<h3>About us</h3>

INSTICC is the Institute for Systems and Technologies of Information, Control and Communication, a scientific, non-profit, association whose main goals are to serve the international scientific community by promoting, developing and disseminating knowledge in the areas of information systems and technologies, control and communications.
To achieve these goals, INSTICC is committed to integrate and support many activities relevant for the international scientific community, including:

<ul>
<li>Promotion of the mobility of renowned researchers, usually involved as keynote speakers at INSTICC events, so that they can share their knowledge with conference delegates;</li>
<li>Providing grants to support the presence of many young researchers from all over the world, especially from regions facing economic difficulties, who wish to attend INSTICC conferences;</li>
<li>Publication of proceedings, books and journals - some of them in cooperation with distinguished international publishers - widely indexed and made available at appropriate digital libraries;</li>
<li>Sponsorship of research projects, proposed by universities and R&D institutes, related to INSTICC main interest areas;</li>
<li>Collaboration with international associations, who may technically co-sponsor INSTICC events, as well as with companies involved in R&D or supporting of the international academic community.</li>
</ul>

Institute for Systems and Technologies of Information, Control and Communication

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 AREAS 
1 . Models and Algorithms 
2 . Machine Learning 
3 . Big Data Analytics 
4 . Computer Vision Applications 
5 . Natural Language Understanding

DeLTA 2021

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.

DeLTA 2021

4

lectures

4

authors

1

subjects

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