectiveness
these
techniques
is
explored
diverse
contexts,
from
identifying
deepfakes
media
AI-generated
scientific
texts.
Ethical
concerns,
such
privacy
violations,
algorithmic
bias,
false
positives,
overreliance
on
automated
systems,
are
also
critically
discussed.
Furthermore,
addresses
legal
regulatory
frameworks,
intellectual
property
emerging
legislation,
emphasizing
need
robust
governance
mitigate
misuse.
Real-world
examples
systems
analyzed
provide
practical
insights
into
implementation
Future
directions
developing
generalizable
adaptive
fostering
collaboration
between
stakeholders,
integrating
ethical
safeguards.
By
presenting
a
comprehensive
overview
AIGC
detection,
this
aims
inform
researchers,
policymakers,
practitioners
addressing
dual-edged
implications
AI-driven
creation.
