Illegal,
unreported,
and
unregulated
(IUU)
fishing
practices
pose
significant
threats
to
marine
ecosystems
global
fisheries
sustainability.
The
detection
classification
of
loitering
events,
where
vessels
spend
an
extended
period
in
a
specific
area,
are
critical
for
identifying
potential
IUU
activities.
This
research
proposes
comprehensive
approach
that
combines
supervised
semi-supervised
learning
techniques
effectively
detect
classify
events.
By
leveraging
various
machine
algorithms,
including
logistic
regression,
Gaussian
mixture
models,
support
vector
machines,
random
forests,
accurate
predictions
can
be
made
enhance
surveillance
combat
fishing.
2021 5th International Conference on Information Systems and Computer Networks (ISCON),
Journal Year:
2023,
Volume and Issue:
unknown
Published: March 3, 2023
Brain
tumours
are
caused
by
the
aberrant
development
of
cells,
which
is
what
leads
to
their
formation.
It
one
primary
factors
contributing
death
in
adults
all
over
world.
Millions
lives
could
be
saved
via
earlier
detection
brain
tumours.
An
increased
survival
rate
may
possible
if
detected
MRI
at
an
stage.
aids
treatment
process
providing
a
clearer
image
tumour.
utmost
importance
detect,
segment,
and
extract
contaminated
tumour
areas
from
scans,
but
this
massive
time-consuming
task
that
requires
skill
radiologists
or
clinical
professionals.
In
article,
modified
version
Alexnet
architecture
provided
for
purpose
identifying
classifying
through
use
productive
segmentation
strategy.
The
efficacy
proposed
approach
illustrated
numerical
results
showing
almost
87.38%
accuracy
recognising
normal
tissue
images.
goal
work
detect
stage
than
currently
possible,
given
strategy
performed
better
competing
methods.
2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT),
Journal Year:
2023,
Volume and Issue:
unknown, P. 1 - 6
Published: July 6, 2023
Litchi
plant
diseases
are
a
major
threat
to
global
agricultural
productivity,
economies,
and
the
environment
as
they
cause
significant
losses.
Therefore,
it
is
necessary
have
an
early
accurate
litchi
monitoring
system
for
farmers,
managers,
decision-makers.
To
develop
constraint-free
reliable
work
plan
total
disease
management,
comprehensive
review
of
literature
industry
practices
was
conducted.
A
conceptual
framework
classification
outlined,
which
uses
structured
approach
combining
professional
visual
interpretation,
pathological
analysis,
feature
extraction
using
convolutional
neural
networks.
The
in
were
identified,
described,
divided
into
groups
broad
classification.
next
step
recommend
course
treatment
related
provide
contact
information
pathologist
further
queries
suggestions.
aim
create
user-friendly
interface
offer
farmers
affordable,
simple,
quick
assistance.
Trafik ve Ulaşım Araştırmaları Dergisi,
Journal Year:
2025,
Volume and Issue:
8(1), P. 1 - 14
Published: April 30, 2025
Bu
çalışma,
çevresel
sürdürülebilirlik
perspektifinden
akıllı
ulaşım
sistemlerinin
performanslarını
değerlendirmek
amacıyla
TOPSIS
(İdeal
Çözüme
Benzerlik
ile
Tercih
Sıralama
Tekniği)
yöntemini
kullanmıştır.
Elektrikli
otobüs
sistemi
(EOS),
paylaşımlı
elektrikli
skuter
(PESS),
otonom
araç
paylaşım
(OAPS)
ve
bisiklet
(ABPS)
olmak
üzere
dört
farklı
analiz
edilmiştir.
Analiz;
karbon
emisyonlarının
azaltılması,
enerji
verimliliği,
kaynak
kullanımı,
hava
kalitesine
etki
yenilenebilir
kullanımı
kriterleri
doğrultusunda
gerçekleştirilmiştir.
Analiz
sonuçlarına
göre,
EOS
en
yüksek
göreceli
yakınlık
değeriyle
iyi
performansı
sergilemiştir.
EOS,
azaltılması
verimliliği
konularında
üstün
performans
göstermekte
fosil
yakıt
kullanımını
azaltarak
optimize
etmektedir.
ABPS
ikinci
sırada
yer
alarak
çevre
dostu
bir
alternatif
olarak
öne
çıkmaktadır.
OAPS
üçüncü
almakta
olup,
açısından
diğer
sistemlere
göre
daha
düşük
sergilemektedir.
PESS
ise
göstermiştir.
Bulgular,
şehir
plancıları
politika
yapıcılar
için
önemli
bilgiler
sunmakta
hedefleri
uygun
seçeneklerin
belirlenmesine
katkı
sağlamaktadır.
