Search results for "A* algorithm"

Statin-induced autoimmune myositis: a proposal of an “experience-based” diagnostic algorithm from the analysis of 69 patients

2023

AbstractStatin-induced autoimmune myositis (SIAM) represents a rare clinical entity that can be triggered by prolonged statin treatment. Its pathogenetic substrate consists of an autoimmune-mediated mechanism, evidenced by the detection of antibodies directed against the 3-hydroxy-3-methylglutaryl-coenzyme A reductase (anti-HMGCR Ab), the target enzyme of statin therapies. To facilitate the diagnosis of nuanced SIAM clinical cases, the present study proposes an “experience-based” diagnostic algorithm for SIAM. We have analyzed the clinical data of 69 patients diagnosed with SIAM. Sixty-seven patients have been collected from the 55 available and complete case records regarding SIAM in the l…

Simulazione numerica tridimensionale del campi de moto indotti dal vento

2006

Nell’articolo viene proposto un modello numerico tridimensionale al volume finiti, che viene utilizzato per la simulazione di campi di moto indotti dal mento in corpi idrici con batimetria complessa. I risultati numerici sono confrontati con misure sperimentali. Al fine di verificare gli effetti sul calcolo del campi idrodinamici di alcume ipotesi simplificative, relative alla distribuzione verticale della pressione e degli sforzi di Reynolds, sono stati quindi condotti alcuni test numerici, dai quali si è evidenziato che l’ipotesi di distribuzione idrostatica delle pressioni, cui si fa ricorso nelle approssinazioni di tipo “quasi-3D”, può essere utilizzata per il carcolo del profili vertic…

Identification Of P.Leu167Del Apoe Gene Mutation By Next Generation Sequencing In A Large Hypercholesterolemic Family

2019

Computation of a few smallest eigenvalues of elliptic operators using fast elliptic solvers

2001

The computation of a few smallest eigenvalues of generalized algebraic eigenvalue problems is studied. The considered problems are obtained by discretizing self-adjoint second-order elliptic partial differential eigenvalue problems in two- or three-dimensional domains. The standard Lanczos algorithm with the complete orthogonalization is used to compute some eigenvalues of the inverted eigenvalue problem. Under suitable assumptions, the number of Lanczos iterations is shown to be independent of the problem size. The arising linear problems are solved using some standard fast elliptic solver. Numerical experiments demonstrate that the inverted problem is much easier to solve with the Lanczos…

On spline methods of approximation under L-fuzzy information

2011

This work is closely related to our previous papers on algorithms of approximation under L-fuzzy information. In the classical theory of approximation central algorithms were worked out on the basis of usual, that is crisp splines. We describe central methods for solution of linear problems with balanced L-fuzzy information and develop the concept of L-fuzzy splines.

Reduced complexity models in the identification of dynamical networks: Links with sparsification problems

2009

In many applicative scenarios it is important to derive information about the topology and the internal connections of more dynamical systems interacting together. Examples can be found in fields as diverse as Economics, Neuroscience and Biochemistry. The paper deals with the problem of deriving a descriptive model of a network, collecting the node outputs as time series with no use of a priori insight on the topology. We cast the problem as the optimization of a cost function operating a trade-off between accuracy and complexity in the final model. We address the problem of reducing the complexity by fixing a certain degree of sparsity, and trying to find the solution that “better” satisfi…

Sequential Mining Classification

2017

Sequential pattern mining is a data mining technique that aims to extract and analyze frequent subsequences from sequences of events or items with time constraint. Sequence data mining was introduced in 1995 with the well-known Apriori algorithm. The algorithm studied the transactions through time, in order to extract frequent patterns from the sequences of products related to a customer. Later, this technique became useful in many applications: DNA researches, medical diagnosis and prevention, telecommunications, etc. GSP, SPAM, SPADE, PrefixSPan and other advanced algorithms followed. View the evolution of data mining techniques based on sequential data, this paper discusses the multiple …

Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field

2018

Data Mining in general is the act of extracting interesting patterns and discovering non-trivial knowledge from a large amount of data. Medical data mining can be used to understand the events happened in the past, i.e. studying a patients vital signs to understand his complications and discover why he has died, or to predict the future by analyzing the events that had happened. In this chapter we are presenting an overview on studies that use data mining to predict heart failure and heart diseases classes. We will also focus on one of the trendiest data-mining field, namely the Sequential Mining, which is a very promising paradigm. Due to its important results in many fields, this chapter …

Overview on Sequential Mining Algorithms and Their Extensions

2018

The main purpose of data mining is to extract hidden, important and nontrivial information from a database. Sequential Pattern Mining is a data mining technique that aims to obtain and analyze frequent subsequences from sequences of events or items with or without time constraint. The importance of a sequence can be measured based on different factors such as the frequency of their occurrence, their length and also their profit. The pattern mining or the discovery of important and unexpected patterns and information was first introduced in 1990 with the well-known Apriori algorithm. Then, and after many studies on frequent pattern mining, a new approach appeared: Sequential Pattern Mining. …

Hop: Histogram of patterns for human action representation

2017

This paper presents a novel method for representing actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. This paper proposes to learn a codebook of frequent sequential patterns by means of an apriori-like algorithm, and to represent an action with a Bag-of-Frequent-Sequential-Patterns approach. Preliminary experiments of the proposed method have been conducted for action classification on skeletal data. The method achieves state-of-the-art accuracy value in cross-subject validation.