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National Research Council, Italy

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24 Papers

Digital Twins for Traffic Congestion in Smart Cities: A Novel Solution Using Data Mining Techniques

Topics: Information Technology and Information System Research; Big Data and Analytics; Process Modeling and Mining

Authors: Arianna Anniciello , Simona Fioretto , Elio Masciari and Enea Napolitano

Affiliation: Department of Electrical and Information Technology Engineering, University of Naples Federico II, Italy

Digital Object Identifier: 10.5220/0012208100003598

Proceedings of KMIS 2023, ISBN: 978-989-758-671-2

Quick Abstract

This article serves as a position paper that explores the complex issue of traffic management in smart cities and the challenges it presents. The problem of urban traffic is particularly relevant in our modern world, where more and more people are moving to urban environments, leading to congestion, pollution and reduced quality of life. To address this challenge, we propose an innovative methodology based on Digital Twins. The paper proposes an extended approach that integrates Digital Twins with other existing techniques such as Trajectory Mining, Process Mining, and Decision Making. These techniques, which combine motion data, process analysis, and data-driven Decision Making, can enrich the Digital Twin model, provide a deeper understanding of traffic flows, and deliver more targeted and effective traffic management solutions. This proposal represents a significant step forward in the search for innovative and sustainable solutions for urban traffic management, and lays the foundation for further research and development in this critical area.

Clustered Majority Judgement

Topics: Decision Support Systems; Data Integrity; Pattern Recognition; Data Analytics; Information and Scientific Visualization; Web Analytics; Data Science; Big Data as a Service; Semantic Web Technologies for Data Management

Authors: Emanuele d’Ajello , Davide Formica , Elio Masciari , Gaia Mattia , Arianna Anniciello , Cristina Moscariello , Stefano Quintarelli and Davide Zaccarella

Affiliation: University of Napoli Federico II, Napoli, Italy

Digital Object Identifier: 10.5220/0011319400003269

Proceedings of DATA 2022, ISBN: 978-989-758-583-8

Quick Abstract

In order to overcome the classical methods of judgement, in the literature there is a lot of material about different methodology and their intrinsic limitations. One of the most relevant modern model to deal with votation system dynamics is the Majority Judgement. It was created with the aim of reducing polarization of the electorate in modern democracies and not to alienate minorities, thanks to its use of a highest median rule, producing more informative results than the existing alternatives. Nonetheless, as shown in the literature, in the case of multiwinner elections it can lead to scenarios in which minorities, albeit numerous, are not adequately represented. For this reason our aim is to implement a clustered version of this algorithm, in order to mitigate these disadvantages: it creates clusters taking into account the similarity between the expressed judgements and then for, each of these created groups, Majority Judgement rule is applied to return a ranking over the set of candidates. These traits make the algorithm available for applications in different areas of interest in which a decisional process is involved.

Decision Making with Clustered Majority Judgment

Topics: e-Government; Intelligent Information Systems; Big Data and Analytics; Tools and Technologies for Knowledge Management; Collaborative Software

Authors: Emanuele D’ajello ¹ , Davide Formica ² , Elio Masciari ¹ , Gaia Mattia ¹ , Arianna Anniciello ¹ , Cristina Moscariello ¹ , Stefano Quintarelli ¹ and Davide Zaccarella ¹

Affiliations: ¹ University of Napoli Federico II, Napoli, Italy , ² Copernicani, Milano, Italy

Digital Object Identifier: 10.5220/0011524600003335

Proceedings of KMIS 2022/IC3K, ISBN: 978-989-758-614-9

Quick Abstract

In order to make a decision process that can best represent the will of a group of people who express themselves about something, like the election of a president or any other situation where people make judgements about more than two possibilities, this paper wants to propose the usage of unsupervised learning techniques, in particular cluster techniques, to extend a single-winner voting system Majority Judgement to a multi-winner system which aggregate the preferences of subsets of voters. After an introduction about Majority Judgement, the algorithm used for its clustered version is presented. In the end, a case study will be reported to highlight the differences with the classic Majority Judgment, since sometimes it could be preferable based on the contingencies of the particular election, especially when there is a desire not to neglect minority groups with the same preferences.

