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Book ; Online: Sistemi ACM e Imaging Diagnostico

Buscema, Paolo Massimo

Le immagini mediche come Matrici Attive di Connessioni

2006

Author's details	by Paolo Massimo Buscema
Keywords	Radiology, Medical
Publisher	Springer-Verlag Italia
Publishing place	Milano
Document type	Book ; Online
HBZ-ID	TT050387919
ISBN	978-88-470-0387-3 ; 978-88-470-0444-3 ; 88-470-0387-3 ; 88-470-0444-6
DOI	10.1007/88-470-0444-6
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: A Pattern Recognition Analysis of Vessel Trajectories

Paolo Massimo Buscema / Giulia Massini / Giovanbattista Raimondi / Giuseppe Caporaso / Marco Breda / Riccardo Petritoli

Algorithms, Vol 16, Iss 414, p

2023 Volume 414

Abstract: The automatic identification system (AIS) facilitates the monitoring of ship movements and provides essential input parameters for traffic safety. Previous studies have employed AIS data to detect behavioral anomalies and classify vessel types using ... ...

Abstract	The automatic identification system (AIS) facilitates the monitoring of ship movements and provides essential input parameters for traffic safety. Previous studies have employed AIS data to detect behavioral anomalies and classify vessel types using supervised and unsupervised algorithms, including deep learning techniques. The approach proposed in this work focuses on the recognition of vessel types through the “Take One Class at a Time” (TOCAT) classification strategy. This approach pivots on a collection of adaptive models rather than a single intricate algorithm. Using radar data, these models are trained by taking into account aspects such as identifiers, position, velocity, and heading. However, it purposefully excludes positional data to counteract the inconsistencies stemming from route variations and irregular sampling frequencies. Using the given data, we achieved a mean accuracy of 83% on a 6-class classification task.
Keywords	AIS ; vessel classification ; TOCAT ; Industrial engineering. Management engineering ; T55.4-60.8 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2023-08-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: MicroRNA expression is associated with auditory dysfunction in workers exposed to ototoxic solvents and noise.

Sisto, Renata / Moleti, Arturo / Capone, Pasquale / Sanjust, Filippo / Cerini, Luigi / Tranfo, Giovanna / Massini, Giulia / Buscema, Sara / Buscema, Paolo Massimo / Chiarella, Pieranna

Frontiers in public health

2022 Volume 10, Page(s) 958181

Abstract: This study is part of a project on early hearing dysfunction induced by combined exposure to volatile organic compounds (VOCs) and noise in occupational settings. In a previous study, 56 microRNAs were found differentially expressed in exposed workers ... ...

Abstract	This study is part of a project on early hearing dysfunction induced by combined exposure to volatile organic compounds (VOCs) and noise in occupational settings. In a previous study, 56 microRNAs were found differentially expressed in exposed workers compared to controls. Here, we analyze the statistical association of microRNA expression with audiometric hearing level (HL) and distortion product otoacoustic emission (DPOAE) level in that subset of differentially expressed microRNAs. The highest negative correlations were found; for HL, with miR-195-5p and miR-122-5p, and, for DPOAEs, with miR-92b-5p and miR-206. The homozygous (
MeSH term(s)	Auditory Threshold ; Hearing Loss, Noise-Induced/diagnosis ; Hearing Loss, Noise-Induced/genetics ; Humans ; MicroRNAs/genetics ; Ototoxicity ; Solvents/toxicity ; Volatile Organic Compounds/adverse effects
Chemical Substances	MIRN206 microRNA, human ; MicroRNAs ; Solvents ; Volatile Organic Compounds
Language	English
Publishing date	2022-09-20
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2022.958181
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: COVID-19 in Italy and extreme data mining.

Buscema, Paolo Massimo / Della Torre, Francesca / Breda, Marco / Massini, Giulia / Grossi, Enzo

Physica A

2020 Volume 557, Page(s) 124991

Abstract: In this article we want to show the potential of an evolutionary algorithm called Topological Weighted Centroid (TWC). This algorithm can obtain new and relevant information from extremely limited and poor datasets. In a world dominated by the concept of ...

