Maintaining
cultural
and
linguistic
diversity
is
a
challenge
in
an
increasingly
digital
world.
For
language
like
Bangla,
with
rich
script
heritage,
preserving
the
essence
of
handwritten
text
crucial.
The
research
employs
Deep
Learning
algorithm,
to
decipher
nuances
Bangla
script.
algorithm
learns
mimic
fluid
strokes,
unique
characters,
artistic
intrinsic
through
extensive
training
on
authentic
dataset.
To
democratize
use
this
technology,
user-friendly
model
interface
for
generating
developed.
This
allows
users,
regardless
technical
expertise,
seamlessly
recognize
into
beautiful
imagery.
However,
Bangla's
beauty
not
just
act
conservation;
it's
testament
our
commitment
diversity.
paper
addresses
by
proposing
novel
approach
recognizing
image
format,
leveraging
Applied Sciences,
Journal Year:
2023,
Volume and Issue:
13(18), P. 10229 - 10229
Published: Sept. 12, 2023
The
detection
technique
for
IP
packet
header
modifications
associated
with
store-and-forward
operation
pertains
to
a
methodology
or
mechanism
utilized
the
identification
and
of
alterations
made
headers
within
network
setting
that
utilizes
operation.
problem
led
employing
this
lies
fact
previous
research
studies
expected
intrusion
systems
(IDSs)
perform
everything
inspecting
entire
transmission
session
detecting
any
modification.
However,
in
process,
upon
arrival
at
node
such
as
router
switch,
is
temporarily
stored
prior
being
transmitted
its
intended
destination.
Throughout
duration
storage,
IDS
tasks
would
not
be
able
store
packet;
however,
it
possible
certain
adjustments
could
implemented
does
recognize.
For
reason,
current
uses
combination
convolutional
neural
long
short-term
memory
predict
process.
CNN
LSTM
suggests
significant
improvement
model’s
performance
an
increase
number
packets
each
flow:
on
average,
99%
was
achieved.
This
implies
when
comprehending
ideal
pattern,
model
exhibits
accurate
predictions
cases
where
abruptly
increases.
study
has
contribution
are
linked
Computers, materials & continua/Computers, materials & continua (Print),
Journal Year:
2024,
Volume and Issue:
79(3), P. 4433 - 4448
Published: Jan. 1, 2024
Along
with
the
progression
of
Internet
Things
(IoT)
technology,
network
terminals
are
becoming
continuously
more
intelligent.IoT
has
been
widely
applied
in
various
scenarios,
including
urban
infrastructure,
transportation,
industry,
personal
life,
and
other
socio-economic
fields.The
introduction
deep
learning
brought
new
security
challenges,
like
an
increment
abnormal
traffic,
which
threatens
security.Insufficient
feature
extraction
leads
to
less
accurate
classification
results.In
traffic
detection,
data
is
high-dimensional
complex.This
not
only
increases
computational
burden
model
training
but
also
makes
information
difficult.To
address
these
issues,
this
paper
proposes
MD-MRD-ResNeXt
for
detection.To
fully
utilize
multi-scale
a
Multi-scale
Dilated
(MD)
block
introduced.This
module
can
effectively
understand
process
at
scales
uses
dilated
convolution
technology
significantly
broaden
model's
receptive
field.The
proposed
Max-feature-map
Residual
Dual-channel
pooling
(MRD)
integrates
maximum
map
residual
block.This
ensures
focuses
on
key
information,
thereby
optimizing
efficiency
reducing
unnecessary
redundancy.Experimental
results
show
that
compared
latest
methods,
detection
improves
accuracy
by
about
2%.
In
recent
years,
vehicular
ad
hoc
networks
(VANETs)
have
faced
growing
security
concerns,
particularly
from
Denial
of
Service
(DoS)
and
Distributed
(DDoS)
attacks.
These
attacks
flood
the
network
with
malicious
traffic,
disrupting
services
compromising
resource
availability.
While
various
techniques
been
proposed
to
address
these
threats,
this
study
presents
an
optimized
framework
leveraging
advanced
deep-learning
models
for
improved
detection
accuracy.
The
Intrusion
Detection
System
(IDS)
employs
Convolutional
Neural
Networks
(CNN),
Long
Short-Term
Memory
(LSTM),
Deep
Belief
(DBN)
alongside
robust
feature
selection
techniques,
Random
Projection
(RP)
Principal
Component
Analysis
(PCA).
This
extracts
analyzes
significant
features
using
a
publicly
available
application-layer
DoS
attack
dataset,
achieving
higher
accuracy
than
traditional
methods.
Experimental
results
indicate
that
combining
CNN,
LSTM
networks,
DBN
like
PCA
in
classification
performance,
0.994,
surpassing
state-of-the-art
machine
learning
models.
novel
approach
enhances
reliability
safety
vehicle
communications
by
providing
efficient,
real-time
threat
detection.
findings
contribute
significantly
VANET
security,
laying
foundation
future
advancements
connected
protection.
Maintaining
cultural
and
linguistic
diversity
is
a
challenge
in
an
increasingly
digital
world.
For
language
like
Bangla,
with
rich
script
heritage,
preserving
the
essence
of
handwritten
text
crucial.
The
research
employs
Deep
Learning
algorithm,
to
decipher
nuances
Bangla
script.
algorithm
learns
mimic
fluid
strokes,
unique
characters,
artistic
intrinsic
through
extensive
training
on
authentic
dataset.
To
democratize
use
this
technology,
user-friendly
model
interface
for
generating
developed.
This
allows
users,
regardless
technical
expertise,
seamlessly
recognize
into
beautiful
imagery.
However,
Bangla's
beauty
not
just
act
conservation;
it's
testament
our
commitment
diversity.
paper
addresses
by
proposing
novel
approach
recognizing
image
format,
leveraging
