Abstract
Implementing
the
necessary
countermeasures
to
detect
growing
and
highly
destructive
family
of
malware
is
an
urgent
obligation.
The
proliferation
diversity
make
these
problems
more
challenging.
For
beginners,
it
arduous
attain
crucial
features
for
multi‐class
classification
extract
valuable
information
from
obtained
features.
Another
issue
that
building
a
model
effectively
absorbs
samples
adapts
various
This
work
indicates
precise
identification
method
Android
application
families
(ANDF)
tackle
issues.
It
perceptively
analyzes
can
utilize
identify
members
further
excavates
relationship
between
implicit
severity
those
distinctions.
A
appropriate
developed
heterogeneous
file
formats,
beneficial
feature
with
diverse
array
chosen
as
replacement
representation
sample.
capable
upgrading
learning
ability
mastering
multi‐modal
traits
malware.
ANDF
real
data
sets
yields
effective
results.
0.9800
in
f1‐macro
has
accuracy
98.61%.
performs,
respectively,
0.0088
points
better
than
two‐feature
comparison
0.0872
single‐feature
model.
kappa
coefficient
also
exceed
0.9830,
which
at
least
0.1044
higher
other
contrasting
classifiers
0.0105
greater
contrasted
containing
two
features,
0.1046
larger
classifier
single
feature.
Diagnostics,
Год журнала:
2023,
Номер
13(7), С. 1320 - 1320
Опубликована: Апрель 2, 2023
Brain
tumor
diagnosis
at
an
early
stage
can
improve
the
chances
of
successful
treatment
and
better
patient
outcomes.
In
biomedical
industry,
non-invasive
diagnostic
procedures,
such
as
magnetic
resonance
imaging
(MRI),
be
used
to
diagnose
brain
tumors.
Deep
learning,
a
type
artificial
intelligence,
analyze
MRI
images
in
matter
seconds,
reducing
time
it
takes
for
potentially
improving
Furthermore,
ensemble
model
help
increase
accuracy
classification
by
combining
strengths
multiple
models
compensating
their
individual
weaknesses.
Therefore,
this
research,
weighted
average
deep
learning
is
proposed
For
model,
three
different
feature
spaces
are
taken
from
transfer
VGG19
Convolution
Neural
Network
(CNN)
without
augmentation,
CNN
with
augmentation.
These
ensembled
best
combination
weights,
i.e.,
weight1,
weight2,
weight3
using
grid
search.
The
dataset
simulation
Cancer
Genome
Atlas
(TCGA),
having
lower-grade
glioma
collection
3929
110
patients.
helps
reduce
overfitting
that
have
learned
aspects
data.
outperforms
detecting
tumors
terms
accuracy,
precision,
F1-score.
act
second
opinion
tool
radiologists
brain.
Decision Analytics Journal,
Год журнала:
2024,
Номер
11, С. 100470 - 100470
Опубликована: Апрель 24, 2024
Convolutional
Neural
Network
(CNN)
is
a
prevalent
topic
in
deep
learning
(DL)
research
for
their
architectural
advantages.
CNN
relies
heavily
on
hyperparameter
configurations,
and
manually
tuning
these
hyperparameters
can
be
time-consuming
researchers,
therefore
we
need
efficient
optimization
techniques.
In
this
systematic
review,
explore
range
of
well
used
algorithms,
including
metaheuristic,
statistical,
sequential,
numerical
approaches,
to
fine-tune
hyperparameters.
Our
offers
an
exhaustive
categorization
(HPO)
algorithms
investigates
the
fundamental
concepts
CNN,
explaining
role
variants.
Furthermore,
literature
review
HPO
employing
above
mentioned
undertaken.
A
comparative
analysis
conducted
based
strategies,
error
evaluation
accuracy
results
across
various
datasets
assess
efficacy
methods.
addition
addressing
current
challenges
HPO,
our
illuminates
unresolved
issues
field.
By
providing
insightful
evaluations
merits
demerits
objective
assist
researchers
determining
suitable
method
particular
problem
dataset.
highlighting
future
directions
synthesizing
diversified
knowledge,
survey
contributes
significantly
ongoing
development
optimization.
2021 5th International Conference on Information Systems and Computer Networks (ISCON),
Год журнала:
2023,
Номер
unknown, С. 1 - 4
Опубликована: Март 3, 2023
There
has
been
an
increase
in
interest
digitizing
and
preserving
old
books
papers
the
last
few
years.
The
quick
advancement
of
data
innovation
Internet's
spread
also
contributed
to
enormous
volume
image
video
data.
texts
that
are
included
assist
us
analyzing
them
utilized
for
indexing,
archiving,
retrieval.
Different
noises,
such
as
Gaussian
noise,
salt
pepper
speckle
etc.,
can
readily
damage
image.
