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    Mathematical mOdelling and Data AnaLysis

    Department of Mathematics and Applications "R. Caccioppoli"

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    Mathematical mOdelling and Data AnaLysis

    Deep Learning methodologies for Medicine and Healthcare

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    Physics-Informed Neural Networks

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    Mathematical mOdelling and Data AnaLysis

    Machine Learning in Biochemistry

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    Mathematical mOdelling and Data AnaLysis

    Deep Learning for Market Neutral Portfolio

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    Mathematical mOdelling and Data AnaLysis

    Artificial Intelligence for fighting COVID-19

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    Mathematical mOdelling and Data AnaLysis

    IoT and Deep Learning in Cultural Heritage

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    Mathematical mOdelling and Data AnaLysis

    Deep Learning in Geoscience

The M.O.D.A.L. research group at the University Naples Federico II conducts research and teaches a wide range of AI related topics, often cross-disciplinary. We collaborate internationally and locally with academics, businesses and public stakeholders, working at the frontiers of knowledge on solving real-world problems. The M.O.D.A.L. research group is dynamic and community-oriented, providing many research collaboration and innovation opportunities. M.O.D.A.L. is committed to advancing knowledge and fostering learning in an atmosphere of discovery and creativity. People-oriented values such as transparency, trust, creativity and autonomy are central.

Research Activities

M.O.D.A.L. research interests fall within the following main areas:

Machine and Deep Learning

Machine learning means computers learning from data using algorithms to perform a task without being explicitly programmed. Deep learning uses a complex structure of algorithms modeled on the human brain. This enables the processing of unstructured data such as documents, images and text.

Scientific Machine Learning

Nowadays, combining physics law and domain knowledge into Machine Learning models can be considered a new frontier; the goal is to provide some “informative priors” such as theoretical constraints on top of observational ones. Therefore, Physics-Informed Machine Learning aims to introduce a novel paradigm to improve the performance of the learning algorithms.

Data Science

Data science is a field of applied mathematics and statistics that provides useful information based on large amounts of complex data or big data.
Data science, or data-driven science, combines aspects of different fields with the aid of computation to interpret reams of data for decision-making purposes.

Real-World Applications

Healthcare, Precision Medicine, Industry 4.0, Smart City, Smart Mobility, Agritech, Biochemistry, Cultural Heritage, Energy Efficiency, Geosciences, Seismology.

Last News

Last News @ M.O.D.A.L.

Highly Cited Paper

AWARD - We are proud to announce that our article titled A survey on deep learning in medicine: Why, how and when? has been awarded as Highly

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The BIOCHIP project

NEW PUBLICATION - First research results of the BIOCHIP (Intelligent BIOsensors based on CHImeric Proteins) project have been published by M.O.D

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Predictive Medicine and Deep Learning

NEW PUBLICATION - Predictive Medicine for Salivary gland tumours identification through Deep Learning is the title of the article published by M

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AI and Smart Mobility

NEW PUBLICATION -  Predictive Analytics for Smart Parking: A Deep Learning Approach in Forecasting of IoT Data is the title of the article pub

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AI and COVID-19

NEW PUBLICATION - The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic is the title of the article published by M.O.D.A.L. on I

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AI and Healthcare

NEW PUBLICATION - Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion is the title o

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Last Projects

C.E.T.R.A. - Cultural Equipment with Transmedial Recommendation Analytics

Il progetto di ricerca porta avanti una combinazione originale di IoT, Machine Learning, Data Analytics secondo il paradigma della transmedialità.

4I: mixed reality, machine learning, gamification and educational for Industry

Il progetto 4I, diretto al miglioramento dei processi esistenti, intende proporre alle industrie un nuovo modello di condivisione della conoscenza, di supervisione e manutenzione dei processi e delle attrezzature e di formazione del personale e QA, Quality Assurance, sfruttando le nuove tecnologie di mixed reality, il machine learning, l’educational e la gamification.

BIOCHIP - Intelligent BIOsensors based on CHImeric Proteins

Un progetto di ricerca multi-disciplinare che coinvolge 3 dipartimenti ed altrettanti settori scientifico-disciplinari, e che abbraccia tematiche innovative (biosensoristica ed intelligenza artificiale), e propone lo sviluppo di un biosensore per la rilevazione di inquinamento da mercurio nelle acque marine basato su proteine di fusione adesive ed auto-assemblanti in grado di legare il metallo.