Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
2023
Journal article
Open Access
Efficient lung ultrasound classification
Bruno A., Ignesti G., Salvetti O., Moroni D., Martinelli M.
A machine learning method for classifying Lung UltraSound is here proposed to provide a point of care tool for supporting a safe, fast and accurate diagnosis, that can also be useful during a pandemic like as SARS-CoV-2. Given the advantages (e.g. safety, rapidity, portability, cost-effectiveness) provided by the ultrasound technology over other methods (e.g. X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest LUS public dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art. The complexity of this solution keeps the number of parameters in the same order as an EfficientNet-b0 by adopting specific design choices that are adaptive ensembling with a combination layer, ensembling performed on the deep features, minimal ensemble only two weak models. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where the focus is on an inaccurate weak model versus an accurate model.Source: Bioengineering (Basel) 10 (2023). doi:10.3390/bioengineering10050555
DOI: 10.3390/bioengineering10050555
Metrics:
Bruno A., Ignesti G., Salvetti O., Moroni D., Martinelli M.
A machine learning method for classifying Lung UltraSound is here proposed to provide a point of care tool for supporting a safe, fast and accurate diagnosis, that can also be useful during a pandemic like as SARS-CoV-2. Given the advantages (e.g. safety, rapidity, portability, cost-effectiveness) provided by the ultrasound technology over other methods (e.g. X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest LUS public dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art. The complexity of this solution keeps the number of parameters in the same order as an EfficientNet-b0 by adopting specific design choices that are adaptive ensembling with a combination layer, ensembling performed on the deep features, minimal ensemble only two weak models. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where the focus is on an inaccurate weak model versus an accurate model.Source: Bioengineering (Basel) 10 (2023). doi:10.3390/bioengineering10050555
DOI: 10.3390/bioengineering10050555
Metrics:
See at:
ISTI Repository 
| www.mdpi.com 
| CNR ExploRA
2023
Report
Unknown
Artificial Intelligence in TiAssisto: first results
Ignesti G., Bruno A., Deri C., D'Angelo G., Salvetti O., Moroni D., Pratali L., Martinelli M.
Artificial Intelligence (AI) is integrated into medical applications since its beginning. The advent of deep learning algorithms, powerful computation power and large datasets has made possible the development of numerous new medical applications. A significant part of these applications is focused on the classification or on the segmentation of medical images. In this paper we present an innovative solution for clinical images classification.Source: ISTI Working papers, 2023
Ignesti G., Bruno A., Deri C., D'Angelo G., Salvetti O., Moroni D., Pratali L., Martinelli M.
Artificial Intelligence (AI) is integrated into medical applications since its beginning. The advent of deep learning algorithms, powerful computation power and large datasets has made possible the development of numerous new medical applications. A significant part of these applications is focused on the classification or on the segmentation of medical images. In this paper we present an innovative solution for clinical images classification.Source: ISTI Working papers, 2023
See at: CNR ExploRA
2023
Journal article
Open Access
Explaining ensemble models for lung ultrasound classification
Bruno A., Ignesti G, Martinelli M.
Correct classification is the main aspect in evaluating the quality of an artificial intelligence system, but what happens when you reach top accuracy and no method explains how it works? In our study, we aim at addressing the black-box problem using an ad-hoc built classifier for lung ultrasound images.Source: ERCIM news 134 (2023).
Bruno A., Ignesti G, Martinelli M.
Correct classification is the main aspect in evaluating the quality of an artificial intelligence system, but what happens when you reach top accuracy and no method explains how it works? In our study, we aim at addressing the black-box problem using an ad-hoc built classifier for lung ultrasound images.Source: ERCIM news 134 (2023).
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Deep learning methods for point-of-care ultrasound examination
Ignesti G., Deri C., D'Angelo G., Pratali L., Bruno A., Benassi A., Salvetti O., Moroni D., Martinelli M.
