Home      Log In      Contacts      FAQs      INSTICC Portal
 
 

Upcoming Submission Deadlines

Regular Paper Submission: February 16, 2021
Doctoral Consortium Paper Submission: May 12, 2021
Abstracts Track Submission: May 12, 2021

(See Important Dates for more information)

Deep Learning and Big Data Analytics are two major topics of data science, nowadays. Big Data has become important in practice, as many organizations have been collecting massive amounts of data that can contain useful information for business analysis and decisions, impacting existing and future technology. A key benefit of Deep Learning is the ability to process these data and extract high-level complex abstractions as data representations, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled.

Machine-learning and artificial intelligence are pervasive in most real-world applications scenarios such as computer vision, information retrieval and summarization from structured and unstructured multimodal data sources, natural language understanding and translation, and many other application domains. Deep learning approaches, leveraging on big data, are outperforming state-of-the-art more “classical” supervised and unsupervised approaches, directly learning relevant features and data representations without requiring explicit domain knowledge or human feature engineering. These approaches are currently highly important in IoT applications.






Conference Chair

Kurosh MadaniUniversity of Paris-EST Créteil (UPEC), France

PROGRAM CHAIR

Ana FredInstituto de Telecomunicações and University of Lisbon, Portugal






 
 SCITEPRESS Digital Library
All papers presented at the conference venue
will be available at the SCITEPRESS Digital Library
(consult SCITEPRESS Ethics of Publication)
CCIS Series
It is planned to publish a short list of revised and
extended versions of presented papers with Springer
in a CCIS Series book (final approval pending)
Proceedings will be submitted for indexation by:


footer