Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 449 - 468
Published: Jan. 1, 2024
Language: Английский
Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 34(18), P. 15313 - 15348
Published: June 10, 2022
Language: Английский
Citations
96
Journal of Personalized Medicine, Journal Year: 2022, Volume and Issue: 12(11), P. 1920 - 1920
Published: Nov. 17, 2022
The use of health applications (apps) in smartphones increased exponentially during COVID-19. This study was conducted the with aim to understand factors that determine consumer's perception apps towards telemedicine COVID-19 and test any relation between these consumers Telemedicine India.
Language: Английский
Citations
77
Information, Journal Year: 2024, Volume and Issue: 15(1), P. 39 - 39
Published: Jan. 11, 2024
Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing need for deep computer science expertise. Designed to make ML more accessible, they enable users build high-performing models without extensive technical knowledge. This study delves into these in context time series analysis, which is essential forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin COVID-19 The results reveal performance each tool highly dependent on specific dataset its ability manage complexities thorough investigation not only demonstrates strengths limitations but also highlights criticality dataset-specific considerations analysis. Offering valuable insights both practitioners researchers, this emphasizes ongoing research development specialized area. It aims serve as a reference organizations dealing with guiding framework academic enhancing application
Language: Английский
Citations
20
Oeconomia Copernicana, Journal Year: 2024, Volume and Issue: 15(1), P. 27 - 58
Published: March 30, 2024
Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis symptom tracing, optimize intensive care unit admission, use clinical data to determine patient prioritization mortality risk, being pivotal qualitative provision, reducing errors, increasing survival rates, thus diminishing the massive healthcare system burden relation severe inpatient stay duration, while operational costs throughout organizational management of hospitals. Data-driven financial scenario-based contingency planning, predictive modelling tools, risk pooling mechanisms should be deployed for additional equipment unforeseen demand expenses. Purpose article: We show that deep decision making systems likelihood treatment outcomes with regard susceptible, infected, recovered individuals, performing accurate analyses by modeling based on vital signs, surveillance data, infection-related biomarkers, furthering hospital facility optimization terms bed allocation. Methods: The review software employed article screening quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, SRDR. Findings & value added: support tools forecast spread, confirmed cases, infection rates data-driven appropriate resource allocations effective therapeutic protocol development, determining suitable measures regulations using symptoms comorbidities, laboratory records across units, impacting financing infrastructure. As a result heightened personal protective equipment, pharmacy medication, outpatient treatment, supplies, revenue loss vulnerability occur, also due expenses related hiring staff critical expenditures. Hospital care, screening, capacity expansion, lead further losses affecting frontline workers patients.
Language: Английский
Citations
15
Neurocomputing, Journal Year: 2022, Volume and Issue: 511, P. 142 - 154
Published: Sept. 8, 2022
Language: Английский
Citations
38
IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 46472 - 46489
Published: Jan. 1, 2022
Universities
around
the
world
are
keen
to
develop
study
plans
that
will
guide
their
graduates
success
in
job
market.
The
internship
course
is
one
of
most
significant
courses
provides
an
experiential
opportunity
for
students
apply
knowledge
and
prepare
start
a
professional
career.
However,
internships
do
not
guarantee
employability,
especially
when
graduate's
performance
satisfactory
requirements
met.
Many
factors
contribute
this
issue
making
prediction
employability
important
challenge
researchers
higher
education
field.
In
paper,
our
aim
introduce
effective
method
predict
student
based
on
context
using
Gradient
Boosting
classifiers.
Our
contributions
consist
harnessing
power
gradient
boosting
algorithms
perform
context-aware
status
processes.
Student
relies
identifying
predictive
features
impacting
hiring
graduates.
Hence,
we
define
two
models,
which
features.
