Special Issue "Machine Learning, Stochastic Modelling and Applied Statistics for EMF Exposure Assessment"
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Health".
Deadline for manuscript submissions: 1 September 2020.
Interests: electromagnetic fields (EMF) exposure assessment; Machine Learning; numerical dosimetry; application and modeling of EMF in medical devices
Interests: electromagnetic fields (EMF) exposure assessment; computational dosimetry; stochastic dosimetry; uncertainty in EMF assessment
Interests: electromagnetic fields (EMF); exposure assessment; numerical dosimetry; monitoring; safety guidelines
Interests: EMF exposure; experimental and numerical dosimetry; telecommunication; machine learning; uncertainty quantification; stochastic dosimetry
In addition to occupational environments or biomedical applications, exposure to electromagnetic fields (EMF) is also very common in everyday life as a result of the widespread and pervasive use of a variety of EMF sources, ranging from electric lines, electric appliances, wireless devices, mobile communication, etc. It is expected that EMF exposure will be increasing even more in the next years due to the growth of applications based on wireless communication for the exchange of information, such as Internet of Thing (IoT) devices and vehicular communication (vehicle-to-vehicle V2V or vehicle-to-infrastructure V2I).
The assessment of EMF exposure is of crucial importance to go deeper in understanding possible negative health effects, especially by studying exposure in real everyday conditions and in the general population. To achieve this, huge and expensive (in term of time and resources) exposure measurement campaigns to provide the data for the subsequent analyses must be performed or heavy numerical solutions to model exposure must be developed.
This Special Issue is open to scientific studies addressing the application of applied statistics, machine learning, and stochastic dosimetry for EMF exposure assessment. Machine Learning, stochastic dosimetry, and applied statistics are emerging techniques that complement classical exposure analyses, offering the advantage of being able to predict and model the exposure in more generalized environmental scenarios and not only for a particular case under study. This Special Issue is dedicated to works in any frequency area, from static fields up to exposures in the THz region, dealing with exposure assessment, dosimetry, hazard identification, and characterization, risk assessment.
Prof. Gabriella Tognola
Dr. Emma Chiaramello
Prof. Masao Taki
Prof. Joe Wiart
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- EMF exposure assessment
- Machine learning
- Stochastic dosimetry
- Applied statistics
- Environmental health