[agents] Call for Paper: AI and Machine Learning for automatic monitoring and diagnostic systems

[Domenico Maisto]


Second Call for Paper:
AI AND MACHINE LEARNING FOR AUTOMATIC MONITORING AND DIAGNOSTIC SYSTEMS
special session of
IMEKO TC4 International Symposium
"Research on Electrical and Electronic Measurement for the Economic Upturn"
15 - 17 September, 2014   -   Benevento, Italy


  AIMS AND SCOPE
Complexity in industrial and experimental installations is becoming more and more significant. Consequently, the probability of fault appearances enlarges, while tractability of monitoring, diagnosing, and controlling the system as a whole, as well as of each single component, reduces. Artificial Intelligence and Machine Learning offer models, computational methods, and architectures able to effectively tackle these problems.
This special session invites articles that propose AI and Machine Learning techniques applied to monitoring and diagnostic systems in a wide variety of fields.

Topics of interest might be application domains such as (but not limited to):
- Monitoring and Diagnostic systems in industry and agriculture;
- Experimental scientific facilities for large area and big data systems;
- Energy aware systems and technologies;
- Environmental measurements and monitoring;
- Intelligent Transport Systems and Traffic Management;
- Bioinformatics and Bio-engineering;
- Healthcare;
- Smart grids;
- Smart buildings;
- Sensor-based Networks.

AI and Machine Learning methods might include a wide variety of approaches such as (but not restricted to):
- Reinforcement learning;
- Markov decision processes;
- Active learning and optimization;
- Transfer learning;
- Bayesian methods;
- Ensembles and Statistical learning;
- Fuzzy Systems;
- High-performance and Distributing computing;
- Search and Constraint Satisfaction;
- Meta-heuristic Search and Optimization;
- Evolutionary algorithms and Swarm Intelligence;
- Multi-agent Systems;
- Decision Support Systems.

Both case studies of technology transfer and working progress papers on challenging areas are especially welcome.

  PAPER SUBMISSION
Prospective authors must electronically submit an extended abstract (4  pages, including figures) by June 10, 2014  (http://www.imeko-tc4-2014.org). All papers will receive multiple peer  reviews; authors will receive timely notification of paper acceptance. If accepted, final papers must be no more than 6 pages  and will be submitted electronically.

Papers must be presented at the conference -- either orally or as a poster -- by an author, will appear in the final conference proceedings and will be indexed in the Scopus citation index.

Formats and complete submission instructions are available at  http://www.imeko-tc4-2014.org.
Student papers must follow the same submission procedure and deadlines.

  IMPORTANT NOTICES
An extended abstract is required by June 10th for the acceptance or rejection decision, while the final paper should be sent by July 31.
There will be a student paper competition, a woman authored and presented paper competition and a poster presentation competition.
The most relevant papers of any session will be invited to be extended and peer reviewed for the publication on the Measurement journal, edited by Elsevier, or the Acta IMEKO journal, edited by IMEKO. 
The Measurement journal has an Impact Factor = 1.130 and a 5-year Impact Factor = 1.159
The Acta IMEKO journal, is younger but still a Scopus indexed journal that is being to receive an IF.
There is no limit to the number of papers that can be invited to the peer review phase for each journal.
None of the journals has an overlenght fee.

Begin developing your topic and paper now to present at this conference's special session.

We apologize if you receive multiple copies of this message.
Please forward to any colleague who might be interested.


  CONTACTS
Special Session Co-Chairs
Dr. Domenico Maisto
domenico.maisto at na.icar.cnr.it
Dr. Carlo Manna
carlo.manna at insight-centre.org

Web site:
http://www.imeko-tc4-2014.org/index.php/ai-and-machine-learning-for-automatic-monitoring-and-diagnostic-systems