Experiments
conducted
three
classifiers:
e
Language: Английский
Citations
32
IETE Journal of Research, Journal Year: 2023, Volume and Issue: 70(2), P. 1328 - 1341
Published: Feb. 9, 2023
AbstractIn any smart city and society, the citizens' mental health is one of utmost concerns. Nowadays, people from different sectors our community face a severe threat due to prolonged pandemic COVID-19. Depression, anxiety, suicidal behaviours, posttraumatic stress disorder are widespread terms nowadays for students, care workers, jobless people, etc. And Machine Learning (ML), image processing, expert systems, Internet Things (IoT) performing an essential function in significant acceleration automation process within healthcare industry. Therefore, this article aims address problem preventing disorders by early predicting individuals using developed web portal "Mind Turner"; integrating mentioned emerging tools way, later chronic can be avoided. We used Random Forest Classifier detect levels Question-Answer-based assessment, SVM facial emotions. Finally, both combined Interval Type-2 Fuzzy Logic predict probable person, i.e. acute depression, moderate depression not depressed.KEYWORDS: COVID-19DepressionImage processingInternet Health (IoHT)Machine learningMental illnessMental healthSmart Disclosure statementNo potential conflict interest was reported author(s).Correction StatementThis has been corrected with minor changes. These changes do impact academic content article.Additional informationNotes on contributorsArpita ChakrabortyArpita Chakraborty, BTech, MTech, PhD (Engg), assistant professor Electronics Communications Engineering Department at Bengal Institute Technology, Kolkata, India. Email: [email protected] Sekhar BanerjeeJyoti Banerjee, ME, PG Diploma IPR & TBM head CSE (AI ML) India visiting researcher (Post Doc) Nottingham Trent University, UK. Corresponding author. [email protected] BhadraRitam Bhadra pursuing bachelor's degree electronics communication engineering Kolkata. [email protected] DuttaAnik Dutta [email protected] GangulyShatabdi Ganguly [email protected] DasDeblina Das [email protected] KunduSouvik Kundu, graduate department Electrical Computer Engineering, Iowa State Ames, IA, USA. [email protected] MahmudMufti Mahmud associate cognitive computing Science He recipient top 2% cited scientists worldwide computer science (2020), NTU VC outstanding research award 2021, Marie-Curie postdoctoral fellowship. Mahmudis coordinator Informatics excellence framework unit assessment deputy group leader Cognitive Computing Brain Interactive Systems groups. [email protected] SahaGautam Saha, MBBS, MD, psychiatry (Calcutta University) senior psychiatrists founder director Clinic Neuropsychiatric Research Center, Kolkata-700124. diagnosis, treatment, prevention disorders, including addiction sexual disorders. At present, he president Indian Psychiatric Society (IPS), Vice-President SAARC Federation Psychiatry Association Geriatric Mental (IAGMH). [email protected]
Language: Английский
Citations
22
IEEE Transactions on Artificial Intelligence, Journal Year: 2023, Volume and Issue: 5(5), P. 2151 - 2162
Published: Sept. 12, 2023
The association between drugs and side effects encompasses information about approved medications their documented adverse drug reactions. Traditional experimental approaches for studying this tend to be time-consuming expensive. To represent all drug-side effect associations, a bipartite network framework is employed. Consequently, numerous computational methods have been devised tackle problem, focusing on predicting new potential associations. However, significant gap lies in the neglect of Multi-View Learning (MVL) algorithm, which has ability integrate diverse sources enhance prediction accuracy. In our study, we developed novel predictor named Semi-Supervised Sparse Model (Mv3SM) address problem. Our approach aims explore distinctive characteristics various view features obtained from fully paired multi-view data mitigate influence noisy data. test performance Mv3SM other approaches, conducted experiments using three benchmark datasets. results clearly demonstrate that proposed method achieves superior predictive compared alternative approaches.
Language: Английский
Citations
21
Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 240, P. 122670 - 122670
Published: Nov. 19, 2023
Language: Английский
Citations
9
International Journal of Fuzzy Systems, Journal Year: 2024, Volume and Issue: unknown
Published: June 3, 2024
Language: Английский
Citations
3