1,721,000 research outputs found
The non-linear relationship between scientific production and national states' journals
No full textThe main goal of this paper is to show how the scientific production of advanced western countries is distributed and connected in global networks. Together with various indicators for the evaluation of the journal, the database used is provided by Scopus, the most comprehensive for dimension and variety in representing international scientific production. An elementary statistics is not suitable for this task: different journals are linked with different countries, with many indices in a complex many to many relations. Therefore a new artificial adaptive system, named Atemporal Target Diffusion Model (ATDM, for short), will be used for this analysis. ATDM has already proved to be capable of detecting hidden and meaningful relations in different datasets, where traditional multivariate linear algorithms fail [1]. The use of ATDM gives a more comprehensive support for the evaluation of the countries’ scientific production. A comparison of two other models will confirm this assumption
Auto Contractive Maps, the H Function and the Maximally Regular Graph (MRG): a New Methodology for Data Mining
No full textOutcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: Insights from an observational study using artificial neural networks
No full textBackground: Treatment as usual (TAU) for autism spectrum disorders (ASDs) includes eclectic treatments usually available in the community and school inclusion with an individual support teacher. Artificial neural networks (ANNs) have never been used to study the effects of treatment in ASDs. The Auto Contractive Map (Auto-CM) is a kind of ANN able to discover trends and associations among variables creating a semantic connectivity map. The matrix of connections, visualized through a minimum spanning tree filter, takes into account nonlinear associations among variables and captures connection schemes among clusters. Our aim is to use Auto-CM to recognize variables to discriminate between responders versus no responders at TAU. Methods: A total of 56 preschoolers with ASDs were recruited at different sites in Italy. They were evaluated at T0 and after 6 months of treatment (T1). The children were referred to community providers for usual treatments. Results: At T1, the severity of autism measured through the Autism Diagnostic Observation Schedule decreased in 62% of involved children (Response), whereas it was the same or worse in 37% of the children (No Response). The application of the Semeion ANNs overcomes the 85% of global accuracy (Sine Net almost reaching 90%). Consequently, some of the tested algorithms were able to find a good correlation between some variables and TAU outcome. The semantic connectivity map obtained with the application of the Auto-CM system showed results that clearly indicated that “Response” cases can be visually separated from the “No Response” cases. It was possible to visualize a response area characterized by “Parents Involvement high”. The resultant No Response area strongly connected with “Parents Involvement low”. Conclusion: The ANN model used in this study seems to be a promising tool for the identification of the variables involved in the positive response to TAU in autism
MST Fitness Index and implicit data narratives: a comparative test on alternative unsupervised algorithms
No full textIn this paper, we introduce a new methodology for the evaluation of alternative algorithms in capturing the deep statistical structure of datasets of different types and nature, called MST Fitness, and based on the notion of Minimum Spanning Tree (MST). We test this methodology on six different databases, some of which artificial and widely used in similar experimentations, and some related to real world phenomena. Our test set consists of eight different algorithms, including some widely known and used, such as Principal Component Analysis, Linear Correlation, or Euclidean Distance. We moreover consider more sophisticated Artificial Neural Network based algorithms, such as the Self-Organizing Map (SOM) and a relatively new algorithm called Auto-Contractive Map (AutoCM). We find that, for our benchmark of datasets, AutoCM performs consistently better than all other algorithms for all of the datasets, and that its global performance is superior to that of the others of several orders of magnitude. It is to be checked in future research if AutoCM can be considered a truly general-purpose algorithm for the analysis of heterogeneous categories of datasets
La complessità strutturale dei distretti industriali: un approccio basato sulle similarità multidimensionali
No full textOpen societies and social sustainability: toward a new synthetic index for the emergent world order
No full textThe Interaction Between Culture, Health and Psychological Well-Being: Data Mining from the Italian Culture and Well-Being Project
No full textApplication of artificial neural networks to link genetic and environmental factors to DNA methylation in colorectal cancer
No full textAIMS: We applied artificial neural networks (ANNs) to understand the connections among polymorphisms of genes involved in folate metabolism, clinico-pathological features and promoter methylation levels of MLH1, APC, CDKN2A(INK4A), MGMT and RASSF1A in 83 sporadic colorectal cancer (CRC) tissues, and to link dietary and lifestyle factors with gene promoter methylation.
MATERIALS & METHODS:
Promoter methylation was assessed by means of methylation-sensitive high-resolution melting and genotyping by PCR-RFLP technique. Data were analyzed with the Auto Contractive Map, a special kind of ANN able to define the strength of the association of each variable with all the others and to visually show the map of the main connections.
RESULTS:
We observed a strong connection between the low methylation levels of the five CRC genes and the MTR 2756AA genotype. Several other connections were revealed, including those between dietary and lifestyle factors and the methylation levels of CRC genes.
CONCLUSION:
ANNs revealed the complexity of the interconnections among factors linked to DNA methylation in CRC
The contribution of Artificial Adaptive System to limit the influence of systematic errors in the definition of the kinematic behavior of an extremely-slow landslide
No full textThis paper describes the application of some new mathematical algorithms, developed at Semeion
Research Center and based on Artificial Adaptive System (AAS), to the redundant measurements of
displacement of an extremely-slow landslide that may be affected by some systematic errors. The main aim
is to understand if AAS may overcome their influence in the definition of the landslide kinematic behavior
thus being able to use the measurements even though they differ by systematic errors. This would be a
particularly good result for the monitoring of extremely-slow landslides that move at displacement rates
less than 16 mm/year and can be recognized only with instrumentation, usually of geodetic type for the
ground surface and inclinometers for the subsurface. In the short time, displacements are so small that
they may include systematic errors of the same order of magnitude that can neither be identified nor
reduced. For the monitoring of extremely-slow landslides it is therefore recommended to use redundant
measurement systems and check the reliability of data by comparing the displacements. This paper shows
how the use of the Artificial Adaptive System may get the information on the landslide kinematic even
when there is no agreement between displacements measured with the different techniques. The
validation of these results was made by comparing them with the well-known data field and a good
agreement was found
Multidimensional similarities at a global scale: an approach to mapping open society orientations
No full textThis paper analyzes the global geography of open society orientations in the sense of Karl Popper’s notion of open society, by means of a database consisting of five common, public and widely used indicators such as UNDP’s Human Development Index, the World Economic Forum’s Global Competitiveness Index, the Heritage Foundation’s Economic Freedom Index, Reporters Sans Frontie`res’ Press Freedom Index, and Trans- parency International’s Corruption Perception Index. We carry out a cluster analysis based on the Self-Organizing Map (SOM) technique, and find that the geography of open society orientation organizes globally into four main clusters with distinctive socio-economic characteristics. We discuss the implications of the clusterization and find that it provides interesting insight also as to the post-2008 response of countries to the global financial crisis
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