Thanks to the widespread use of mobile devices, analyses that in the past had to be carried out in specifically designated and equipped laboratories and which required long processing times, may now take place outdoor and in real time. In the marine science, for example, the development of a mobile and compact system for the on-site detection of heavy metals contamination in seawater would be helpful for scientists and society in at least two ways: i) reduction of time and costs associated with these experiments; ii) the implementation of a strategy for outdoor analysis, eventually embeddable in a lab-on-hardware system. This paper falls within the context of machine learning (ML) for utility pattern mining applied on interdisciplinary domains: starting from wellplates images, we provide a novel proof-of-concept (PoC) machine learning-based framework to assist scientists in their daily research on seawater samples, proposing a system which automatically recognise wells in a multiwell firstly and then predicts the degree of fluorescence in each of them, thus showing possible presence of heavy metals.

A machine learning-based approach for mercury detection in marine waters / Piccialli, F.; Giampaolo, F.; Di Cola, V. S.; Gatta, F.; Chiaro, D.; Prezioso, E.; Izzo, Stefano; Cuomo, Salvatore. - 2022-:(2022), pp. 527-536. (Intervento presentato al convegno 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022 tenutosi a usa nel 2022) [10.1109/ICDMW58026.2022.00074].

A machine learning-based approach for mercury detection in marine waters

Piccialli F.
;
Giampaolo F.;Gatta F.;Chiaro D.;Prezioso E.;Izzo Stefano;Cuomo Salvatore
2022

Abstract

Thanks to the widespread use of mobile devices, analyses that in the past had to be carried out in specifically designated and equipped laboratories and which required long processing times, may now take place outdoor and in real time. In the marine science, for example, the development of a mobile and compact system for the on-site detection of heavy metals contamination in seawater would be helpful for scientists and society in at least two ways: i) reduction of time and costs associated with these experiments; ii) the implementation of a strategy for outdoor analysis, eventually embeddable in a lab-on-hardware system. This paper falls within the context of machine learning (ML) for utility pattern mining applied on interdisciplinary domains: starting from wellplates images, we provide a novel proof-of-concept (PoC) machine learning-based framework to assist scientists in their daily research on seawater samples, proposing a system which automatically recognise wells in a multiwell firstly and then predicts the degree of fluorescence in each of them, thus showing possible presence of heavy metals.
2022
979-8-3503-4609-1
A machine learning-based approach for mercury detection in marine waters / Piccialli, F.; Giampaolo, F.; Di Cola, V. S.; Gatta, F.; Chiaro, D.; Prezioso, E.; Izzo, Stefano; Cuomo, Salvatore. - 2022-:(2022), pp. 527-536. (Intervento presentato al convegno 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022 tenutosi a usa nel 2022) [10.1109/ICDMW58026.2022.00074].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/914746
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