Bleaching,
dying,
straightening,
curling,
and
other
chemical
treatments
for
hair
are
becoming
increasingly
common
around
the
world
as
people's
interest
in
hairstyles
colouring
is
increasing.
As
a
result,
has
sustained
significant
damage
that
can
be
observed
with
naked
eye
by
touching
texture.
The
chemicals
applied
to
produce
severe
health
issues
such
skin
cancer,
migraine,
fall.
Despite
dangerous
consequences
of
treatments,
people
still
applying
these
chemicals.
disease
detected
at
its
early
stages
lead
reducing
loss
avoiding
cancer
migraine.
With
advancements
technologies,
methods
detection
also
developing.
In
proposed
work,
dataset
been
collected
from
Kaggle
which
further
implemented
using
convolutional
neural
network
model.
results
have
calculated
different
epochs
two
optimizers
namely,
SGD
Adam
identified
model
outperforms
epoch
85
ADAM
optimizer
achieving
an
accuracy
rate
95%.
achieved
highest
89%
50.
This
better
outcomes
when
compared
existing
models.
Early
and
correct
diagnosis
of
scalp
hair
loss
is
essential
to
provide
prompt
efficient
treatment
programs
stop
future
progression
reduce
medical
expenses.
Deep
learning
has
been
used
build
a
variety
methods
for
automating
the
loss-detecting
process.
However,
practice
still
needs
be
improved
due
precision
reliability
determining
severity
loss.
We
designed
Cat
Swarm-based
Convolutional
Neural
System
(CS-CNS)
overcome
these
issues
follicle
segmentation
status
classification.
First,
images
are
collected
trained
in
system.
Then
dataset
preprocessed
using
Adaptive
Weiner
Filter
(AWF).
Moreover,
feature
extraction
employed
Hexagonal
Scale
Invariant
Feature
Transform
(H-SIFT).
Additionally,
Cellular
Automation
based
Rough
Set
Theory
(CA-RST)
improve
In
classification
phase,
update
fitness
cat
swarm
accurate
prediction
status,
such
as
normal,
serve,
healthy.
Each
receives
score
that
calculated
adjusted
fall
between
0
2.
Finally,
experimental
outcomes
model
validated
with
other
prevailing
models
terms
accuracy,
precision,
recall,
F1-score,
error
rate.
World Journal of Advanced Research and Reviews,
Год журнала:
2023,
Номер
19(1), С. 455 - 463
Опубликована: Июль 12, 2023
The
use
of
technology
in
healthcare
has
become
increasingly
popular
recent
years,
with
the
potential
to
improve
how
is
delivered,
patient
outcomes,
and
cost-effectiveness.
This
review
paper
provides
an
overview
been
used
healthcare,
particularly
cities
for
personalized
medicine.
discusses
different
ways
being
such
as
electronic
health
records,
telemedicine,
remote
monitoring,
medical
imaging,
wearable
devices,
artificial
intelligence.
It
also
looks
at
challenges
problems
that
come
using
keeping
data
private
secure,
making
sure
systems
can
work
together,
ensuring
patients
are
comfortable
technology.
In
addition,
explores
including
improving
easily
get
care,
quality
care
they
receive,
cost
care.
talks
about
help
personalize
individual
patients.
Finally,
summarizes
main
points,
makes
recommendations
providers
policymakers,
suggests
directions
future
research.
Overall,
this
shows
be
while
acknowledging
way.
International Journal of Current Science Research and Review,
Год журнала:
2023,
Номер
06(07)
Опубликована: Июль 12, 2023
Millions
of
individuals
all
ages
are
affected
by
skin
diseases,
a
widespread
problem
worldwide.
Early
diagnosis
and
detection
essential
for
these
diseases
to
be
effectively
treated
improve
patient
outcomes.
Automated
disease
systems
viable
way
increase
diagnostic
accuracy
lighten
the
workload
dermatologists,
developments
in
machine
learning
computer
vision.
These
examine
lesions
categorize
them
into
several
groups
using
various
techniques,
including
feature
extraction,
deep
learning,
image
processing.
Such
still
being
developed
enhance
their
precision
usefulness.
This
paper
provides
an
overview
different
information
technologies
detection,
effectiveness,
challenges
limitations
existing
systems,
future
research
directions
this
field.
International Journal on Recent and Innovation Trends in Computing and Communication,
Год журнала:
2023,
Номер
11(6s), С. 499 - 508
Опубликована: Июнь 13, 2023
Hair
fall,
a
prevalent
issue
affecting
many
individuals
globally,
necessitates
early
detection
for
preventive
measures
and
hair
health
maintenance.
