Safety and Health For Medical Workers,
Год журнала:
2024,
Номер
1(2), С. 78 - 94
Опубликована: Июль 10, 2024
Objective:
This
study
focuses
on
utilizing
Machine
Learning
(ML)
approaches
to
improve
Occupational
Safety
and
Health
(OSH)
performance,
involving
the
prediction
prevention
of
risks
based
data.Methods:
Analysis
a
dataset
550
OSH
incident
reports
from
Metax
Cancer
Hospital
(2019–2023)
was
conducted
using
descriptive
inferential
statistics.
algorithms
including
decision
trees,
random
forests,
support
vector
machines
were
used
for
evaluation
results.
The
models
evaluated
various
performance
metrics
such
as
accuracy,
precision,
recall,
AUC.Findings:
analysis
made
key
observations
both
workplace
environmental
factors,
safety
protocols,
occurrence.
ML
demonstrated
high
with
forests
achieving
best
accuracy
in
terms
correct
classification
events.
These
findings
highlight
promise
hospitals.Novelty:
We
propose
an
original
contribution
integration
process
towards
improvement
hospital
ecosystem
also
characterized
complex
challenges
which
predictive
analytics
can
yield
substantial
risk
mitigation.Research
Implications:
proposes
spillover
framework
establishing
intelligence
systems
that
combines
data-driven
techniques
traditional
management
structures.
It
highlights
role
real-time
improving
outcomes.
demonstrates
ability
facilitate
assessment
safety.
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 111 - 128
Опубликована: Фев. 28, 2025
The
primary
objective
of
this
chapter
is
to
evaluate
the
cybersecurity
framework
for
software
security,
which
has
gained
significant
importance
due
rise
in
malicious
attacks
and
other
hacker-related
threats
computer
systems
recent
years.
As
a
result,
numerous
researchers
have
explored
security
solutions
from
earliest
stages
requirement
engineering.
Given
growth
industry
internet,
it
essential
understand
associated
with
each
stage
development
life
cycle
(SDLC).
To
address
need,
we
conducted
an
industrial
empirical
study
assess
impact
at
every
SDLC.
Cybersecurity
become
top
priority
businesses
across
all
industries
sizes
as
digital
technology
data
are
now
integral
components
commercial
landscape.
Engineering Science & Technology Journal,
Год журнала:
2024,
Номер
5(8), С. 2649 - 2669
Опубликована: Авг. 31, 2024
The
increasing
demand
for
energy
efficiency
and
sustainability
in
the
industrial
sector
has
spurred
development
of
innovative
software
solutions
effective
management
systems
(EMS).
This
review
explores
key
advancements
applications
these
enhancing
practices.
Modern
EMS
leverages
cutting-edge
technologies
such
as
artificial
intelligence
(AI),
machine
learning,
Internet
Things
(IoT)
to
optimize
consumption,
reduce
operational
costs,
minimize
environmental
impact.
By
integrating
real-time
data
from
various
sensors
devices,
provide
comprehensive
insights
into
usage
patterns,
enabling
industries
identify
inefficiencies
implement
corrective
measures
promptly.
AI-driven
predictive
analytics
play
a
crucial
role
forecasting
optimizing
distribution
across
processes.
Machine
learning
algorithms
analyze
historical
predict
peak
periods,
allowing
proactive
load
reducing
risk
wastage.
Additionally,
IoT-enabled
devices
facilitate
seamless
communication
between
different
components
infrastructure,
ensuring
accurate
collection
monitoring.
One
significant
innovation
is
user-friendly
interfaces
dashboards
that
present
complex
an
accessible
format.
These
enable
facility
managers
operators
make
informed
decisions
quickly,
their
ability
manage
consumption
efficiently.
Moreover,
advanced
offer
automated
control
features
adjust
dynamically
based
on
predefined
parameters
conditions,
further
streamlining
Case
studies
industries,
manufacturing,
logistics,
centers,
demonstrate
tangible
benefits
implementing
software.
include
reductions
improved
regulatory
compliance,
enhanced
performance.
For
instance,
manufacturing
plant
utilizing
AI-powered
reported
15%
decrease
within
first
year
implementation,
highlighting
potential
substantial
savings.
In
conclusion,
developing
aiming
achieve
goals.
harnessing
power
AI,
IoT,
actionable
insights,
automate
control,
promote
sustainable
Continued
research
this
field
will
enhance
capabilities
software,
driving
progress
toward
more
energy-efficient
sector.
Keywords:
Industry,
Software
Solutions,
Innovative,
Effective,
Energy
Management
System.