Several
filtering
algorithms,
including
filter,
mean
median
employed
eliminate
these
various
noises
from
images.
This
article
analyses
impact
several
pre-processing
approaches,
thresholding,
morphology,
blurring
procedures,
maximise
text
extraction
strategies.
experiment's
findings
demonstrate
approaches
unquestionably
improve
document's
structural
visual
quality.
Computational Intelligence and Neuroscience,
Год журнала:
2022,
Номер
2022, С. 1 - 9
Опубликована: Июль 31, 2022
Nowadays,
so
many
people
are
living
in
world.
If
living,
then
the
diseases
also
increasing
day
by
due
to
adulterated
and
chemical
content
food.
The
may
suffer
either
from
a
small
disease
such
as
cold
cough
or
big
cancer.
In
this
work,
we
have
discussed
on
encephalon
tumor
cancer
which
is
problem
nowadays.
will
consider
about
whole
world,
there
deficiency
of
clinical
experts
doctors
compared
affected
person.
So,
here,
used
an
automatic
classification
help
particle
swarm
optimization
(PSO)-based
extreme
learning
machine
(ELM)
technique
with
segmentation
process
improved
fast
robust
fuzzy
C
mean
(IFRFCM)
algorithm
most
commonly
feature
reduction
method
gray
level
co-occurrence
matrix
(GLCM)
that
helpful
experts.
Here,
BraTs
("Multimodal
Brain
Tumor
Segmentation
Challenge
2020")
dataset
for
both
training
testing
purpose.
It
has
been
monitored
our
system
given
better
accuracy
approximation
99.47%
can
be
observed
good
outcome.
Marvin
Minsky,
the
father
of
Artificial
intelligence
(AI),
defined
AI
as
machines
that
are
smarter
than
humans
and
can-do
tasks
cannot
easily
do.
has
grown
at
a
rapid
rate
never
before
seen
in
variety
industries.
Different
types
already
used
by
insurance
companies,
service
providers
companies.
Now,
with
addition
AI,
healthcare
sector
is
also
emerging.
Increased
availability
medical
data
advances
analytical
techniques
causing
paradigm
shift
healthcare.
The
literature
reveals
multiple
applications
for
services
an
incompletely
covered
body
research.
authors
explored
current
situation
technology
was
examined,
to
their
long-term
prospects.
As
result,
goal
use
bibliometric
method
analyze
dynamics
interconnection
between
digital
health
approaches
while
taking
into
account
responsible
ethical
elements
scientific
output
throughout
time.
Machine
learning
(ML)
is
used
for
advancement
of
various
fields
which
also
applicable
to
education
system
will
change
and
teaching
methods
fundamentally.
As
educational
institutions
gather
a
sizable
amount
student
data,
this
information
can
be
further
narrow
down
the
elements
that
changed
improve
likelihood
students
succeed.
ML
utilized
by
educators
like
retention
better
grading
systems
results.
Development
new
insights
done
using
ML.
This
study
discusses
how
we
use
in
sector
tackle
problems
from
students'
teachers'
perspective
them
future
research
on
topic.
2022 International Conference on Computer Communication and Informatics (ICCCI),
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 23, 2023
Diagnosis
of
any
disease
is
done
only
after
detection
the
type
and
intensity
disease.
Chronic
pulmonary
such
as
Obstructive
Pulmonary
Disease
(COPD),
lung
cancer,
cystic
fibrosis
needs
to
be
detected
in
initial
stages
get
proper
treatment.
However,
early
diagnosis
cancer
case
lungs
not
very
easy
due
presence
no
recognizable
symptoms
until
much
more
adverse
situations.
The
proposed
work
aims
at
detecting
using
Convolutional
Neural
Network
(CNN)
regarding
dataset
compiled
from
Kaggle
Domain.
Various
parameters
are
used
determine
performance
method,
namely,
precision,
recall
F1-score.
Experimental
results
concluded
that,
CNN-based
approach
resulted
significantly
improved
when
compared
few
existing
methods.
method
has
94%
average
93.6%
94.6%
F1-score,
respectively.
Augmentation
of
the
datasets
authentic
microscopy
images
with
synthetic
is
a
promising
solution
to
problem
limited
availability
biomedical
data
for
training
deep
neural
network
(DNN)
based
classifiers.
In
present
study,
we
use
text-to-image
latent
stable
diffusion
model
fine-tuned
by
means
low-rank
adaptation
(LoRA)
augment
small
dataset
organ
on
chip
cells.
While
resulting
appear
quite
similar
which
was
performed,
find
that
neither
EfficientNetB7
DNN
solely
nor
augmentation
real-world
different
proportions
(10,
25,
50,
and
75
percent)
these
leads
improvement
accuracy
model.
The
findings
our
study
suggest
further
exploration
options
needed
fully
capacity
models
synthesis
images.