Abstract	In this article we want to show the potential of an evolutionary algorithm called Topological Weighted Centroid (TWC). This algorithm can obtain new and relevant information from extremely limited and poor datasets. In a world dominated by the concept of big (fat?) data we want to show that it is possible, by necessity or choice, to work profitably even on small data. This peculiarity of the algorithm means that even in the early stages of an epidemic process, when the data are too few to have sufficient statistics, it is possible to obtain important information. To prove our theory, we addressed one of the most central issues at the moment: the COVID-19 epidemic. In particular, the cases recorded in Italy have been selected. Italy seems to have a central role in this epidemic because of the high number of measured infections. Through this innovative artificial intelligence algorithm, we have tried to analyze the evolution of the phenomenon and to predict its future steps using a dataset that contained only geospatial coordinates (longitude and latitude) of the first recorded cases. Once the coordinates of the places where at least one case of contagion had been officially diagnosed until February 26th, 2020 had been collected, research and analysis was carried out on: outbreak point and related heat map (TWC alpha); probability distribution of the contagion on February 26th (TWC beta); possible spread of the phenomenon in the immediate future and then in the future of the future (TWC gamma and TWC theta); how this passage occurred in terms of paths and mutual influence (Theta paths and Markov Machine). Finally, a heat map of the possible situation towards the end of the epidemic in terms of infectiousness of the areas was drawn up. The analyses with TWC confirm the assumptions made at the beginning.
Keywords	covid19
Language	English
Publishing date	2020-07-25
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1466577-3
ISSN	1873-2119 ; 0378-4371
ISSN (online)	1873-2119
ISSN	0378-4371
DOI	10.1016/j.physa.2020.124991
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Article ; Online: Computer Aided Diagnosis for atrial fibrillation based on new artificial adaptive systems.

Buscema, Paolo Massimo / Grossi, Enzo / Massini, Giulia / Breda, Marco / Della Torre, Francesca

Computer methods and programs in biomedicine

2020 Volume 191, Page(s) 105401

Abstract: Background and objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, having been recognized as a true cardiovascular epidemic. In this paper, a new methodology for Computer Aided Diagnosis of AF based on a ... ...

Abstract	Background and objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, having been recognized as a true cardiovascular epidemic. In this paper, a new methodology for Computer Aided Diagnosis of AF based on a special kind of artificial adaptive systems has been developed. Methods: Following the extraction of data from the PhysioNet repository, a new dataset composed of the R/R distances of 73 patients was created. To avoid redundancy, the training set was created by randomly selecting 50% of the subjects from the entire sample, thus making a choice by patient and not by record. The remaining 50% of subjects were randomly split by records in testing and prediction sets. The original ECG data has been transformed according to the following four orders of abstraction: a) sequence of R/R intervals; b) composition of ECG data into a moving window; c) training of different machine learning systems to abstract the function governing the AF; d) fuzzy transformation of Machine learning estimations. In this paper, in parallel with the classic method of windowing, we propose a variant based on a system of progressive moving averages. Results: The best performing machine learning, Supervised Contractive Map (SVCm), reached an overall mean accuracy of 95%. SVCm is a new deep neural network based on a different principle than the usual descending gradient. The minimization of the error occurs by means of decomposition into contracted sine functions. Conclusions: In this research, atrial fibrillation is considered from a completely different point of view than classical methods. It is seen as the stable process, i.e. the function, that manages the irregularity of the irregularities of the R/R intervals. The idea, therefore, is to abstract from mere physiology to investigate fibrillation as a mathematical object that handles irregularities. The attained results seem to open new perspectives for the use of potent artificial adaptive systems for the automatic detection of atrial fibrillation, with accuracy rates extremely promising for real world applications.
MeSH term(s)	Algorithms ; Atrial Fibrillation/diagnosis ; Databases, Factual ; Diagnosis, Computer-Assisted ; Humans ; Machine Learning
Language	English
Publishing date	2020-02-19
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2020.105401
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Article ; Online: COVID-19 in Italy and extreme data mining

Buscema, Paolo Massimo / Della Torre, Francesca / Breda, Marco / Massini, Giulia / Grossi, Enzo

Physica A: Statistical Mechanics and its Applications

2020 Volume 557, Page(s) 124991

Keywords	Statistics and Probability ; Condensed Matter Physics ; covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ZDB-ID	1466577-3
ISSN	1873-2119 ; 0378-4371
ISSN (online)	1873-2119
ISSN	0378-4371
DOI	10.1016/j.physa.2020.124991
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Alcohol Addiction: One Entity or Different Entities? A DSM-4-Based Attempt Toward a Geographicization of Alcohol Addiction and Abuse.

Allamani, Allaman / Voller, Fabio / Bravi, Stefano / Pepe, Pasquale / Biffino, Marco / Buscema, Paolo Massimo / Maurelli, Guido / Massini, Giulia / Einstein, S / Manthey, Jakob / Rehm, Jürgen

Alcohol and alcoholism (Oxford, Oxfordshire)

2022 Volume 57, Issue 6, Page(s) 687–695

Abstract: Aim: To examine whether in Europe perceptions of 'alcoholism' differ in a discrete manner according to geographical area.: Method: Secondary analysis of a data set from a European project carried out in 2013-2014 among 1767 patients treated in ... ...

