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Subject:
From:
Marie-Helene PREVOTEAU <[log in to unmask]>
Reply To:
Marie-Helene PREVOTEAU <[log in to unmask]>
Date:
Tue, 5 May 2020 13:20:58 +0200
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Bonjour,

 

J'attends du monde de la DOCUMENTATION de RESEPECTER les DONNEES PERSONNELLES. De prendre la précaution de mettre en CCI

 

Cldt

 

 

 

 

> Message du 05/05/20 13:18
> De : "Menaouer Brahami" 
> A : "Grce" , "GRCE" , "Liste EGC" 
, "[log in to unmask]" , "JiscMail" 
, "Liste-egc" 
, "Bull-i3" , "Info-ic" , "Liste-proml" 
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> Copie à : 
> Objet : Special Session on : Knowledge, Machine and Deep Learning for Chronic Diseases Preventionand Management (KMDL'2020)
> 
>

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 : ICECOCS’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 by June 1st , 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 Department
> National 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
>
ORC ID://orcid.org/0000-0003-0045-9797
> 
>























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