Giovanna Maria Dimitri

Studenti Visitatori e Alumni

giovanna.dimitri@unisi.it

Biografia

Giovanna Maria Dimitri è ricercatrice in AI presso l’Universitá di Siena e Guest Lecturer presso l’Universitá di Cambridge..

Ha conseguito il dottorato di ricerca in Computer Science presso l’Università di Cambridge e le lauree in Ingegneria Informatica e dell’Automazione presso l’Università di Siena.

La sua esperienza comprende metodologie di rete multistrato per l’analisi e la modellazione dei dati cerebrali, la previsione degli effetti collaterali dei farmaci e la previsione della funzione delle proteine.

Profili

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Ultime 5 pubblicazioni (Scopus)

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  • WeAIR: Wearable Swarm Sensors for Air Quality Monitoring to Foster Citizens’ Awareness of Climate Change; Computer Standards and Interfaces; August 2025; DOI: 10.1016/j.csi.2025.104004
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  • Analyzing the Impact of Organic Food Consumption on Citizens Health Using Unsupervised Machine Learning; Mathematics; April 2025; DOI: 10.3390/math13081272
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  • Mechanotransduction and inflammation: An updated comprehensive representation; Mechanobiology in Medicine; March 2025; DOI: 10.1016/j.mbm.2024.100112
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  • Enhancing Synthetic Generated-Images Detection through Post-Hoc Calibration; Proceedings 2025 IEEE Cvf Winter Conference on Applications of Computer Vision Workshops Wacvw 2025; 2025; DOI: 10.1109/WACVW65960.2025.00087
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  • Semiotic-Based Construction of a Large Emotional Image Dataset with Neutral Samples; Proceedings 2025 IEEE Winter Conference on Applications of Computer Vision Wacv 2025; 2025; DOI: 10.1109/WACV61041.2025.00734
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Ultime 5 pubblicazioni (PubMed)

  • Mechanotransduction and inflammation: An updated comprehensive representation

    Mechanotransduction is the process that enables the conversion of mechanical cues into biochemical signaling. While all our cells are well known to be sensitive to such stimuli, the details of the systemic interaction between mechanical input and inflammation are not well integrated. Often, indeed, they are considered and studied in relatively compartmentalized areas, and we therefore argue here that to understand the relationship of mechanical stimuli with inflammation - with a high...

  • Precision agriculture for wine production: A machine learning approach to link weather conditions and wine quality

    The agricultural sector, in particular viticulture, is highly susceptible to variations in the environment, crop conditions, and operational factors. Effectively managing these variables in the field necessitates observation, measurement, and responsive actions. Leveraging new technologies within the realm of precision agriculture, vineyards can enhance their long-term efficiency, productivity, and profitability. In our work we propose a novel analysis of the impact of pedoclimatic factors on...

  • Echo state networks for the recognition of type 1 Brugada syndrome from conventional 12-LEAD ECG

    Artificial Intelligence (AI) applications and Machine Learning (ML) methods have gained much attention in recent years for their ability to automatically detect patterns in data without being explicitly taught rules. Specific features characterise the ECGs of patients with Brugada Syndrome (BrS); however, there is still ambiguity regarding the correct diagnosis of BrS and its differentiation from other pathologies. This work presents an application of Echo State Networks (ESN) in the Recurrent...

  • A machine learning approach to assess Sustainable Development Goals food performances: The Italian case

    In this study, we introduce an innovative application of clustering algorithms to assess and appraise Italy's alignment with respect to the Sustainable Development Goals (SDGs), focusing on those related to climate change and the agrifood market. Specifically, we examined SDG 02: Zero Hunger, SDG 12: Responsible Consumption and Production, and SDG 13: Climate Change, to evaluate Italy's performance in one of its most critical economic sectors. Beyond performance analysis, we administered a...

  • A multi-modal machine learning approach to detect extreme rainfall events in Sicily

    In 2021 almost 300 mm of rain, nearly half of the average annual rainfall, fell near Catania (Sicily Island, Italy). Such events took place in just a few hours, with dramatic consequences on the environmental, social, economic, and health systems of the region. These phenomena are now very common in various countries all around the world: this is the reason why, detecting local extreme rainfall events is a crucial prerequisite for planning actions, able to reverse possibly intensified dramatic...