Abstract	Aim: To examine whether in Europe perceptions of 'alcoholism' differ in a discrete manner according to geographical area. Method: Secondary analysis of a data set from a European project carried out in 2013-2014 among 1767 patients treated in alcohol addiction units of nine countries/regions across Europe. The experience of all 11 DSM-4 criteria used for diagnosing 'alcohol dependence' and 'alcohol abuse' were assessed in patient interviews. The analysis was performed through Multiple Correspondence Analysis. Results: The symptoms of 'alcohol dependence' and 'alcohol abuse', posited by DSM-IV, were distributed according to three discrete geographical patterns: a macro-area mainly centered on drinking beer and spirit, a culture traditionally oriented toward wine and a mixed intermediate alcoholic beverage situation. Conclusion: These patterns of perception seem to parallel the diverse drinking cultures of Europe.
MeSH term(s)	Humans ; Alcohol Drinking/epidemiology ; Alcohol Drinking/adverse effects ; Alcoholism/diagnosis ; Alcoholism/epidemiology ; Beer ; Diagnostic and Statistical Manual of Mental Disorders ; Europe/epidemiology ; Wine
Language	English
Publishing date	2022-05-18
Publishing country	England
Document type	Journal Article
ZDB-ID	604956-4
ISSN	1464-3502 ; 0309-1635 ; 0735-0414
ISSN (online)	1464-3502
ISSN	0309-1635 ; 0735-0414
DOI	10.1093/alcalc/agac021
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Article ; Online: Theory of impossible worlds: Toward a physics of information.

Buscema, Paolo Massimo / Sacco, Pier Luigi / Della Torre, Francesca / Massini, Giulia / Breda, Marco / Ferilli, Guido

Chaos (Woodbury, N.Y.)

2018 Volume 28, Issue 5, Page(s) 55914

Abstract: In this paper, we introduce an innovative approach to the fusion between datasets in terms of attributes and observations, even when they are not related at all. With our technique, starting from datasets representing independent worlds, it is possible ... ...

Abstract	In this paper, we introduce an innovative approach to the fusion between datasets in terms of attributes and observations, even when they are not related at all. With our technique, starting from datasets representing independent worlds, it is possible to analyze a single global dataset, and transferring each dataset onto the others is always possible. This procedure allows a deeper perspective in the study of a problem, by offering the chance of looking into it from other, independent points of view. Even unrelated datasets create a metaphoric representation of the problem, useful in terms of speed of convergence and predictive results, preserving the fundamental relationships in the data. In order to extract such knowledge, we propose a new learning rule named double backpropagation, by which an auto-encoder concurrently codifies all the different worlds. We test our methodology on different datasets and different issues, to underline the power and flexibility of the Theory of Impossible Worlds.
Language	English
Publishing date	2018-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	1472677-4
ISSN	1089-7682 ; 1054-1500
ISSN (online)	1089-7682
ISSN	1054-1500
DOI	10.1063/1.5024371
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Article ; Online: Artificial Neural Networks: an overview and their use in the analysis of the AMPHORA-3 dataset.

Buscema, Paolo Massimo / Massini, Giulia / Maurelli, Guido

Substance use & misuse

2014 Volume 49, Issue 12, Page(s) 1555–1568

Abstract: The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. ...

Abstract	The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.
MeSH term(s)	Alcohol Drinking/epidemiology ; Alcohol Drinking/prevention & control ; Europe/epidemiology ; Health Policy ; Humans ; Models, Statistical ; Neural Networks (Computer)
Language	English
Publishing date	2014-10
Publishing country	England
Document type	Journal Article
ZDB-ID	1310358-1
ISSN	1532-2491 ; 1082-6084
ISSN (online)	1532-2491
ISSN	1082-6084
DOI	10.3109/10826084.2014.933009
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Article: COVID-19 in Italy and extreme data mining

Buscema, Paolo Massimo / Della Torre, Francesca / Breda, Marco / Massini, Giulia / Grossi, Enzo

Phys A Stat Mech Appl

Abstract	In this article we want to show the potential of an evolutionary algorithm called Topological Weighted Centroid (TWC). This algorithm can obtain new and relevant information from extremely limited and poor datasets. In a world dominated by the concept of big (fat?) data we want to show that it is possible, by necessity or choice, to work profitably even on small data. This peculiarity of the algorithm means that even in the early stages of an epidemic process, when the data are too few to have sufficient statistics, it is possible to obtain important information. To prove our theory, we addressed one of the most central issues at the moment: the COVID-19 epidemic. In particular, the cases recorded in Italy have been selected. Italy seems to have a central role in this epidemic because of the high number of measured infections. Through this innovative artificial intelligence algorithm, we have tried to analyze the evolution of the phenomenon and to predict its future steps using a dataset that contained only geospatial coordinates (longitude and latitude) of the first recorded cases. Once the coordinates of the places where at least one case of contagion had been officially diagnosed until February 26th, 2020 had been collected, research and analysis was carried out on: outbreak point and related heat map (TWC alpha); probability distribution of the contagion on February 26th (TWC beta); possible spread of the phenomenon in the immediate future and then in the future of the future (TWC gamma and TWC theta); how this passage occurred in terms of paths and mutual influence (Theta paths and Markov Machine). Finally, a heat map of the possible situation towards the end of the epidemic in terms of infectiousness of the areas was drawn up. The analyses with TWC confirm the assumptions made at the beginning.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #669972
Database	COVID19

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