CFP: Special Issue on Artificial Intelligence for Mobile Health Data Analysis and Processing (IF 0.849)

[Apologies if you receive multiple copies of this CFP]

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Special Issue on Artificial Intelligence for Mobile Health Data Analysis 
and Processing

Mobile Information Systems

Impact Factor: 0.849

https://jcr.incites.thomsonreuters.com/JCRJournalProfileAction.action?pg=JRNLPROF&journalImpactFactor=0.849&year=2016&journalTitle=Mobile%20Information%20Systems&edition=SCIE&journal=MOB%20INF%20SYST 
<https://jcr.incites.thomsonreuters.com/JCRJournalProfileAction.action?pg=JRNLPROF&journalImpactFactor=0.849&year=2016&journalTitle=Mobile%20Information%20Systems&edition=SCIE&journal=MOB%20INF%20SYST> 

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MISSION:
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Nowadays, Internet of Things (IoT) is changing eHealth and especially 
mobile Health (m-Health) systems. Currently, more and more fixed and 
mobile medical devices installed in patients\'92 personal body networks, 
medical devices and in surrounding clinical/home environments collect 
and send a huge amount of heterogeneous health data to healthcare 
information systems for their analysis. In this context machine learning 
and data mining techniques are becoming more and more important in many 
real-life problems. An important number of these techniques are 
dedicated to health data processing and analysis on mobile devices. 
Several mobile applications based on these techniques have emerged as an 
essential technology for improving the quality of medical diagnosis and 
treatments of many illnesses as well as many health disorders.
Existing techniques used for processing health data can be broadly 
classified into two categories: (a) Non-Artificial Intelligence (AI) 
systems & (b) Artificial Intelligence systems. Even though non-AI 
techniques are less complex in nature, most of the systems suffer from 
the drawbacks of inaccuracy and lack of convergence. Hence, these 
systems are generally replaced by AI based systems which are much 
superior to the conventional systems. AI techniques are mostly hybrid in 
nature and include Artificial Neural Networks (ANN), Fuzzy theory, 
Evolutionary algorithms, etc. Though most of the techniques are 
theoretically sound, the potential of these techniques is not fully 
explored for practical applications. Many of the computational 
applications still depend on Non-AI systems, which limit their practical 
usage.

This special issue especially focuses on the feasibility of machine 
learning and data mining techniques on practical mobile health 
applications. These practical mobile applications include for instance 
biomedical, medical images processing, health management, etc. This 
special issue serves for discovering the untold advantages of data 
science techniques for practical mobile health applications, and also 
brings out solutions for many real-life problems through advanced 
theoretical and experimental approaches.


TOPICS:
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The topics for this special issue include but not limited to:

- Novel architectures for m-Health data analysis and processing
- Fuzzy approaches for mobile applications dedicate to health management
- Evolutionary algorithms for optimization methodologies for mHealth 
applications
- Medical-informatics applications using intelligence methodologies on 
mobile devices
- Applications of AI techniques in signal & image processing on mobile 
devices
- Mobile bio-medical applications involving ANN, fuzzy theory, etc
- Data Mining for health data processing and analysis on mobile devices
- Machine Learning and Deep learning for health-related mobile applications


PAPER SUBMISSION:
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Authors are invited to submit their papers written in English in pdf.
Please, find information on how to prepare it 
at:https://www.hindawi.com/journals/misy/guidelines/ 
<https://www.hindawi.com/journals/misy/guidelines/>.


IMPORTANT DATES:
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Submission deadline: June 29, 2018
Publication date: November, 2018


FOR ANY OTHER 
INFORMATIONhttps://www.hindawi.com/journals/misy/si/532718/cfp/ 
<https://www.hindawi.com/journals/misy/si/532718/cfp/>