International Medical Science Research Journal,
Год журнала:
2024,
Номер
4(8), С. 806 - 833
Опубликована: Авг. 23, 2024
Artificial
intelligence
(AI)
is
transforming
personalized
medicine
by
enhancing
drug
efficacy
and
reducing
adverse
effects,
promising
a
new
era
of
precision
healthcare.
This
paper
explores
the
role
AI
in
revolutionizing
therapies
tailoring
treatments
to
individual
patient
profiles,
thereby
optimizing
therapeutic
outcomes
minimizing
risks.
leverages
vast
amounts
medical
data,
including
genetic
information,
electronic
health
records
(EHRs),
real-time
monitoring
create
comprehensive
profiles.
Machine
learning
algorithms
analyze
these
profiles
identify
patterns
correlations
that
might
not
be
apparent
human
practitioners.
enables
development
treatment
plans
consider
patient's
unique
makeup,
lifestyle,
existing
conditions.
One
critical
applications
pharmacogenomics,
which
studies
how
genes
affect
person’s
response
drugs.
can
variations
influence
metabolism,
efficacy,
toxicity,
allowing
healthcare
providers
predict
medications
dosages
will
most
effective
for
patients.
reduces
trial-and-error
approach
traditionally
used
prescribing
medications,
incidence
reactions
(ADRs).
also
plays
significant
repurposing
development.
By
analyzing
data
outcomes,
uses
potential
side
effects
before
clinical
trials,
accelerating
process
costs.
Moreover,
AI-driven
predictive
analytics
continuously
monitor
responses
treatment,
adjusting
maintain
optimal
levels.
particularly
beneficial
managing
chronic
conditions
such
as
diabetes,
hypertension,
cancer,
where
maintaining
correct
dosage
crucial
disease
management.
Despite
its
promise,
integration
faces
challenges,
privacy
concerns,
need
robust
regulatory
frameworks,
ensuring
equitable
access
innovations.
Addressing
challenges
requires
collaborative
efforts
from
providers,
researchers,
policymakers,
technology
developers.
In
conclusion,
at
forefront
medicine,
Continued
advancements
technologies
supportive
policies
realizing
full
ultimately
leading
more
safer
solutions.
Keywords:
AI,
Drug
Efficacy,
Personalized
Medicine,
Enhancing,
Reducing
Adverse
Effect.
International Journal of Applied Research in Social Sciences,
Год журнала:
2024,
Номер
6(8), С. 1797 - 1823
Опубликована: Авг. 21, 2024
Artificial
intelligence
(AI)
and
big
data
analytics
have
emerged
as
powerful
tools
in
enhancing
public
health
surveillance,
particularly
rural
communities
where
traditional
monitoring
methods
face
significant
challenges.
These
technologies
offer
the
potential
to
transform
how
is
collected,
analyzed,
utilized,
enabling
more
effective
timely
responses
threats.
Rural
often
struggle
with
limited
healthcare
infrastructure,
making
it
difficult
monitor
respond
issues
effectively.
AI
can
bridge
this
gap
by
providing
advanced
capabilities
for
real-time
collection
analysis.
algorithms
process
vast
amounts
of
from
various
sources,
including
electronic
records,
mobile
applications,
social
media,
environmental
sensors.
This
enables
identification
patterns
trends
that
may
indicate
emerging
threats,
such
outbreaks
infectious
diseases
or
increases
chronic
conditions.
Big
allows
integration
analysis
diverse
datasets,
a
comprehensive
view
areas.
holistic
approach
officials
identify
high-risk
populations,
track
spread
diseases,
evaluate
effectiveness
interventions.
For
instance,
AI-powered
predictive
models
forecast
disease
based
on
historical
current
trends,
allowing
proactive
measures
mitigate
impact.
Moreover,
these
enhance
accuracy
efficiency
surveillance.
automate
processing
tasks,
reducing
time
resources
required
manual
authorities
quickly
improving
overall
outcomes
communities.
Additionally,
aid
identifying
correlations
between
factors
issues,
insights
inform
policies
Despite
benefits,
implementing
surveillance
faces
several
Data
privacy
security
concerns
must
be
addressed
ensure
confidentiality
information.
Furthermore,
areas
lack
technological
infrastructure
expertise
needed
fully
leverage
technologies.
Overcoming
challenges
requires
investment
training
professionals,
development
user-friendly
applications
tailored
needs
In
conclusion,
hold
promise
By
leveraging
technologies,
improve
detection,
monitoring,
response
ultimately
leading
better
populations.
Keywords:
AI,
Analytics,
Public
Health,
Surveillance,
Communities.
International Medical Science Research Journal,
Год журнала:
2024,
Номер
4(8), С. 834 - 858
Опубликована: Авг. 23, 2024
Antimicrobial
resistance
(AMR)
poses
a
significant
threat
to
global
health,
complicating
the
treatment
of
infectious
diseases
and
leading
increased
morbidity
mortality.
