[Opt-Net] CFP in Topical Issue on 'Machine Learning for Pandemic Prediction and Control'

[Dr. Anand Kulkarni]

Journal of Springer Nature Computer Science

Submission opens: July 29, 2020

For Submissions refer to: https://www.editorialmanager.com/sncs/default.aspx
Journal homepage: https://www.springer.com/journal/42979


Aims, Scope and Objectives of the Topical Issue:

Pandemic is considered as one of the major problems associated with the
spread of infectious disease crossing international boundaries. The list of
major pandemics includes Black Death a.k.a. the Plague (year 1347), H1N1
(1918), H2N2 Pandemic (1957-1958), H3N2 Pandemic (1968), H1N1 Pandemic
(2009) and Coronavirus Disease (COVID-19). The past data reveals that the
impact of pandemic situations severely affects and disrupts economy,
agricultural and industry growth and relations, employment, supply chains,
services, tourism, and many more aspects of human activities. The enormity
and vast scale of disruption is mainly due to lack of preparedness. This
necessitates the scientific and research community to provide effective
methods, strategies, forecasting techniques and models, to understand and
mitigate the effects of pandemic.
The domain of Machine Learning (ML) has been adopted to solve complex
problems in various domains such as engineering, medical, banking, weather,
mission critical activities, failure and anomalies detection, prediction,
etc. The main reason is that ML techniques are non-parametric and are
centred on rules to build algorithms which can learn and predict the
outcome with certain level of confidence. They identify the underlying
patterns in the input data and generate systematic relationship using
statistical techniques to predict the output accurately (within limits).
The ML techniques, along with Meta-Heuristics and Big Data Analytics can
help in designing appropriate strategies and applications thereby
minimizing the potential burden of pandemics on society. ML could be a
potential tool in modelling, prediction and prescriptive analysis of a wide
range of problems associated with pandemic.
We intend to invite elite and reflective contributions with a focus on
development and use of effective ML based tools to predict possible
outbreak, trend of spread, measures and effects of prevention, possible
allocation of resources and supply chain management, patient recovery
rates, handling emergency, medical waste management, etc. The experts from
the domains associates with the big data, artificial intelligence, and
policy making are encouraged to submit high quality and scientifically
sound papers describing novel methods, datasets, tools or innovative
solutions which can significantly contribute to the prediction and control
of pandemic.


Topics of Interest (not limited to)

• Big data and ML in Pandemic risk analysis, tracking and prediction models
• Applications of the Internet of Things in healthcare
• Social network analysis for contact tracing
• Information technologies in hospital management during an epidemic or
pandemic
• Artificial intelligence security in analysis of public health emergencies
• Secure and privacy-preserving analysis of data in public health
emergencies
• Optimal allocation of resources during a pandemic or epidemic
• Telemedicine for managing non-emergent patients
• Predictive Analytics in pandemic patient risk profiling, patterns
analysis for diagnosis
• Innovative solutions for sustainable and resilient supply chains in the
face of pandemics
• Resource and environmental impacts of mitigation strategies (e.g.,
lockdowns, working from home)
• Implications and solutions on municipal solid waste management (e.g.,
surges in household waste due to increased online shopping)
• Implications and solutions on healthcare waste
• Solutions for preventing virus spreading through wastewater and solid
waste streams
• Opportunities for circular economy after Pandemic (e.g., behaviour
continued after Pandemic, supply chain changes, community
self-organization, product design, localization of supply and production)


Editors

Dr Anand J KULKARNI
Symbiosis Center for Research and Innovation
Symbiosis International (Deemed University)
Pune 412115, MH, INDIA
Email: anand.kulkarni at sitpune.edu.in; kulk0003 at ntu.edu.sg

Dr Akash TAYAL
Department of Electronic & Communication
Indira Gandhi Delhi Technical of University for Women
Delhi 110006, INDIA
Email: akashtayal at yahoo.com

Dr Patrick SIARRY

Université de Paris-Est Créteil Val de Marne (UPEC)
Laboratoire LiSSi (E.A. 3956)
61 avenue du Général de Gaulle, 94010 Créteil, FRANCE
Email: siarry at u-pec.fr

Dr Arun SOLANKI
Gautam Buddha University, Greater Noida, INDIA
School of ICT, Gautam Buddha University, Greater Noida, INDIA
Email: ymca.arun at gmail.com

Dr Ali Husseinzadeh KASHAN
Faculty of Industrial and Systems Engineering
Tarbiat Modares University
Tehran, IRAN
Email: a.kashan at modares.ac.ir

-- 
Anand J Kulkarni  PhD, MASc, BEng, DME

Associate Professor
Symbiosis Center for Research and Innovation
Symbiosis International (Deemed University)
Pune 412 115, MH, India
Email: anand.kulkarni at sitpune.edu.in
kulk0003 at ntu.edu.sg; anandmasc at gmail.com
URL: sites.google.com/site/oatresearch/anand-jayant-kulkarni
Ph: 91 20 6193 6790

ResearcherID: www.researcherid.com/rid/O-3585-2016
ORCID ID: orcid.org/0000-0001-6242-9492
Google Scholar: scholar.google.ca/citations?user=IAvtDokAAAAJ&hl=en

-- 

*
*
**This email is governed by the Disclaimer Terms of  SIU which may be 
viewed at **http://www.siu.edu.in/downloads/email-disclaimer.php 
<http://www.siu.edu.in/downloads/email-disclaimer.php>




-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://listserv.zib.de/pipermail/opt-net/attachments/20201013/6420a1df/attachment.html>