[Amdnl] CFP: SI - Advances in Architectures, Big Data, and Machine Learning Techniques for Complex IoT - Deadline extended to 19 October, 2018 - Impact Factor 1.829

[David Gil]

Dear Colleagues,

Complexity, Open Access Journal - Hindawi is currently running a Special
Issue entitled "Advances in Architectures, Big Data, and Machine Learning
Techniques for Complex Internet of Things Systems" Dr. David Gil Assoc.
Prof.
We would like to invite you to prepare a research article or a
comprehensive review to be published in this special issue.

AIM AND SCOPE

The Internet of Things (IoT) has made it possible for devices around
the world to acquire information and store it, to be able to use it at
a later stage. However, there are important limitations for a
large-scale achievement in this revolution. Furthermore, IoT allows
developing big data architectures based on services. IoT technologies
are available and every day there are new ones that arrive. Some of
them are sensors, RFID, GPS, and many other types of smart devices.

However, this potential opportunity is often not exploited. There are
some reasons like the excessively big interval between the data
collection and the capability to process and analyze it. Another
reason is the need of new models of design for suitable big data
architecture. To effectively synthesize big data and communicate among
devices using IoT, machine learning techniques are employed. Machine
learning extracts meaning from big data using various techniques which
include regression analysis, clustering, Bayesian methods, decision
trees and random forests, support vector machines, reinforcement
learning, ensemble learning, and deep learning. Currently, the
quantitative data description of complex huge systems is no longer
exclusively experimental sample data but the full overview data for
the entire state. In this scenario, data analysis should endorse
complex scientific intelligent analysis method for modeling and
simulating. It has also to utilize and constantly optimize big data
for machine learning and analyze and study the self-organizing and
evolving rules of complex systems.

The purpose of this special issue is to publish high-quality research
papers as well as review articles addressing recent advances in
modeling, formal methods, and complexity handling of architectures,
big data, and machine learning techniques for complex Internet of
Things systems. Theoretical studies and state-of-the-art practical
applications are welcome for submission.


THEMES

This special issue aims to report state-of-the-art approaches and
recent advances in modeling, formal methods, and complexity handling
of architectures, big data, and machine learning techniques for
complex Internet of Things systems. Theoretical studies and
state-of-the-art practical applications are welcome for submission.

Potential topics include but are not limited to the following:
- Complexity modeling and formalization of architectures for big data
- Regression, classification, and clustering for complex big data analysis
- Deep learning and artificial neural network for optimizing big data
- Data integration in big data environments
- Genetic algorithm based data integration and management for Hadoop ecosystems
- Data virtualization, ELT, or ETL for complex data integration
- Cellular automata model of data mining over the cloud
- Data mining with big data: new machine learning algorithms
- MapReduce algorithms for complex IoT
- Big data for open data and privacy protection for complex IoT
- Complex modeling and management in IoT domains
- Chaotic approach for stream mining in IoT
- Cloud computing based evolutionary game for Internet of Things systems
- Genetic algorithms analysis for Mobile Cloud Computing systems
- Complexity modeling and decision-making methods for complex IoT applications
- Internet of Things and complexity handling
- Chaos theory, information theory, genetic and biologically inspired
algorithms, cellular automata, neural networks, intelligent search
algorithms, and evolutionary game theory in complex manufacturing
applications
- Tools and techniques for solving complex machine learning problems
- Actual versus perceived complexity in knowledge representation of big data
- Handling complexities with big data as the new technology
- Development of science of complexity


SUBMISSION

Authors can submit their manuscripts through the Manuscript Tracking
System at https://mts.hindawi.com/submit/journals/complexity/aabms/

IMPORTANT DATES

Submission Deadline	Friday, 19 October 2018
Publication Date	March 2019

Papers are published upon acceptance, regardless of the Special Issue
publication date.


Lead Guest Editor
David Gil, University of Alicante, Alicante, Spain

Guest Editors
Magnus Johnsson, Magnus Johnsson AI Research AB, Lund, Sweden
Higinio Mora, University of Alicante, Alicante, Spain
Julian Szymanski, Gdansk University of Technology, Gdansk, Poland
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.cse.msu.edu/pipermail/amdnl/attachments/20180905/10dc7453/attachment.html>