Innovative
drug
delivery
methods
are
emerging
as
critical
strategies
combat
AMR
by
enhancing
efficacy
existing
antibiotics
facilitating
development
new
therapeutic
approaches.
This
paper
explores
role
novel
systems
in
addressing
challenges.
One
primary
approaches
is
targeted
that
improve
precision
antibiotic
therapy.
Nanotechnology
has
revolutionized
this
field,
enabling
creation
nanoparticles
nanocarriers
can
deliver
drugs
directly
infection
sites,
reducing
systemic
side
effects
concentration
at
target.
These
advanced
be
engineered
release
controlled
manner,
overcoming
bacterial
mechanisms
minimizing
likelihood
development.
Another
promising
strategy
involves
use
combination
therapies
delivered
through
innovative
methods.
By
combining
with
adjuvants
or
resistance-modifying
agents,
these
counteract
restore
effectiveness
drugs.
For
instance,
platforms
co-deliver
inhibitors
efflux
pumps
biofilm
formation
enhance
resistant
infections.
The
integration
smart
systems,
which
respond
environmental
stimuli
such
pH
changes
specific
enzymes,
offers
additional
advantages.
only
presence
bacteria,
thereby
overall
exposure
bacteria
decreasing
risk
resistance.
also
include
long-acting
formulations
implants
provide
sustained
over
extended
periods.
Such
reduce
frequency
dosing,
patient
adherence,
ensure
consistent
levels,
crucial
for
managing
chronic
infections
preventing
In
conclusion,
pivotal
fight
against
AMR.
targeting,
therapies,
utilizing
sustained-release
offer
solutions
curb
Continued
research
area
essential
advancing
ensuring
effective
management
face
rising
antimicrobial
Keywords:
Innovative,
Drug
Delivery
Methods,
Combating,
Antimicrobial,
Resistance.
Computer Science & IT Research Journal,
Год журнала:
2024,
Номер
5(8), С. 1828 - 1854
Опубликована: Авг. 23, 2024
The
integration
of
artificial
intelligence
(AI)
in
healthcare
has
the
potential
to
revolutionize
early
diagnosis
chronic
diseases,
particularly
rural
areas
where
resources
are
often
limited.
This
paper
explores
transformative
impact
AI
technologies
on
identifying
diseases
at
their
earliest
stages,
enhancing
patient
outcomes,
and
alleviating
burden
systems.
AI's
ability
analyze
vast
amounts
data
rapidly
accurately
enables
detection
such
as
diabetes,
hypertension,
cardiovascular
conditions.
Machine
learning
algorithms
can
process
from
various
sources,
including
electronic
health
records
(EHRs),
wearable
devices,
diagnostic
imaging,
identify
patterns
biomarkers
indicative
disease
onset.
predictive
capability
allows
providers
intervene
sooner,
potentially
preventing
progression
reducing
long-term
costs.
In
areas,
access
specialized
medical
expertise
advanced
tools
is
constrained,
AI-driven
offer
a
significant
advantage.
Telemedicine
platforms
integrated
with
facilitate
remote
consultations,
assists
interpreting
providing
suggestions.
approach
not
only
expands
quality
but
also
empowers
local
decision-support
tools,
improving
accuracy
management.
Moreover,
help
mitigate
challenges
limited
personnel
regions
by
automating
routine
tasks
enabling
workers
focus
more
complex
cases.
For
instance,
AI-powered
imaging
analysis
quickly
screen
large
populations
for
signs
flagging
suspicious
cases
further
review
professionals.
deployment
settings
fosters
continuous
monitoring
personalized
care
through
connected
devices.
These
devices
collect
real-time
data,
which
systems
provide
actionable
insights
alerts
both
patients
providers.
proactive
ensures
timely
interventions
enhances
adherence
treatment
plans.
conclusion,
into
significantly
improves
offering
scalable
solution
address
disparities
outcomes
between
urban
populations.
Continued
investment
infrastructure,
along
targeted
training
providers,
essential
realize
full
transforming
life
millions.
Keywords:
AI,
Impact,
Early
Diagnostic,
Chronic
Disease,
Rural
Areas.
International Journal of Applied Research in Social Sciences,
Год журнала:
2024,
Номер
6(8), С. 1725 - 1746
Опубликована: Авг. 21, 2024
Mobile
health
(mHealth)
applications
offer
a
transformative
approach
to
disease
management,
particularly
in
rural
areas
where
healthcare
resources
are
often
limited.
This
systematic
review
explores
the
role
of
mHealth
enhancing
management
settings,
focusing
on
their
effectiveness,
challenges,
and
potential
benefits.