Point-of-care Test (POCT) is the delivery of medical care at or near the patient's bedside. Primarily employed in emergencies, where rapid diagnosis and treatment are critical, POCT is now being used in domestic telehealth solutions, as in the TiAssisto project, thanks to technological advances such as the development of portable and affordable devices, high-speed Internet connections, video conferencing, and Artificial Intelligence (AI). Ultrasound (US) images of internal organs and structures are valuable tools in POCT medicine since this examination is portable, quick, and cost-effective. USs can help diagnose different conditions, including heart problems, abdominal pain, and pneumonia. Deep learning algorithms have proven to be highly effective in image recognition, enabling physicians to make informed decisions on-site. This article presents and investigates a decision support system based on deep learning algorithms. The primary aim of this research is to detect various signs in US images using cutting-edge classification methods. The proposed pipeline initially adopts an optical character recognition (OCR) algorithm: this technique inspects and cleans the US image, ensuring privacy and better classification potential. The collected images are forwarded to a state-of-the-art (SOTA) deep learning network, a fine-tuned EfficientNET-b0, able to detect any signs potentially related to pathology in a rapid way. The network classification is then assessed in the pipeline using a visual explanation method, i.e. Grad-CAM, to evaluate if the proper medical signs were identified, offering a quick and effective second opinion. The involved physician's feedback remarks that this system can detect important signs in pulmonary US imaging, although the dataset is not yet the final one since the TiAssisto project is still ongoing, with a planned conclusion in February 2024. Our ultimate goal is not merely to develop a classification system but to create an effective healthcare support system that can be used beyond primary healthcare facilities.Source: SITIS 2023 - 17th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 436–441, Bangkok, Thailand, 8-10/11/2023
DOI: 10.1109/sitis61268.2023.00078
Metrics:
Ignesti G., Deri C., D'Angelo G., Pratali L., Bruno A., Benassi A., Salvetti O., Moroni D., Martinelli M.
Point-of-care Test (POCT) is the delivery of medical care at or near the patient's bedside. Primarily employed in emergencies, where rapid diagnosis and treatment are critical, POCT is now being used in domestic telehealth solutions, as in the TiAssisto project, thanks to technological advances such as the development of portable and affordable devices, high-speed Internet connections, video conferencing, and Artificial Intelligence (AI). Ultrasound (US) images of internal organs and structures are valuable tools in POCT medicine since this examination is portable, quick, and cost-effective. USs can help diagnose different conditions, including heart problems, abdominal pain, and pneumonia. Deep learning algorithms have proven to be highly effective in image recognition, enabling physicians to make informed decisions on-site. This article presents and investigates a decision support system based on deep learning algorithms. The primary aim of this research is to detect various signs in US images using cutting-edge classification methods. The proposed pipeline initially adopts an optical character recognition (OCR) algorithm: this technique inspects and cleans the US image, ensuring privacy and better classification potential. The collected images are forwarded to a state-of-the-art (SOTA) deep learning network, a fine-tuned EfficientNET-b0, able to detect any signs potentially related to pathology in a rapid way. The network classification is then assessed in the pipeline using a visual explanation method, i.e. Grad-CAM, to evaluate if the proper medical signs were identified, offering a quick and effective second opinion. The involved physician's feedback remarks that this system can detect important signs in pulmonary US imaging, although the dataset is not yet the final one since the TiAssisto project is still ongoing, with a planned conclusion in February 2024. Our ultimate goal is not merely to develop a classification system but to create an effective healthcare support system that can be used beyond primary healthcare facilities.Source: SITIS 2023 - 17th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 436–441, Bangkok, Thailand, 8-10/11/2023
DOI: 10.1109/sitis61268.2023.00078
Metrics:
See at:
ISTI Repository | CNR ExploRA
2023
Contribution to conference
Restricted
Efficient lung ultrasound classification
Ignesti G., Bruno A., Martinelli M., Moroni D.
