CfP for Special  Issue on “Artificial Intelligence for Mobile Health Data Analysis and  Processing”, Journal of Mobile Information Systems, Hindawi

It is a pleasure for me to invite you to participate to the Special
Issue on “Artificial Intelligence for Mobile Health Data Analysis and
Processing”, Journal of Mobile Information Systems, Hindawi (Impact
Factor: 0.958)

You are welcome to distribute this call or to send us the names, e-mail
addresses, and affiliations of colleagues that you would recommend
enriching the content of the theme issue.

We would appreciate a brief feedback about your interest at your
earliest convenience, and of course your contribution in due time.

We are looking forward to receiving your contribution, and we remain

Yours sincerely,

Giovanna Sannino,

Lead Guest Editor of the Special Issue on “Artificial Intelligence for
Mobile Health Data Analysis and Processing”, JMIS, Hindawi.

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

Mobile Information Systems

Impact Factor: 0.958


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’ 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

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.


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
– Medical-informatics applications using intelligence methodologies on
mobile devices
– Applications of AI techniques in signal & image processing on mobile
– 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


Authors are invited to submit their papers written in English in pdf.
Please, find information on how to prepare it at:


Submission deadline: August 03, 2018 (extended and firm)
Publication date: December, 2018