Machine
learning
algorithms
have
gained
attention
in
predicting
fall
by
analysing
genetic
predisposition,
lifestyle
habits,
environmental
factors.
However,
the
performance
of
individual
can
be
improved
through
ensemble
models
that
combine
their
strengths.
This
research
paper
proposes
an
machine
approach
tailored
prediction.
Comparative
evaluations
with
reveal
consistently
outperform
accuracy,
precision,
recall.
Leveraging
diverse
algorithms,
captures
wider
range
patterns,
enhancing
prediction
accuracy.
The
also
exhibit
higher
precision
recall
rates,
correctly
identifying
both
non-hair
instances.
models'
superiority
stems
from
mitigating
limitations
resulting
comprehensive
robust
framework.
Overall,
this
showcases
efficacy
prediction,
enabling
intervention
loss
prevention.
These
findings
provide
valuable
insights
researchers,
practitioners,
concerned
about
health.
Folliculitis
is
a
common
skin
condition
that
happens
when
hair
follicle(s)
become
inflamed.
The
cause
of
inflammation
can
be
bacterial
or
fungal
infection,
ingrown
due
to
removal
etc.
Based
on
the
in
follicle,
folliculitis
categorized
into
eight
different
types.
If
left
untreated,
it
may
spread,
deep
infections
which
further
permanent
loss,
scarring,
cellulitis
and
even
pass
bloodstream
fatal.
Dermatologists
usually
diagnose
only
by
glancing
at
patient's
skin.
However,
order
find
folliculitis,
dermatologists
recommend
taking
tissue
sample,
swab
having
laboratory
tests
done.
Additionally,
sample
extracted
from
troubled
areas
obtained
for
testing.
Using
potassium
hydroxide,
samples
are
microscopically
examined
identify
potentially
infectious
cause.
In
this
paper,
we
investigate
how
accurate
CNNs
identifying
type
folliculitis.
We
will
use
Convolutional
Neural
Network
(CNN)
models
like
AlexNet,
DenseNet201,
GoogLeNet,
InceptionV3,
ResNet50,
VGG19
Xception.
results
show
GoogLeNet
performs
best
Recent Advances in Computer Science and Communications,
Год журнала:
2023,
Номер
17(2)
Опубликована: Дек. 7, 2023
Abstract:
This
journal
paper
examines
the
transformative
role
of
Artificial
Intelligence
(AI)
and
Machine
Learning
(ML)
in
shaping
human
psychology.
It
investigates
how
cognitive
processes,
emotional
states,
social
interactions
are
impacted
by
AI
ML
technology.
The
use
psychology
is
covered
this
study,
covering
behaviour
analysis,
emotion
identification,
mental
health
assessment,
personalised
therapies.
also
explores
moral
issues
prospective
effects
comprehending
influencing
emphasises
enormous
influence
on
comprehension
research
through
a
thorough
analysis
pertinent
literature
empirical
evidence.
seeks
to
offer
explanation
profound
that
have
had
We
will
insight
into
possible
advantages,
difficulties,
ethical
occur
when
integrating
study
looking
at
recent
developments
implementations
these
technologies
psychological
research.
look
other
areas
psychology,
such
as
clinical
neurology,
been
ML.
Bleaching,
dying,
straightening,
curling,
and
other
chemical
treatments
for
hair
are
becoming
increasingly
common
around
the
world
as
people's
interest
in
hairstyles
colouring
is
increasing.
As
a
result,
has
sustained
significant
damage
that
can
be
observed
with
naked
eye
by
touching
texture.
The
chemicals
applied
to
produce
severe
health
issues
such
skin
cancer,
migraine,
fall.
Despite
dangerous
consequences
of
treatments,
people
still
applying
these
chemicals.
disease
detected
at
its
early
stages
lead
reducing
loss
avoiding
cancer
migraine.
With
advancements
technologies,
methods
detection
also
developing.
In
proposed
work,
dataset
been
collected
from
Kaggle
which
further
implemented
using
convolutional
neural
network
model.
results
have
calculated
different
epochs
two
optimizers
namely,
SGD
Adam
identified
model
outperforms
epoch
85
ADAM
optimizer
achieving
an
accuracy
rate
95%.
achieved
highest
89%
50.
This
better
outcomes
when
compared
existing
models.