Overview of the SMART-BEAR Technical Infrastructure

Topics: Home Care Monitoring Systems; Remote Monitoring; Autonomy and Active Ageing; Dignity and Equal Opportunities of Senior Citizens; Internet of Things and Smart Devices for Independent Living

Authors: Vadim Peretokin ¹ , Ioannis Basdekis ² , Ioannis Kouris ³ , Jonatan Maggesi ⁴ , Mario Sicuranza ⁵ , Qiqi Su ⁶ , Alberto Acebes ⁷ , Anca Bucur ¹ , Vinod Jaswanth Roy Mukkala ⁴ , Konstantin Pozdniakov ⁶ , Christos Kloukinas ⁶ , Dimitrios D. Koutsouris ³ , Elefteria Iliadou ⁸ , Ioannis Leontsinis ⁹ , Luigi Gallo ⁵ , Giuseppe De Pietro ⁵ and George Spanoudakis ²

Affiliations: ¹ Philips Research, Eindhoven, The Netherlands , ² SPHYNX Technology Solutions AG, Zug, CH, Switzerland , ³ Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece , ⁴ Computer Science Department, Università degli Studi di Milano, Milan, Italy , ⁵ Institute for High-Performance Computing and Networking, National Research Council of Italy, ICAR - CNR, Naples, Italy , ⁶ Department of Computer Science, City, University of London, London, U.K. , ⁷ Atos Research and Innovation. Madrid, Spain 81st Otolaryngology University Department, National and Kapodistrian University of Athens, Athens, Greece 91st Cardiology Clinic, Medical school, National and Kapodistrian University of Athens, Athens Greece , ⁸ 1st Otolaryngology University Department, National and Kapodistrian University of Athens, Athens, Greece , ⁹ 1st Cardiology Clinic, Medical school, National and Kapodistrian University of Athens, Athens Greece

Digital Object Identifier: 10.5220/0011082700003188

Proceedings of ICT4AWE 2022, ISBN: 978-989-758-566-1

Quick Abstract

This paper describes a cloud-based platform that offers evidence-based, personalised interventions powered by Artificial Intelligence to help support efficient remote monitoring and clinician-driven guidance to people over 65 who suffer or are at risk of hearing loss, cardiovascular diseases, cognitive impairments, balance disorders, and mental health issues. This platform has been developed within the SMART-BEAR integrated project to power its large-scale clinical pilots and comprises a standards-based data harmonisation and management layer, a security component, a Big Data Analytics system, a Clinical Decision Support tool, and a dashboard component for efficient data collection across the pilot sites.

A Reinforcement Learning and IoT based System to Assist Patients with Disabilities

Authors: Muddasar Naeem ¹ , Antonio Coronato ² , Giovanni Paragliola ² and Giuseppe De Pietro ²

Affiliations: ¹ Universita’ Degli Studi di Napoli Parthenope, Napoli, Italy , ² ICAR-CNR, Napoli, Italy

Digital Object Identifier: 10.5220/0009783804910498

Proceedings of AI4EIoTs 2020, ISBN: 978-989-758-426-8

Quick Abstract

One of the important aspect of clinical process is to complete treatment according to given plan. The successful completion of this task is more challenging when a person have some physical or mental disability and requires resources and man power for personalized treatment and care. We can mitigate this problem by an intelligent guidance and monitoring system who can assist elderly persons and patients in their treatment schedule. Reinforcement learning and IoT systems have received considerable credit of significant contribution in healthcare over last few years, could be suitable choice for said objective. We propose a pill reminder system using Bayesian reinforcement learning assisted with IoT devices to help people (having mental and/or physical disability) in their treatment plan. The proposed intelligent system is able to successfully communicate with the person through a suitable audio, visual and textual message. The proposed pill-reminder system has been demonstrated for a specific treatment plan of a hypertension patient.

Leveraging Machine Learning for Fake News Detection

Topics: Data Mining; Social Data Analytics

Authors: Elio Masciari , Vincenzo Moscato , Antonio Picariello and Giancarlo Sperlì

Affiliation: University Federico II, Naples, Italy

Digital Object Identifier: 10.5220/0009767401510157

Proceedings of DATA 2020, ISBN: 978-989-758-440-4

Quick Abstract

The uncontrolled growth of fake news creation and dissemination we observed in recent years causes continuous threats to democracy, justice, and public trust. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies. Early detection of fake news is crucial, however the availability of information about news propagation is limited. Moreover, it has been shown that people tend to believe more fake news due to their features (Vosoughi et al., 2018). In this paper, we present our complete framework for fake news detection and we discuss in detail a solution based on machine learning. Our experiments conducted on two well-known and widely used real-world datasets suggest that our settings can outperform the state-of-the-art approaches and allows fake news accurate detection, even in the case of limited content information.

