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Subject:
From:
Menaouer Brahami <[log in to unmask]>
Reply To:
Menaouer Brahami <[log in to unmask]>
Date:
Sun, 3 May 2020 19:26:15 +0200
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Dear Colleagues,

Please forward this CfP to colleagues who might be interested in
contributing to this conference and special session.

Regards,

-------------------------------------------------------------------------------------------------------------------

*CALL FOR PAPERS*

*SPECIAL SESSION ON:*
AI techniques for Healthcare applications and diagnosis

Track II:* “Knowledge, Machine and Deep Learning for Chronic Diseases
Prevention and Management (KMDL'2020)”*

 *Under the frame of : **I**C**E**C**O**C**S’**20 - *
https://www.uit.ac.ma/icecocs2020/

*December 2nd –3rd, 2020   ▪   Kenitra, Morocco*

*Session Co-Chairs :*

 Prof. Menaouer Brahami, National Polytechnic School of Oran - Maurice
Audin, Algeria

Dr. Mohammed Sabri, National Polytechnic School of Oran - Maurice Audin,
Algeria


*Session description*

Chronic diseases constitute a major cause of mortality, and the World
Health Organization (WHO) attributes 38 million deaths a year to
non-communicable diseases. Likewise, chronic diseases such as heart
disease, cancer, asthma, diabetes and some viral diseases are the leading
causes of death and disability in the world.

More recently, knowledge has been increasingly seen as a key competitive
resource in organizations and this has influenced selection and recruitment
practices in many organizations. Today, it is an essential necessity for
healthcare organizations to manage both tacit and explicit knowledge
effectively in order to provide the best possible healthcare by solving
problems and making the most ideal and flawless. In addition, knowledge
engineering is an intelligent process by which the gathered raw health data
is transformed into knowledge in order to be used for “integrate and
interpret knowledge for individualized healthcare”. Knowledge engineers use
artificial intelligence (AI) concepts and techniques in developing
knowledge-based systems.

Likewise, machine learning is a field of artificial intelligence that uses
statistical techniques to give computer systems the ability to "learn from
health data”, without being explicitly programmed. Meanwhile, deep learning
is one of machine learning methods based on artificial neural networks.
During the past few years, with the advances in deep learning, many new
computation models have been proposed and significantly applied in natural
language processing, neuroscience, biomedical, biometrics, information
security, etc. In another side, Deep Learning in medicine is one of the
most rapidly and new developing fields of science. Currently, almost every
medical device intended for imaging has a more or less extended image and
signal analysis and processing module which can use deep learning. It
provides quantitative data necessary to make a diagnosis with predicting
diagnosis.

This special issue on Knowledge, Machine and Deep Learning invites
researchers and practitioners to present novel contributions addressing
theoretical and practical aspects of knowledge management, machine learning
and deep learning. The special issue will feature a collection of high
quality theoretical articles for improving the knowledge management and
learning process. State-of-the-art applications based on deep reinforcement
learning and knowledge graph are also very welcome.

The topics of interest include, but are not limited to:

   - Information and knowledge management tools for chronic disease
   - Knowledge models for chronic disease
   - Data science and knowledge engineering for chronic disease
   - Machine and deep learning techniques for chronic disease
   - Supervised, Unsupervised and Reinforcement learning for chronic disease
   - Data mining and its applications in chronic diseases
   - Natural language processing and text mining for chronic diseases
   prevention
   - Social media for chronic disease and participatory activities


   - Service-oriented computing for chronic diseases management
   - Geo-Information technologies for chronic diseases diagnosis
   - Fuzzy Logic and its application in chronic disease prevention
   - Big Data and large scale methods for chronic disease (diagnostic or
   therapeutic)
   - Internet of Things (IoT) and cloud computing for chronic disease
   diagnosis
   - Applications, algorithms, tools directly related to machine deep
   learning
   - Datasets for machine learning/deep learning experiments in chronic
   diseases



*SUBMISSION*

Papers must be submitted electronically for peer review through through EDAS
<http://controls.papercept.net/conferences/scripts/start.pl> by *June 1*
*st **, 2020**:*



IMPORTANT: All papers must be written in English and should describe
original work. The length of the paper is limited to a maximum of 6 pages
(in the standard IEEE conference double column format).



*DEADLINES*

June 1st, 2020: deadline for paper submission

July 31st, 2020: notification of acceptance/reject

August 15th, 2020: deadline for final paper

September 10th 2020: Early Registration

October 17th 2020: Late Registration



*-------------------------------------*
*Dr. BRAHAMI Menaouer*
*Associate Professor - HDR*

*Head of Systems Engineering DepartmentNational Polytechnic School of Oran
- Maurice Audin*
BP:1523 El M'naouer 31000, Oran, Algeria
Tel mobile: +213553420947/ Tel of Department: +21341518787
E-mail: [log in to unmask] , [log in to unmask]
Website (Publications and citations):
ResearchGate: https://www.researchgate.net/profile/Brahami_Menaouer3
Google Scholar: https://scholar.google.com/citations?hl=en&user=Ufn5IcwAAAAJ
Academia: https://enp-oran.academia.edu/BRAHAMIMenaouer
Web of Science Researcher ID: AAE-4845-2019
<https://publons.com/researcher/3218006/brahami-menaouer/>
ORC ID[image: ORCID]://orcid.org/0000-0003-0045-9797
<https://orcid.org/0000-0003-0045-9797>

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