[AISWorld] Sustainability. 2nd CFP to contribute to Special Issue "Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems"

[Jesús Peral Cortés]

[Apologies if you receive multiple copies of this announcement. Please 
pass it on to your colleagues and students who might be interested in 
contributing]

================================================================================================================================ 

2nd Call for Papers
Special Issue "Advances in Architectures, Big Data, and Machine Learning 
Techniques for Complex Internet of Things Systems" of Sustainability 
(ISSN 2071-1050).
https://www.mdpi.com/journal/sustainability/special_issues/ciot_sus
================================================================================================================================ 


Sustainability (ISSN 2071-1050), IF 2.592, is running a Special Issue on 
the topic of "Advances in Architectures, Big Data, and Machine Learning 
Techniques for Complex Internet of Things Systems".  Dr. Jesús Peral,  
Dr. Hadi Moradi, Dr. Javi Medina Quero, Dr. Jie Lian and Dr. David Gil 
are serving as the guest editors.

You are cordially invited to submit a contribution to this Special 
Issue, of either original research or a review. The submission deadline 
is 18 December 2020.

Massive volumes of data are already present and still rapidly growing as 
a result of diverse data sources, including all type of smart devices 
and sensors (Internet of Things) and social networks. This fact has led 
to an increasing interest in incorporating these huge amounts of 
external and unstructured data, normally referred to as "Big Data", into 
traditional applications. This requirement has made that traditional 
database systems and processing need to evolve and accommodate them.

However, there are important limitations for a large-scale achievement 
in this revolution. Furthermore, IoT allows developing big data 
architectures based on services. Of course, in IoT the information 
varies broadly in structure, complexity and type. This leads to a need 
for integration, one of the most complex as well as challenging issues 
of Big Data, which can be defined as a set of complex techniques used to 
combine data from disparate sources into meaningful and valuable 
information. To effectively synthesize big data and communicate among 
devices using IoT, machine learning techniques are employed. Machine 
learning extracts meaning from big data using different kind of 
techniques (clustering, Bayesian methods, decision trees, SVM, deep 
learning, etc.).

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

Keywords:
- Complex IoT systems
- Big Data architectures
- Data Mining with Big Data
- Machine learning techniques for Big Data analysis
- Data visualization and integration
- Deep learning
- Cognitive systems

Prof. Dr. Jesús Peral
Prof. Dr. Hadi Moradi
Prof. Dr. Javi Medina Quero
Prof. Dr. Jie Lian
Prof. Dr. David Gil
Guest Editors

-- 

______________________________________________________________________

Jesús Peral Cortés

LUCENTIA Research Group
http://www.lucentia.es

Departamento de Lenguajes        Telf.: (96)5903400 ext. 3385
y Sistemas Informáticos          Fax:   (96)5909326
UNIVERSIDAD DE ALICANTE          E-mail: jperal at dlsi.ua.es
______________________________________________________________________
URL: http://www.dlsi.ua.es/~jperal
______________________________________________________________________