The SARS-CoV-2 pandemic has taught us that point-of-care signs quickly or in remote settings are essential. Ultrasound imaging is a fast and common diagnostic tool, which made it a popular choice during the pandemic. Our team implemented a deep learning algorithm with remarkable accuracy (100%) to detect signs of COVID-19 and bacterial pneumonia, which can better assist physicians. GradCAM was employed to examine the outcomes and determine whether the network relied on dependable medical indicators for classification.Source: VISMAC 2023 - International Summer School on Machine Vision, Padova, Italy, 04-08/09/2023
Ignesti G., Bruno A., Martinelli M., Moroni D.
The SARS-CoV-2 pandemic has taught us that point-of-care signs quickly or in remote settings are essential. Ultrasound imaging is a fast and common diagnostic tool, which made it a popular choice during the pandemic. Our team implemented a deep learning algorithm with remarkable accuracy (100%) to detect signs of COVID-19 and bacterial pneumonia, which can better assist physicians. GradCAM was employed to examine the outcomes and determine whether the network relied on dependable medical indicators for classification.Source: VISMAC 2023 - International Summer School on Machine Vision, Padova, Italy, 04-08/09/2023
See at:
vismac23.github.io | CNR ExploRA
2023
Contribution to conference
Open Access
Trustworthy AI for signals and image processing: a telemedicine perspective
Ignesti G., Bruno A., Moroni D., Martinelli M.
Artficial Intelligence is showing unprecedented performance in signals & image processing. Classification, segmentation and generative process seem to have unlimited potential. The roots of Artificial Intelligence are deep in scientific history, but in the world of Big Data and Internet 5.0, its use and effects have yet to be entirely tested. The black box problem, security, privacy issues, and public opinion are some of the factors that push towards the development of a new concept: "Trustworthy AI". The use of advanced methods, such as EfficientNet & GradCAM, leads to remarkable accuracy and consistent explanation in the classification of ultrasound. Further studies aim at analyzing results could lead to a more robust application of AI in the generalized field of signal and image processing and will lay the foundation for future work on reliable AI.Source: AI & Society 2023 Summer School, La Maddalena, Italy, 05-09/06/2023
Ignesti G., Bruno A., Moroni D., Martinelli M.
Artficial Intelligence is showing unprecedented performance in signals & image processing. Classification, segmentation and generative process seem to have unlimited potential. The roots of Artificial Intelligence are deep in scientific history, but in the world of Big Data and Internet 5.0, its use and effects have yet to be entirely tested. The black box problem, security, privacy issues, and public opinion are some of the factors that push towards the development of a new concept: "Trustworthy AI". The use of advanced methods, such as EfficientNet & GradCAM, leads to remarkable accuracy and consistent explanation in the classification of ultrasound. Further studies aim at analyzing results could lead to a more robust application of AI in the generalized field of signal and image processing and will lay the foundation for future work on reliable AI.Source: AI & Society 2023 Summer School, La Maddalena, Italy, 05-09/06/2023
See at:
ISTI Repository | sites.google.com | CNR ExploRA
2023
Report
Unknown
Study and development of trustworthy AI application in Medicine
Ignesti G., Moroni D., Martinelli M.
National PhD in Artificial Intelligence, section for Society, first year report.Source: ISTI Working papers, 2023
Ignesti G., Moroni D., Martinelli M.
National PhD in Artificial Intelligence, section for Society, first year report.Source: ISTI Working papers, 2023
See at: CNR ExploRA
2022
Other
Unknown
Tutorial per la piattaforma "TiAssisto" - Corso di formazione per il personale sanitario
Ignesti G., Bruno A., Martinelli M.
Corso di formazione per medici di medicina generale e il personale sanitario delle Residenze Sanitarie Assistenziali coinvolto nel progetto TiAssisto, Bando Covid-19 - Regione Toscana.
Ignesti G., Bruno A., Martinelli M.
Corso di formazione per medici di medicina generale e il personale sanitario delle Residenze Sanitarie Assistenziali coinvolto nel progetto TiAssisto, Bando Covid-19 - Regione Toscana.