Rapid
advancements
in
text
classification
algorithms
have
kept
pace
with
the
exponential
rise
of
digital
materials
recent
years.
In
order
to
automatically
extract
expressive
features,
new
machine
learning
been
suggested
that
take
advantage
developments
deep
techniques.
progress
this
area
has
spawned
a
multitude
ways
for
translating
human
speech
into
machine-readable
data.
Ad
hoc
pre-processing
processes
are
utilized
conjunction
state-of-the-art
language
modelling
algorithms;
nevertheless,
their
presentation
is
generally
glossed
over
favor
more
thorough
explanation
stage.
This
work
aims
at
building
model
used
analyze
sentiments,
where
accuracy
tested
by
taking
data
sets
positive
and
negative
movie
reviews.
Proposed
divided
3
tasks:
Data
Extraction,
Preprocessing
Modelling.
We
techniques
Machine
Learning
making
changes
vectorization
using
BOW
(Bag
Of
Words),
N-grams
TFIDF
built
Naïve
Bayes
Random
Forest
algorithms.
Our
findings
demonstrate
provides
superior
performance
terms
accuracy,
precision,
recall,
f-measure.
Using
cutting-edge
methods,
proposed
achieved
competitive
results
on
IMDB
review
dataset.
Research Square (Research Square),
Journal Year:
2023,
Volume and Issue:
unknown
Published: April 19, 2023
Abstract
In
this
study,
we
provide
an
IoT-based
approach
for
observing
and
managing
a
hydroponic
system.
A
multitude
of
sensors
is
built
into
the
system
to
assess
important
factors
like
temperature,
humidity,
nutrient
levels,
pH,
water
levels.
These
are
linked
microcontroller
or
single-board
computer,
which
gathers
sends
data
platform
that
situated
in
cloud
archival
analysis.
web
mobile
application
allows
users
remotely
access
manage
The
also
features
automated
control
modifies
levels
according
sensor
predetermined
criteria.
suggested
provides
all-inclusive
effective
regulating
monitoring
systems,
it
easily
adaptable
various
conditions
systems.
We
show
our
system's
utility
viability
through
experiments
results.
This
research
paper
presents
a
comparative
analysis
of
emotion
recognition
using
deep
learning
techniques.
The
aim
the
study
was
to
compare
performance
state-of-the-art
models,
namely
Convolutional
Neural
Network
(CNN),
Mobilenet
and
Long
Short-Term
Memory
(LSTM).
dataset
used
for
our
work
is
popular
FER
-
2013
dataset,
which
consist
annotated
tweets
with
labels.
proposed
evaluates
models
based
on
their
precision,
accuracy,
recall,
F1-score.
results
show
that
CNN
model,
trained
image
outperforms
other
in
terms
precision
also
analyzes
effect
various
pre-processing
techniques
models.
Overall,
provides
comprehensive
highlights
strengths
weaknesses
different
approaches.
this
are
useful
researchers
practitioners
working
fields
natural
language
processing
recognition.
European Journal of Computer Science and Information Technology,
Journal Year:
2024,
Volume and Issue:
12(3), P. 71 - 83
Published: March 15, 2024
Toll
collection
systems
utilizing
modulation
techniques
encounter
significant
challenges
related
to
signal
interference
and
environmental
conditions.
The
precise
transmission
reception
of
signals
are
critical
for
techniques,
but
they
can
be
disrupted
by
physical
obstacles,
weather
variations,
from
other
electronic
devices,
leading
degradation
potential
errors.
Moreover,
the
complexity
inherent
in
these
necessitates
advanced
infrastructure
ongoing
maintenance,
resulting
elevated
operational
expenses.
Addressing
requires
implementation
robust
technical
solutions,
rigorous
testing
procedures,
continuous
maintenance
ensure
efficient
secure
operation
toll
systems.
This
study
aims
develop
an
efficient,
cost-effective,
scalable,
system
using
Amplitude
Shift
Keying
(ASK)
modulation.
ASK
leverages
amplitude
variations
facilitate
data
between
RFID
tags
readers,
enabling
seamless
vehicle
passage
through
points.
selection
Arduino
Uno
microcontroller
is
based
on
its
affordability
reliability,
while
RC522
reader
chosen
their
compatibility
performance.
Real-time
feedback
provided
OLED
display,
MG996r
metal
gear
servo
utilized
operating
barrier.
offers
several
advantages
Its
simplicity
facilitates
easy
reduces
overall
costs,
making
it
a
financially
viable
option.
technique's
use
binary
representation
ensures
reliable
design
enhances
scalability
simplifies
requirements.
