An Innovative Methodology for Building Better Deep Learning Tools for Real Word Applications

Welcome to NeCOL

This website presents the main outcomes of NeCOL project under the Marie Sklodowska-Curie grant No 799078.

NeCOL has the relevant scientific goal of the development of efficient deep learning (DL) based on the use of co-evolutionary algorithms (CEAs) for real world applications. Hence the name of the project, Neural CO-evoltionary Learning or NeCOL. We want to tackle applications of importance in present societies, such as cybersecurity and Smart Cities (SC).

This project is funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Individual Fellowship grant agreement No 799078. NeCOL involves Jamal Toutouh, PhD. as the Marie Sklodowska-Curie fellow and main researcher and two research groups: NEO (Networking and Emerging Optimization) at the University of Malaga (UMA) in Europe (Spain) and ALFA (Anyscale Learning For All) at the Massachusetts Institute of Technology (MIT) in USA.

Jamal Toutouh

This Marie Sklodowska-Curie fellow works on the use of Artificial Intelligence to improve the life of EU citizens, without leaving the vulnerable population behind.

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ALFA Group

ALFA is distinguished by its work on scalable machine learning (ML) and data science frameworks for large-scale knowledge mining, prediction, analytics, and optimization.

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NEO Group

NEO is a leading group in the domain of advanced optimization, principally using evolutionary algorithms (EAs) and swarm intelligence and their application in the real world.

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Welcome to NeCOL

Smart City tools and Data Science have been applied to analyze the effects of Madrid Central, an environmental measure enforced in Spain.

What does science really say about Madrid Central?

During ICSC-CITIES 2019 we will present our analysis of the environmental impact (air and noise pollution) of this measure. You can consult here the complete outcomes of this work. Read More ›

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