
Massimo BuscemaSemeion Centro Ricerche di Scienze della Comunicazione
Massimo Buscema
Laurea at University of Rome, Italy
Full Professor Adjoint
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315
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Introduction
Massimo Buscema is the director of Semeion (Research Center of Sciences of Communicaion, Rome, Italy) and currently works , as Full Professor Adjoint at the Department of Mathematical and Statistical Sciences, University of Colorado (Denver, USA). Massimo does research in Algorithms, Artificial Intelligence and Artificial Neural Network. His current projects are
1. EEG in autism diagnosis (I FAST Algorithm).
2. Geographic Profiling (TWC Algorithm).
3. Theory of Impossible World (ANNs working with many data sets simultaneously, not linked each others).
4. Deep Learning and Real Deep Learning using an eco system of different ANNs cooperating into a set of Meta Nets.
5. Data Matrix Theory : a algebraic theory of non linear operators using adaptive algorithms (ANNs and Evolutionary Systems)
Publications
Publications (315)
Idiopathic Normal Pressure Hydrocephalus (INPH) patients present symptoms common to other diseases, as dementia (AD). However, while dementia is not reversible, INPH dementia can be treated through neurosurgery.
The perception characteristics of a small sample of patients (n = 19) were observed through the Rorschach Inblok test. Artificial Neural N...
Idiopathic Normal Pressure Hydrocephalus (INPH) patients present symptoms common to other diseases, as dementia (AD). However, while dementia is not reversible, INPH dementia can be treated through neurosurgery.
The perception characteristics of a small sample of patients (n=19) were observed through the Rorschach Inblok test. Artificial Neural Net...
The availability of freshwater and low-cost electricity are two limiting factors for sustainable living in Hawai'i and worldwide. This fact raises the question: Can technology be developed to locate freshwater and geothermal resources simultaneously? We present a multimodal machine learning (MML) workflow to assimilate and simultaneously predict th...
We present a multiphysics-decision tree learning algorithm for (1) estimating saturated hydraulic conductivity, thermal conductivity, bulk density and longitudinal dispersivity in the variably saturated subsurface governed by explicitly coupled equations for water, heat, and solute transport; and (2) providing reduced order simulation of time-depen...
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...
Two simple examples are presented next to further illustrate the TWC algorithms so that a sense of how the algorithms analyze geographic data work. The example of Chap. 3 came from an application. The following two examples are made up. The first example consists of 10 points and is one in which there are two points that are “outliers”. The second...
The previous chapters mentioned two types of analyses, the inclusion of semantic data associated with spatial data and the inclusion of local effects when they are significant so that these are not “washed out” by the global effects. This chapter begins with the development of how TWC balances local effects with the global ones. Then, this is follo...
TWC theory consists of several components. This chapter reviews the key elements that make up the theory and the constituent algorithms that are used in TWC. There are theoretical components of TWC, among them: (1) pseudo-distances, (2) attraction strength, (3) entropy, and (3) free energy, that are used in various TWC algorithms.
These appendices contain four of TWC the algorithms (alpha, beta, gamma, theta) written in MATLAB code. The MATLAB code was used to generate the maps associated with the illustrative examples Chap. . The code is designed to be used with the data tables in the book and those that the reader might wish to use to test out the approaches associated wit...
A complementary approach to the topological one of TWC is what is called “Geographic Profiling.
This section contains a few of the many applications of TWC that have actually been implemented either at the request of a person or as a project from an agency or as a part of a journal publication. A few have been done as a scientific experiment, one was part of a thesis. There are very many more applications that were successfully run but are no...
Landslides pose a significant risk to human life. The Twisting Theory (TWT) and Crown Clustering Algorithm (CCA) are innovative adaptive algorithms that can determine the shape of a landslide and predict its future evolution based on the movement of position sensors located in the affected area. In the first part of this study, the TWT and CCA will...
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 aud...
We analyze the spatial-temporal dynamics of cultural vibrancy in the Swedish sub-region of Skaraborg. Our database consists of 4,170 geo-localized cultural activities and facilities, mapped between October 2013 and March 2014. We make use of the TWC methodology for the dynamic simulation of the evolution of geo-localized activity starting from an o...
We develop a computational approach to the analysis of cultural vibrancy and to the role of the cultural and creative sectors in the socio-economic organization of two districts of Western Kosovo, Gjakove and Peć. Our analysis is built on a geolocalized mapping of the cultural activities and facilities, and on the main socio-economic variables for...
Le tecniche statistiche tradizionali non possono proiettare in uno spazio bidimensionale un elevato numero di variabili secondo la matrice delle loro distanze reciproche perché il tempo computazionale tende all'infinito. Anche l'inerente non-linearità crea difficoltà. Un nuovo approccio matematico consiste nel misurare la dipendenza generale di var...