See at: CNR ExploRA
2022
Other
Unknown
Tutorial per la piattaforma "TiAssisto" - Corso di formazione per il personale sanitario - Aggiornamenti
Ignesti G., Bruno A., Martinelli M.
Corso di formazione per medici di medicina generale e il personale sanitario delle Residenze Sanitarie Assistenziali coinvolto nel progetto TiAssisto, Bando Covid-19 - Regione Toscana.
Ignesti G., Bruno A., Martinelli M.
Corso di formazione per medici di medicina generale e il personale sanitario delle Residenze Sanitarie Assistenziali coinvolto nel progetto TiAssisto, Bando Covid-19 - Regione Toscana.
See at: CNR ExploRA
2022
Report
Unknown
Una piattaforma di tele-assistenza e tele-monitoraggio di pazienti affetti da Covid-19 - Meeting Scientifico TiAssisto
Ignesti G., Martinelli M.
Presentazione meeting comitato scientifico progetto Regione Toscana Bando Covid-19 TiAssistoSource: ISTI Research report, 2022
Ignesti G., Martinelli M.
Presentazione meeting comitato scientifico progetto Regione Toscana Bando Covid-19 TiAssistoSource: ISTI Research report, 2022
See at: CNR ExploRA
2022
Conference article
Open Access
An intelligent platform of services based on multimedia understanding and telehealth for supporting the management of SARS-CoV-2 multi-pathological patients
Ignesti G., Bruno A., Deri C., D'Angelo G., Bastiani L., Pratali L., Memmini S: Cicalini D., Dini A., Galesi G., Pardini F., Tampucci M., Benassi A., Salvetti O., Moroni D., Martinelli M.
The combination of pervasive sensing and multimedia understanding with the advances in communications makes it possible to conceive platforms of services for providing telehealth solutions responding to the current needs of society. The recent outbreak has indeed posed several concerns on the management of patients at home, urging to devise complex pathways to address the Severe Acute Respiratory Syndrome (SARS) in combination with the usual diseases of an increasingly elder population. In this paper, we present TiAssisto, a project aiming to design, develop, and validate an innovative and intelligent platform of services, having as its main objective to assist both Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) multi-pathological patients and healthcare professionals. This is achieved by researching and validating new methods to improve their lives and reduce avoidable hospitalisations. TiAssisto features telehealth and telemedicine solutions to enable high-quality standards treatments based on Information and Communication Technologies (ICT), Artificial Intelligence (AI) and Machine Learning (ML). Three hundred patients are involvedin our study: one half using our telehealth platform, while the other half participate as a control group for a correct validation. The developed AI models and the Decision Support System assist General Practitioners (GPs) and other healthcare professionals in order to help them in their diagnosis, by providing suggestions and pointing out possible presence or absence of signs that can be related to pathologies. Deep learning techniques are also used to detect the absence or presence of specific signs in lung ultrasound images.Source: SITIS 2022 - 16th International Conference on Signal Image Technology & Internet Based Systems, pp. 553–560, Dijon, France, 18-22/10/2022
DOI: 10.1109/sitis57111.2022.00089
Metrics:
Ignesti G., Bruno A., Deri C., D'Angelo G., Bastiani L., Pratali L., Memmini S: Cicalini D., Dini A., Galesi G., Pardini F., Tampucci M., Benassi A., Salvetti O., Moroni D., Martinelli M.