A Machine Learning Approach for Carotid Diseases using Heart Rate Variability Features

Authors: Laura Verde ¹ and Giuseppe De Pietro ²

Affiliations: ¹ University of Naples Parthenope, Italy , ² Institute of High Performance Computing and Networking (ICAR) and National Research Council of Italy (CNR), Italy

Digital Object Identifier: 10.5220/0006730806580664

Proceedings of AI4Health 2018, ISBN: 978-989-758-281-3

Quick Abstract

In the last few years the incidence of carotid diseases has been increasing rapidly. Atherosclerosis constitutes a major cause of morbidities and mortalities worldwide. The early detection of these diseases is considered necessary to avoid tragic consequences and automatic systems and algorithms can be a valid support for their diagnosis. The main objective of this study is to investigate and compare the performances of different machine learning techniques capable of detecting the presence of a carotid disease by analysing the Heart Rate Variability (HRV) parameters of opportune electrocardiographic signals selected from an appropriate database available online on the Physionet website. All the analyses are evaluated in terms of accuracy, precision, recall and F-measure.

Preliminary Steps towards Efficient Classification in Large Medical Datasets: Structure Optimization for Deep Learning Networks through Parallelized Differential Evolution

Authors: Ivanoe De Falco ¹ , Giuseppe De Pietro ¹ , Antonio Della Cioppa ² , Giovanna Sannino ¹ , Umberto Scafuri ¹ and Ernesto Tarantino ¹

Affiliations: ¹ National Research Council of Italy (CNR), Italy , ² University of Salerno, Italy

Digital Object Identifier: 10.5220/0006730006330640

Proceedings of AI4Health 2018, ISBN: 978-989-758-281-3

Quick Abstract

Deep Neural Networks are being more and more widely used to perform several tasks over highly-sized datasets, one of them being classification. Finding good configurations for Deep Neural Network structures is a very important problem in general, and particularly in the medical domain. Currently, either trial-and-error methodologies or sampling-based ones are considered. This paper describes some preliminary steps towards effectively facing this task. The first step consists in the use of Differential Evolution, a kind of an Evolutionary Algorithm. The second lies in using a parallelized version in order to reduce the turnaround time. The preliminary results obtained here show that this approach can be useful in easily obtaining structures that allow increases in the network accuracy with respect to those provided by humans.

A FHIR-based System for the Generation and Retrieval of Clinical Documents

Topics: Electronic Health Records; Case Studies in e-Health; Diagnostic Support; Interoperability and System Integration

Authors: Crescenzo Diomaiuta , Mario Sicuranza , Mario Ciampi and Giuseppe De Pietro

Affiliation: National Research Council of Italy, Italy

Digital Object Identifier: 10.5220/0006311301350142

Proceedings of ICT4AWE 2017, ISBN: 978-989-758-251-6

Quick Abstract

Clinical information exchange among heterogeneous systems is a complex process. It is necessary to use dictionaries, coding standards (e.g. LOINC and SNOMED), and terminology services, in order to: i) produce machine-interpretable information and coded data, ii) reduce information exchange complexity, and iii) allow the fulfillment of semantic interoperability among several systems. This work presents a system to support healthcare professionals in the creation of clinical documents using appropriate standards and in the information reading and retrieval of interest on clinical documents. The system allows healthcare professionals to use standard codes easily (via terminology server) during the creation of documents. Similarly, it also permits the decoding of health standard codes, during the read documents process, to facilitate the obtaining of information. In summary, it makes use of a simple and intuitive graphical user interface and a Fast Healthcare Interoperability Resources (FHIR) terminology server to support healthcare professionals in diagnosing and treating patients.

An Open Source System for Big Data Warehousing

Authors: Nunziato Cassavia ¹ , Elio Masciari ² and Domenico Saccà ³

Affiliations: ¹ ICAR-CNR and UNICAL, Italy , ² ICAR-CNR, Italy , ³ UNICAL and Centro di Competenza ICT-SUD, Italy

Digital Object Identifier: 10.5220/0006485703060313

Proceedings of KomIS 2017, ISBN: 978-989-758-255-4

Quick Abstract

The pervasive diffusion of new data generation devices has recently caused the generation of massive data flows containing heterogeneous information generated at different rates and having different formats. These data are referred as \emph{Big Data} and require new storage and analysis approaches to be investigated for managing them. In this paper we will describe a system for dealing with massive big data stores. We defined an open source tool that exploits a NoSQL approach for data warehousing in order to offer user am intuitive way to easily query data that could be quite hard to be understood otherwise.

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