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...
In this paper we study the interdependences between the dynamics of the stock market indexes of 30 different stock markets across 29 different countries to analyze the nonlinear dynamics of their information flows. We find that the system exhibits complex dynamic properties that go beyond what has been generally found in the previous literature, su...
We make use of an advanced artificial neural network (Auto-CM) to model the structure of the current world order as a data-driven reconstruction of the implicit relationships between countries and of their time evolution, as derived from a database of publicly observable socioeconomic and political variables. Building on previous research, we analy...
Background and Purpose: The Active Connection Matrixes (ACMs) are unsupervised artificial adaptive systems able to extract from digital images features of interest (edges, tissue differentiation, etc.) unnoticeable with conventional systems. In this proof-of-concept study, we assessed the potentiality of ACMs to increase measurement precision of mo...
The first case of COVID-19 in USA was reported on January 20, 2020. The number of COVID-19 confirmed cases and death has increased since the first reported case and the outbreak has appeared in all states. This paper analyzes disease outbreak using Topological Weighted Centroid (TWC), which is a data driven intelligent geographical dynamical system...
Background and Objective
In 2 previous studies, we have shown the ability of special machine learning systems applied to standard EEG data in distinguishing children with autism spectrum disorder (ASD) from non-ASD children with an overall accuracy rate of 100% and 98.4%, respectively. Since the equipment routinely available in neonatology units em...
Background
Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy o...
Organisations currently compete within contexts that require collaboration with other players (suppliers, customers , competitors), which is central to achieving sustainable competitive advantages. This new perspective, which is centred on relationships, has changed the way companies design and implement their competitive strategies, while also cha...
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...
Objective: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our maingoal was assessing the accuracy of...
Background: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy...
Chianti is a precious red wine and enjoys a high reputation for its high quality in the world wine market. Despite this, the production region is small and product needs efficient tools to protect its brands and prevent adulterations. In this sense, ICP-MS combined with chemometrics has demonstrated its usefulness in food authentication. In this st...
Just two derivation channels of EEG carry a signature of Autism Spectrum Disorder.
Advanced Machine learning systems allow the recognition of ASD from EEG.
ASD can be distinguished from other neuro-psychiatric disorders.
Screening of newborns at risk of ASD is be possible.
In this manuscript the Ebola outbreaks in 2014 and 2018 have been studied. On March 23, 2014, the World Health Organization announced the beginning of the Ebola outbreak in West Africa. The initial location was in a forested area in the south eastern portion of Guinea. We used three different methods to determine the origin of the outbreak. The fir...
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 o...
Data sets collected independently using the same variables can be compared using a new artificial neural network called Artificial neural network What If Theory, AWIT. Given a data set that is deemed the standard reference for some object, i.e. a flower, industry, disease, or galaxy, other data sets can be compared against it to identify its proxim...
The translation of precision medicine in clinical practice will depend mostly on the possibility to make statistical inference at the individual level, exactly positioning a new case in the taxonomy space (diagnosis) or in the time space (prognosis).
As a matter of the fact, clinical epidemiology and medical statistics have not been suited to answe...
Coastal environments are facing constant changes over time due to their dynamic nature and geological, geomorphological, hydrodynamic, biological, climatic and anthropogenic factors. For these reasons, the monitoring of these areas is crucial for the safeguarding of the cultural heritage and the populations living there. The focus of this paper is...
Background:
Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature.
Methods:
Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions....
Knowing the location where groundwater denitrification occurs, or by proxy the groundwater redox status (oxic, mixed, and anoxic), is valuable information for assessing and managing potential agricultural land-use impacts on freshwater quality. We compare the efficacy of supervised (Linear Discriminant Analysis LDA; Boosted Regression Trees, BRT; a...
Background and Objective. In a previous study, we showed a new EEG processing methodology called Multi-Scale Ranked Organizing Map/Implicit Function As Squashing Time (MS-ROM/IFAST) performing an almost perfect distinction between computerized EEG of Italian children with autism spectrum disorder (ASD) and typically developing children. In this stu...
We investigated the relationship between the temporal monitoring series routinely recorded at Mt. Etna and the flank eruptions that occurred between January 2001 and April 2005 by the K-contractive map (K-CM) method pattern classifier with supervised learning. The reference dataset includes 28 variables and 1580 records collected over 52 months for...
This article examines the implicit space grammar of the cultural vibrancy of the region of Halland in Southwest Sweden. By using a new computational approach, we implement for the first time a methodology that allows us, on the one side, to extrapolate the complex dynamic evolution of the region’s cultural geography and, on the other side, to diagn...
