IGI Global eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 163 - 184
Published: April 4, 2025
Cloud-based
legal
platforms
can
transform
aid
for
marginalized
communities,
according
to
this
chapter.
Geography,
cost,
and
social
stigma
prevent
many
people
from
seeking
aid.
Underserved
populations
are
disproportionately
affected
by
justice
gap,
which
makes
it
difficult
them
navigate
complex
systems
assert
their
rights.
Legal
service
providers
overcome
these
barriers
with
cloud
technology,
making
services
more
accessible
affordable.
Virtual
consultations,
document
submission,
case
management
allow
users
interact
lawyers
anywhere,
anytime.
Marginalised
where
location
or
cost
traditional
services,
need
flexibility.
Advances in computational intelligence and robotics book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 153 - 168
Published: Dec. 13, 2024
The
real-time
pool
operation
through
the
combination
of
Internet
Everything
(IoE)
and
Artificial
Intelligence
(AI).
purpose
this
study
is
to
evaluate
how
technologies,
which
connect
people,
processes,
data,
effects,
may
best
optimize
effectiveness
production
when
paired
with
predictive
logical
skills
artificial
intelligence
utilization
IoE
detectors
bias
allows
for
continuous
collection
analysis
provides
insight
into
hand
performance,
workload
allocation,
functional
backups.
systems
also
take
advantage
data
provide
decision
assistance,
enables
dynamic
work
allocation
envisions
solutions
problems.
Among
significant
advantages
that
are
brought
light
by
investigation
improved
resource
application,
increased
translucency,
enhanced
engagement.
practical
operations
problems
associated
integrated
approach
demonstrated
case
studies
coming
from
diligence.
Advances in computational intelligence and robotics book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 89 - 116
Published: Dec. 13, 2024
The
Internet
of
Things
(IoT)
has
emerged
as
a
transformative
technology
paradigm,
interconnecting
vast
array
devices
and
enabling
unprecedented
levels
data
collection,
analysis,
automation.
This
study
explores
the
foundational
concepts,
applications,
implications
IoT
across
various
domains.
facilitates
real-time
monitoring
control,
enhancing
efficiency
productivity
in
sectors
such
healthcare,
agriculture,
manufacturing,
smart
cities.
By
integrating
sensors,
actuators,
connectivity
technologies,
enables
seamless
communication
between
systems,
facilitating
intelligent
decision-making
predictive
analytics.However,
rapid
proliferation
also
raises
significant
challenges,
including
privacy
concerns,
security
risks,
interoperability
issues.
Addressing
these
challenges
requires
robust
protocols,
standards,
regulatory
frameworks
to
ensure
reliable
secure
operation
ecosystems.Looking
ahead,
continues
evolve
with
advancements
edge
computing,
artificial
intelligence,
5G
.
Advances in computational intelligence and robotics book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 567 - 586
Published: Dec. 13, 2024
Concrete
mix
design
is
a
critical
process
in
construction,
influencing
the
strength,
durability,
and
workability
of
concrete.
Traditional
methods
design,
such
as
empirical
analytical
approaches,
often
involve
trial-and-error
techniques
that
can
be
time-consuming
imprecise.
With
advancements
artificial
intelligence
(AI),
new
for
optimizing
concrete
mixes
have
emerged,
offering
enhanced
accuracy
efficiency.
This
paper
reviews
integration
AI
into
automated
with
specific
focus
on
role
computerized
grading
curves
aggregate
distribution.
Discover Nano,
Journal Year:
2025,
Volume and Issue:
20(1)
Published: Feb. 12, 2025
Abstract
A
highly
efficient
and
nontoxic
material
methylammoniumtin(II)
iodideperovskite
solar
cell
is
proposed.
This
proposed
uses
CH
3
NH
SnI
as
the
absorber
layer,
TiO
2
an
Electron
transport
layer
(ETL),
Indium
tin
oxide
a
buffer
Copper(I)
hole
(HTL).
The
device
simulated
using
SCAPS-1D
simulation
tool.
study
details
optimization
of
set
parameters,
including
defect
densities
thickness
layer.
structure
optimized
result
31.73%
enhanced
power
conversion
efficiency
(PCE),
J
SC
24.526
mA/cm
(short-circuit
current),
FF
81.40%
(fill
factor),
V
OC
1.56
(open-circuit
voltage)
obtained
through
process.
Compared
to
previously
reported
works,
performance
has
improved
significantly
due
better
optimization.
Along
with
this
electrical
characteristic
temperature
analyses,
conductance
voltage,
capacitance–voltage,
bandgap
analyses
have
also
been
carried
out
examine
device’s
performance.
Engineering Reports,
Journal Year:
2025,
Volume and Issue:
7(3)
Published: March 1, 2025
ABSTRACT
Circuit
board
analysis
plays
a
critical
role
in
ensuring
the
reliability
of
electronic
devices
by
identifying
temperature
distribution,
assessing
component
health,
and
detecting
potential
defects.
This
study
presents
novel
approach
to
infrared
image
segmentation
for
circuit
boards,
integrating
Markov
Random
Field
(MRF)
Level
Set
(LS)
techniques
enhance
accuracy
reliability.
The
proposed
method
leverages
probabilistic
modeling
capabilities
MRF
contour
evolution
strengths
LS
achieve
robust
images,
revealing
thermal
structural
features.
Experimental
results
demonstrate
that
MRF‐LS
achieves
an
86%,
precision
92%,
recall
94%
on
benchmark
dataset
PCB
images.
These
indicate
significant
improvements
over
conventional
methods,
including
k‐means
clustering
active
models,
which
yielded
accuracies
79%
81%,
respectively.
Furthermore,
shows
adaptability
fine‐grained
anomalies
defects,
with
enhanced
resolution
small
components.
also
discusses
other
imaging
modalities,
highlighting
its
scalability
versatility.
findings
underline
utility
framework
as
valuable
tool
advancing
analysis,
promising
applications
quality
control
predictive
maintenance
electronics
industry.
International Journal of Computational and Experimental Science and Engineering,
Journal Year:
2025,
Volume and Issue:
11(2)
Published: March 23, 2025
AI-driven
cybersecurity
has
emerged
as
a
transformative
solution
for
combating
increasingly
sophisticated
cyber
threats.
This
research
proposes
an
advanced
deep
learning-based
framework
aimed
at
enhancing
threat
detection
and
mitigation
performance.
Leveraging
Convolutional
Neural
Networks
(CNNs)
Long
Short-Term
Memory
(LSTM)
architectures,
the
proposed
model
effectively
identifies
anomalies
classifies
potential
threats
with
high
accuracy
minimal
false
positives.
The
was
rigorously
evaluated
using
real-time
network
traffic
datasets,
demonstrating
notable
increase
in
by
18.5%,
achieving
of
97.4%,
compared
to
traditional
machine
learning
methods
(78.6%).
Additionally,
response
time
significantly
reduced
25%,
while
computational
overhead
decreased
30%,
overall
system
responsiveness.
Experimental
results
further
show
40%
reduction
downtime
incidents
due
faster
identification
proactive
approach
thus
provides
substantial
improvements
security
performance
metrics,
underscoring
its
robust
dynamic
landscapes