The
systematically
assesses
literature
designed
for
areas,
highlighting
key
findings
from
various
studies.
reveals
that
can
significantly
improve
access
services,
enable
remote
monitoring,
facilitate
timely
interventions.
These
include
features
such
as
symptom
tracking,
medication
reminders,
education,
telemedicine
capabilities,
which
collectively
enhance
patient
outcomes.
For
instance,
apps
chronic
diabetes
hypertension,
tools
self-monitoring
personalized
feedback,
thereby
improving
adherence
treatment
regimens
fostering
better
control.
However,
also
identifies
several
challenges
associated
with
implementation
areas.
Key
issues
limited
internet
access,
variability
digital
literacy,
concerns
about
data
privacy
security.
effectiveness
is
constrained
by
these
factors,
need
tailored
solutions
address
unique
needs
populations.
Despite
underscores
bridge
gaps
delivery
By
providing
scalable
accessible
solutions,
have
outcomes
underserved
regions.
Future
research
should
focus
developing
context-specific
barriers
identified
exploring
strategies
effective
integration
into
existing
systems.
Overall,
represent
promising
avenue
advancing
impact
public
health.
Keywords:
Rural
Areas,
Systematic,
Disease,
Management,
Health
Application.
Computer Science & IT Research Journal,
Год журнала:
2024,
Номер
5(8), С. 2040 - 2061
Опубликована: Авг. 31, 2024
In
an
increasingly
interconnected
world,
the
need
for
software
applications
that
function
seamlessly
across
diverse
devices
and
operating
systems
is
paramount.
Developing
crossplatform
addresses
this
by
providing
a
unified
user
experience
operational
efficiency
regardless
of
hardware
or
system
being
used.
This
approach
eliminates
multiple
versions
same
application,
streamlining
development
reducing
costs
while
improving
accessibility
consistency.
Crossplatform
involves
creating
compatible
with
various
such
as
Windows,
macOS,
iOS,
Android,
well
different
device
types
including
desktops,
tablets,
smartphones.
Key
methodologies
in
domain
include
use
frameworks
tools
React
Native,
Flutter,
Xamarin,
which
allow
developers
to
write
code
once
deploy
it
platforms.
These
offer
range
features
enhance
interfaces,
manage
resources
efficiently,
ensure
robust
performance
devices.
The
benefits
are
manifold.
They
provide
consistent
experience,
application
behaves
similarly
devices,
enhancing
usability
customer
satisfaction.
Additionally,
they
simplify
maintenance
updates,
changes
only
be
implemented
rather
than
codebases.
also
accelerates
timetomarket
leveraging
shared
codebases,
thereby
enabling
faster
cycles
quicker
deployment.
However,
developing
presents
challenges.
Ensuring
functionality
can
complex,
requiring
careful
design
testing.
Developers
must
navigate
varying
capabilities
interface
guidelines
Despite
these
challenges,
advances
continue
improve
effectiveness
solutions.
conclusion,
represents
strategic
compatibility
systems.
By
modern
tools,
organizations
deliver
cohesive,
highquality
meet
needs
base
optimizing
costs.
Keywords:
:
Developing,
CrossPlatform,
Software
Applications,
Compatibility,
Devices.
Safety and Health For Medical Workers,
Год журнала:
2024,
Номер
1(2), С. 78 - 94
Опубликована: Июль 10, 2024
Objective:
This
study
focuses
on
utilizing
Machine
Learning
(ML)
approaches
to
improve
Occupational
Safety
and
Health
(OSH)
performance,
involving
the
prediction
prevention
of
risks
based
data.Methods:
Analysis
a
dataset
550
OSH
incident
reports
from
Metax
Cancer
Hospital
(2019–2023)
was
conducted
using
descriptive
inferential
statistics.
algorithms
including
decision
trees,
random
forests,
support
vector
machines
were
used
for
evaluation
results.
The
models
evaluated
various
performance
metrics
such
as
accuracy,
precision,
recall,
AUC.Findings:
analysis
made
key
observations
both
workplace
environmental
factors,
safety
protocols,
occurrence.
ML
demonstrated
high
with
forests
achieving
best
accuracy
in
terms
correct
classification
events.
These
findings
highlight
promise
hospitals.Novelty:
We
propose
an
original
contribution
integration
process
towards
improvement
hospital
ecosystem
also
characterized
complex
challenges
which
predictive
analytics
can
yield
substantial
risk
mitigation.Research
Implications:
proposes
spillover
framework
establishing
intelligence
systems
that
combines
data-driven
techniques
traditional
management
structures.
It
highlights
role
real-time
improving
outcomes.
demonstrates
ability
facilitate
assessment
safety.