The combination of pervasive sensing and multimedia understanding with the advances in communications makes it possible to conceive platforms of services for providing telehealth solutions responding to the current needs of society. The recent outbreak has indeed posed several concerns on the management of patients at home, urging to devise complex pathways to address the Severe Acute Respiratory Syndrome (SARS) in combination with the usual diseases of an increasingly elder population. In this paper, we present TiAssisto, a project aiming to design, develop, and validate an innovative and intelligent platform of services, having as its main objective to assist both Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) multi-pathological patients and healthcare professionals. This is achieved by researching and validating new methods to improve their lives and reduce avoidable hospitalisations. TiAssisto features telehealth and telemedicine solutions to enable high-quality standards treatments based on Information and Communication Technologies (ICT), Artificial Intelligence (AI) and Machine Learning (ML). Three hundred patients are involvedin our study: one half using our telehealth platform, while the other half participate as a control group for a correct validation. The developed AI models and the Decision Support System assist General Practitioners (GPs) and other healthcare professionals in order to help them in their diagnosis, by providing suggestions and pointing out possible presence or absence of signs that can be related to pathologies. Deep learning techniques are also used to detect the absence or presence of specific signs in lung ultrasound images.Source: SITIS 2022 - 16th International Conference on Signal Image Technology & Internet Based Systems, pp. 553–560, Dijon, France, 18-22/10/2022
DOI: 10.1109/sitis57111.2022.00089
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Report
Open Access
SI-Lab annual research report 2021
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Report
Unknown
D3.2- TiAssisto. Design e sviluppo dei moduli SW della piattaforma
Martinelli M., Cicalini D., Pratali L., Ignesti G., Bruno A., Moroni D.
Il presente documento è il primo deliverable dell'Obiettivo Operativo 3 "Design e sviluppo dei moduli SW della piattaforma" del progetto TiAssisto e si inserisce nell'attività 3.2: Progettazione e sviluppo di interfacce grafiche personalizzate e user-friendly e soluzioni Web/mobile. Progettazione e sviluppo di moduli software basati sul Web per l'interazione dell'utente con i pazienti, servizi TiAssisto per utenti, parenti e medici. Applicazioni Web personalizzate per migliorare la qualità della vita e l'assistenza remota.Source: ISTI Project report, TiAssisto, D3.2, 2022
Martinelli M., Cicalini D., Pratali L., Ignesti G., Bruno A., Moroni D.
Il presente documento è il primo deliverable dell'Obiettivo Operativo 3 "Design e sviluppo dei moduli SW della piattaforma" del progetto TiAssisto e si inserisce nell'attività 3.2: Progettazione e sviluppo di interfacce grafiche personalizzate e user-friendly e soluzioni Web/mobile. Progettazione e sviluppo di moduli software basati sul Web per l'interazione dell'utente con i pazienti, servizi TiAssisto per utenti, parenti e medici. Applicazioni Web personalizzate per migliorare la qualità della vita e l'assistenza remota.Source: ISTI Project report, TiAssisto, D3.2, 2022
See at: CNR ExploRA
2022
Report
Unknown
D6.3- TiAssisto Prototipo piattaforma TiAssisto in era Covid -19 nei diversi setting di pazienti
Ignesti G., Bruno A., Deri C., D'Angelo G., Pratali L., Martinelli M.
Il presente documento è il terzo deliverable dell'Obiettivo Operativo 6 "Sottomissione comitato etico locale, arruolamento, sviluppo, formazione, test e validazione" del progetto TiAssisto e si inserisce nell'attività 6.3.Source: ISTI Project report, TiAssisto, D6.3, 2022
Ignesti G., Bruno A., Deri C., D'Angelo G., Pratali L., Martinelli M.
Il presente documento è il terzo deliverable dell'Obiettivo Operativo 6 "Sottomissione comitato etico locale, arruolamento, sviluppo, formazione, test e validazione" del progetto TiAssisto e si inserisce nell'attività 6.3.Source: ISTI Project report, TiAssisto, D6.3, 2022
See at: CNR ExploRA
2022
Report
Unknown
D2.2- TiAssisto Requisiti tecnico-scientifici, integrazione fra analisi delle immagini e l'uso della telemedicina
Ignesti G., Moroni D., Pratali L., Martinelli M.
Definizione dei requisiti delle basi scientifiche e tecnologiche, analisi delle conoscenze riguardo l'integrazione delle immagini e sull'uso della telemedicina, identificazione degli standards.Source: ISTI Project report, TiAssisto, D2.2, 2022
Ignesti G., Moroni D., Pratali L., Martinelli M.
