Antonio Coronato

Full Professor

Università Telematica Giustino Fortunato

Italy

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Antonio Coronato is a Full Professor at Università Telematica Giustino Fortunato in Italy. His research areas include intelligent medication engagement systems, skin cancer and COVID-19 diagnostic accuracy, and AI applications in rehabilitation and risk prevention. Additionally, he has explored blockchain applications in sustainable smart cities and has contributed to various editorial works in these domains.

Articles (19)

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2024

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2020

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2020

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2020

Collaborators (4)

muddasar naeem

Researcher and Adjunct Professor

ITALY

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Fabrizio Stasolla

Professore Associato di Psicologia dello Sviluppo

Giustino Fortunato University

ITALY

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Mario Ciampi

Adjunct Professor of Computer Science

Università degli Studi di Napoli Federico II

ITALY

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Zaib Ullah

Università Telematica Giustino Fortunato

ITALY

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Antonio Coronato

Research Interests

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Articles (19)

Defining a Metric-Driven Approach for Learning Hazardous Situations

Impact of AI-Powered Solutions in Rehabilitation Process: Recent Improvements and Future Trends

Enhancing Diagnostic Accuracy for Skin Cancer and COVID-19 Detection: A Comparative Study Using a Stacked Ensemble Method

A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks

Advancing Patient Care with an Intelligent and Personalized Medication Engagement System

Editorial

An AI-empowered infrastructure for risk prevention during medical examination

Blockchain Applications in Sustainable Smart Cities

Editorial

Editorial

An intelligent environment for preventing medication errors in home treatment

Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning

An AI-Empowered Home-Infrastructure to Minimize Medication Errors

Learning and Assessing Optimal Dynamic Treatment Regimes Through Cooperative Imitation Learning

Definition of a novel federated learning approach to reduce communication costs

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

A reinforcement learning and deep learning based intelligent system for the support of impaired patients in home treatment

An hybrid ECG-based deep network for the early identification of high-risk to major cardiovascular events for hypertension patients

Collaborators (4)

muddasar naeem

Fabrizio Stasolla

Mario Ciampi

Zaib Ullah

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