Objective: Despite the increasing literature on the association of diabetes with inflammation, cardiovascular risk, and vitamin D (25(OH)D) concentrations, strong evidence on the direction of causality among these factors is still lacking. This gap could be addressed by means of artificial neural networks (ANN) analysis.
Methods: Retrospective obse...
Knowledge of a territory is an essential element in any future planning action and in appropriate territorial and environmental requalification action planning. The current large-scale availability of satellite data, thanks to very high resolution images, provides professional users in the environmental, urban planning, engineering, and territorial...
Objective:
The metabolic syndrome (MS) is a multifactorial disorder associated with a higher risk of developing cardiovascular diseases and type 2 diabetes. However, its pathophysiology and risk factors are still poorly understood. In this study, we investigated the associations among gender, psychosocial variables, job-related stress and the pres...
Since understanding the spatial and temporal structure of epidemic processes is a matter of great concern from both the scientific and political point of view, in the last five years a new approach to the problem has been developed, called the topological approach. The new approach makes it possible to estimate both the location of the outbreak poi...
In the last decades the photogrammetry has undergone interesting innovation, both in terms of data processing and acquisition mode, to allow obtaining detailed 3D models useful for complete survey and important support for the management and recovery of cultural heritage and buildings. However, despite recent developments, the main photogrammetry o...
This paper presents an innovative operationalization of world-system analysis through attributional data, and makes use of an innovative Artificial Neural Network computational tool, the Auto-Contractive Map (AutoCM), to analyze the core-periphery structure of a database including five well-known, publicly available indicators that can jointly be c...
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 possi...
In a previous study the authors have shown the ability of a novel kind of Machine Learning System(MLS) named MS-ROM/I-FAST developed by The Semeion Research Institute in Rome to extract interesting features in computerized EEG that allow an almost perfectly distinction of ASD children from typically developing ones.
The aim of the study is to asse...
We have looked at how to visualize the relationships among the elements of a dataset in Chap. 4. This chapter is devoted to the use of Auto-CM in the transformation of datasets for the purpose of extracting further relationships among data elements. The first transformation we call the FS-Transform, which looks beyond an all or nothing, binary rela...
This is the innovative approach of the Semeion that has developed new algorithms able to combine two different data sets (activities and facilities, weighted by their attributes), and able to analyse the whole data set with ANN, and for the geographical profiling with the TWCs.
Artificial Adaptive Systems include Artificial Neural Networks (ANNs or simply neural networks as they are commonly known). The philosophy of neural networks is to extract from data the underlying model that relates this data as an input/output (domain/range) pair. This is quite different from the way most mathematical modeling processes operate. M...
We look at how to use Auto-CM in the context of datasets that are changing in time. We modify our approach while keeping the original philosophy of Auto-CM. © Springer International Publishing AG, part of Springer Nature 2018.
This chapter focuses on Auto-Contractive Maps, which is a particularly useful ANN. Moreover, the relationship between Auto-Contractive Map (Auto-CM), which is the main topic of this monograph, its relationship to other ANNs and some illustrative example applications are presented. © Springer International Publishing AG, part of Springer Nature 2018...
One of the most powerful aspects of our approach to neural networks is not only the development of the Auto-CM neural network but the visualization of its results. In this chapter we look at two visualization approaches—the Minimal Spanning Tree (MST) and the Maximal Regular Graph (MRG). The resultant from Auto-CM is a matrix of weights. This weigh...
This section is devoted to a more advanced type of Auto-CM that is supervised. © Springer International Publishing AG, part of Springer Nature 2018.
We compare Auto-CM with various other methods that extract patterns from data. The way that we measure the results of comparisons uses MST. © Springer International Publishing AG, part of Springer Nature 2018.
Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1--weighted (T1w) sequences with paramagnetic contrast.
Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classifica...
This research has 6 fundamental aims: (i) to present a modified version of Taylor's interpolation, one that is more effective and faster than the original; (ii) outline the capability of artificial neural networks (ANNs) to perform an optimal functional approximation of the digital elevation model reconstruction from a satellite map, using a small...
This paper offers the first systematic presentation of the topological approach to the analysis of epidemic and pseudo-epidemic spatial processes. We introduce the basic concepts and proofs, at test the approach on a diverse collection of case studies of historically documented epidemic and pseudo-epidemic processes. The approach is found to consis...
We propose an alternative approach to “deep” learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of many different examples of a single situation, we opt for model-based learning and adaptive flexibil...
We propose an alternative approach to “deep” learning that is
based on computational ecologies of structurally diverse artificial neural
networks, and on dynamic associative memory responses to stimuli.
Rather than focusing on massive computation of many different
examples of a single situation, we opt for model-based learning and
adaptive flexibil...
An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely-sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning and estimatio...
Since 1992 the Italian local health units (LHU) gained financial independence and became responsible to provide and d