Definizione dei requisiti delle basi scientifiche e tecnologiche, analisi delle conoscenze riguardo l'integrazione delle immagini e sull'uso della telemedicina, identificazione degli standards.Source: ISTI Project report, TiAssisto, D2.2, 2022
See at: CNR ExploRA
2022
Report
Unknown
D5.2 - TiAssisto integrazione ed interoperabilità dei servizi
Bruno A., Ignesti G., Moroni D., Pratali L., Martinelli M.
Integrazione ed interoperabilità dei Servizi della piattaforma TiAssisto.Source: ISTI Project report, TiAssisto, D5.2, 2022
Bruno A., Ignesti G., Moroni D., Pratali L., Martinelli M.
Integrazione ed interoperabilità dei Servizi della piattaforma TiAssisto.Source: ISTI Project report, TiAssisto, D5.2, 2022
See at: CNR ExploRA
2022
Report
Unknown
D5.1 TiAssisto - Progettazione delle funzionalità di interazione dei servizi per l'utente finale
Ignesti G., Bruno A., Galesi G., Pardini F., Cicalini D., Roth L., Pratali L., Martinelli M.
Il presente documento è il primo deliverable dell'Obiettivo Operativo 5 "Servizi per gli utilizzatori finali".Source: ISTI Project report, TiAssisto, D5.1, 2022
Ignesti G., Bruno A., Galesi G., Pardini F., Cicalini D., Roth L., Pratali L., Martinelli M.
Il presente documento è il primo deliverable dell'Obiettivo Operativo 5 "Servizi per gli utilizzatori finali".Source: ISTI Project report, TiAssisto, D5.1, 2022
See at: CNR ExploRA
2021
Report
Unknown
D3.1- TiAssisto - Identificazione degli standard
Ignesti G., Ragognetti G., Moroni D., Pratali L., Martinelli M.
Implementazione Piattaforma del progetto TiAssisto - Attività 3.1 "Design e sviluppo del sistema di gestione dati" di cui rappresenta l'output finale.Source: ISTI Project report, TiAssisto, D3.1, 2021
Ignesti G., Ragognetti G., Moroni D., Pratali L., Martinelli M.
Implementazione Piattaforma del progetto TiAssisto - Attività 3.1 "Design e sviluppo del sistema di gestione dati" di cui rappresenta l'output finale.Source: ISTI Project report, TiAssisto, D3.1, 2021
See at: CNR ExploRA
2021
Report
Unknown
D4.2: TiAssisto - Implementazione dei servizi intelligenti di supporto alla decisione
Ignesti G., Deri C., D'Angelo G., Moroni D., Pratali L., Martinelli M.
Implementazione dei servizi intelligenti di supporto alla decisione (IDSS).Source: ISTI Project report, TiAssisto, D4.2, 2021
Ignesti G., Deri C., D'Angelo G., Moroni D., Pratali L., Martinelli M.
Implementazione dei servizi intelligenti di supporto alla decisione (IDSS).Source: ISTI Project report, TiAssisto, D4.2, 2021
See at: CNR ExploRA
2021
Report
Unknown
D4.1 TiAssisto - Data processing e servizi di supporto alla decisione intelligente
Ignesti G., Bruno A., Pardini F., Moroni D., Deri C., D'Angelo G., Pratali L., Martinelli M.
Il presente documento è il primo deliverable dell'Obiettivo Operativo 4 "Data processing e servizi di supporto alla decisione intelligente".Source: ISTI Project report, TiAssisto, D4.1, 2021
Ignesti G., Bruno A., Pardini F., Moroni D., Deri C., D'Angelo G., Pratali L., Martinelli M.
Il presente documento è il primo deliverable dell'Obiettivo Operativo 4 "Data processing e servizi di supporto alla decisione intelligente".Source: ISTI Project report, TiAssisto, D4.1, 2021
See at: CNR ExploRA