The
acoustic
backscatter
coefficient
values
obtained
from
Echosounders
provides
important
information
about
the
presence
of
fishes
in
water.
There
are
several
developments
underwater
technology
such
as
single
beam
echo
sounder,
multi
frequency
side
scan
radar.
But
there
challenges
associated
with
this
interpretation
echograms
generated
these
devices
time
consuming,
is
requirement
technical
experts
to
understand
and
detection
fish
species
still
a
challenge
etc.
recent
advancement
field
integration
signal
Artificial
Intelligence
algorithms.
Machine
Learning,
Deep
Fuzzy
Logic
some
advanced
algorithms
which
used
for
automatic
classification
that
aids
fishermen
identify
locations
fishes.
Hence,
review
article
focusses
on
role
advance
sea
can
help
saving
their
by
precisely
locating
A
trained
AI
program
locate
areas
scene
recognize
feature
patterns.
Fish
recognition
categorization
3D
photos
have
both
been
successfully
accomplished
using
Echo
sounder
object
framework.
Sustainability,
Год журнала:
2023,
Номер
15(18), С. 13493 - 13493
Опубликована: Сен. 8, 2023
Artificial
intelligence
(AI)
and
deep
learning
(DL)
have
shown
tremendous
potential
in
driving
sustainability
across
various
sectors.
This
paper
reviews
recent
advancements
AI
DL
explores
their
applications
achieving
sustainable
development
goals
(SDGs),
renewable
energy,
environmental
health,
smart
building
energy
management.
has
the
to
contribute
134
of
169
targets
all
SDGs,
but
rapid
these
technologies
necessitates
comprehensive
regulatory
oversight
ensure
transparency,
safety,
ethical
standards.
In
sector,
been
effectively
utilized
optimizing
management,
fault
detection,
power
grid
stability.
They
also
demonstrated
promise
enhancing
waste
management
predictive
analysis
photovoltaic
plants.
field
integration
facilitated
complex
spatial
data,
improving
exposure
modeling
disease
prediction.
However,
challenges
such
as
explainability
transparency
models,
scalability
high
dimensionality
with
next-generation
wireless
networks,
ethics
privacy
concerns
need
be
addressed.
Future
research
should
focus
on
developing
scalable
algorithms
for
processing
large
datasets,
exploring
addressing
considerations.
Additionally,
efficiency
models
is
crucial
use
technologies.
By
fostering
responsible
innovative
use,
can
significantly
a
more
future.
Humanities and Social Sciences Communications,
Год журнала:
2025,
Номер
12(1)
Опубликована: Янв. 3, 2025
Abstract
Marine
fisheries
constitute
a
crucial
component
of
global
green
development,
where
artificial
intelligence
(AI)
plays
an
essential
role
in
enhancing
economic
efficiency
associated
with
marine
fisheries.
This
study
utilizes
panel
data
from
11
coastal
provinces
and
municipalities
China
2009
to
2020,
employing
the
entropy
method
super-efficiency
EBM
model
calculate
AI
index
Based
on
these
calculations,
we
utilize
fixed
effects
models,
moderation
effect
threshold
models
examine
impact
The
reveals
that:
(i)
From
has
significantly
improved
overall,
while
shown
fluctuating
trend,
substantial
regional
disparities.
(ii)
enhances
(iii)
Green
finance,
trade
openness,
R&D
investment
act
as
moderating
variables,
accelerating
development
further
improving
(iv)
varies
across
different
intervals
investment.
These
findings
are
for
understanding
advancing
informatization
strategy
hold
significant
implications
sustainable
PLoS ONE,
Год журнала:
2023,
Номер
18(4), С. e0284992 - e0284992
Опубликована: Апрель 26, 2023
Regular
monitoring
of
the
number
various
fish
species
in
a
variety
habitats
is
essential
for
marine
conservation
efforts
and
biology
research.
To
address
shortcomings
existing
manual
underwater
video
sampling
methods,
plethora
computer-based
techniques
are
proposed.
However,
there
no
perfect
approach
automated
identification
categorizing
species.
This
primarily
due
to
difficulties
inherent
capturing
videos,
such
as
ambient
changes
luminance,
camouflage,
dynamic
environments,
watercolor,
poor
resolution,
shape
variation
moving
fish,
tiny
differences
between
certain
study
has
proposed
novel
Fish
Detection
Network
(FD_Net)
detection
nine
different
types
using
camera-captured
image
that
based
on
improved
YOLOv7
algorithm
by
exchanging
Darknet53
MobileNetv3
depthwise
separable
convolution
3
x
filter
size
augmented
feature
extraction
network
bottleneck
attention
module
(BNAM).
The
mean
average
precision
(mAP)
14.29%
higher
than
it
was
initial
version
YOLOv7.
utilized
method
features
an
DenseNet-169,
loss
function
Arcface
Loss.
Widening
receptive
field
improving
capability
achieved
incorporating
dilated
into
dense
block,
removing
max-pooling
layer
from
trunk,
BNAM
block
DenseNet-169
neural
network.
results
several
experiments
comparisons
ablation
demonstrate
our
FD_Net
mAP
YOLOv3,
YOLOv3-TL,
YOLOv3-BL,
YOLOv4,
YOLOv5,
Faster-RCNN,
most
recent
model,
more
accurate
target
tasks
complex
environments.
Annals of Animal Science,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 29, 2024
Abstract
The
current
work
investigates
the
prospective
applications
of
Artificial
Intelligence
(AI)
in
aquaculture
industry.
AI
depends
on
collecting,
validating,
and
analyzing
data
from
several
aspects
using
sensor
readings,
feeding
sheets.
is
an
essential
tool
that
can
monitor
fish
behavior
increase
resilience
quality
seafood
products.
Furthermore,
algorithms
early
detect
potential
pathogen
infections
disease
outbreaks,
allowing
stakeholders
to
take
timely
preventive
measures
subsequently
make
proper
decision
appropriate
time.
predict
ecological
conditions
should
help
farmers
adopt
strategies
plans
avoid
negative
impacts
farms
create
easy
safe
environment
for
production.
In
addition,
aids
analyze
collect
regarding
nutritional
requirements,
nutrient
availability,
price
could
adjust
modify
their
diets
optimize
feed
formulations.
Thus,
reduce
labor
costs,
aquatic
animal’s
growth,
health,
formulation
waste
output
detection
outbreaks.
Overall,
this
review
highlights
importance
achieve
sustainability
boost
net
profits
