Antonino Staiano

A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference

A fuzzy system for environmental risk assessment of genetically modified plants is described.The fuzzy system is based on Mamdani inference.The Fuzzy System Risk Assessments have been validated on real world trial case studies.The system decisions have been considered coherent and consistent by human experts. Environmental risk assessment (ERA) of the deliberate release of genetically modified plants (GMPs) is currently performed by human experts on the basis of own personal experience and knowledge. In this paper we describe a fuzzy decision system (FDS) for the ERA of GMPs, based on Mamdani fuzzy inference. The risk assessment in the FDS is obtained by using a fuzzy inference system (FIS), performed using jFuzzyLogic library. The FDS permits obtaining an evaluation process for the identification of potential impacts that can achieve one or more receptors through a set of migration paths. The decisions derived by FDS have been validated on real world cases by the human experts that are in charge of ERA. They have confirmed the reliability and correctness of the fuzzy system decisions.

Mining the SDSS archive. I. Photometric redshifts in the nearby universe

In this paper we present a supervised neural network approach to the determination of photometric redshifts. The method, even though of general validity, was fine tuned to match the characteristics of the Sloan Digital Sky Survey (SDSS) and as base of ’a priori’ knowledge, it exploits the rich wealth of spectroscopic redshifts provided by this unique survey. In order to train, validate and test the networks, we used two galaxy samples drawn from the SDSS spectroscopic dataset, namely: the general galaxy sample (GG) and the luminous red galaxies subsample (LRG). Due to the uneven distribution of measured redshifts in the SDSS spectroscopic subsample, the method consists of a two steps approach. In the first step, objects are classified in nearby (z < 0.25) and distant (0.25 < z < 0.50), with an accuracy estimated in 97.52%. In the second step two different networks are separately trained on objects belonging to the two redshift ranges. Using a standard Multi Layer Perceptron operated in a Bayesian framework, the optimal architectures were found to require 1 hidden layer of 24 (24) and 24 (25) neurons for the GG (LRG) sample. The presence of systematic deviations was then corrected by interpolating the resulting redshifts. The final results on the GG dataset give a robust σz ≃ 0.0208 over the redshift range [0.01, 0.48] and σz ≃ 0.0197 and σz ≃ 0.0238 for the nearby and distant Department of Physical Sciences, University of Napoli Federico II, via Cinthia 9, 80126 Napoli, ITALY INAF-Italian National Institute of Astrophysics, via del Parco Mellini, Rome, I INFN Napoli Unit, Dept. of Physical Sciences, via Cinthia 9, 80126, Napoli, ITALY Department of Mathematics and Applications, University of Salerno, Fisciano, ITALY Department of Applied Science, University of Napoli ”Parthenope”, via A. De Gasperi 5, 80133 Napoli, ITALY Institute of Astronomy, University of Cambridge, Madingley Rd, Cambridge CB4 0HA, UK

Intrinsic dimension estimation

The paper reviews state-of-the-art of the methods of Intrinsic Dimension (ID) Estimation.The paper defines the properties that an ideal ID estimator should have.The paper reviews, under the above mentioned framework, the major ID estimation methods underlining their advances and the open problems. Dimensionality reduction methods are preprocessing techniques used for coping with high dimensionality. They have the aim of projecting the original data set of dimensionality N, without information loss, onto a lower M-dimensional submanifold. Since the value of M is unknown, techniques that allow knowing in advance the value of M, called intrinsic dimension (ID), are quite useful. The aim of the paper is to review state-of-the-art of the methods of ID estimation, underlining the recent advances and the open problems.

Clustering and visualization approaches for human cell cycle gene expression data analysis

In this work a comprehensive multi-step machine learning data mining and data visualization framework is introduced. The different steps of the approach are: preprocessing, clustering, and visualization. A preprocessing based on a Robust Principal Component Analysis Neural Network for feature extraction of unevenly sampled data is used. Then a Probabilistic Principal Surfaces approach combined with an agglomerative procedure based on Fishers and Negentropy information is applied for clustering and labeling purposes. Furthermore, a Multi-Dimensional Scaling approach for a 2-dimensional data visualization of the clustered and labeled data is used. The method, which provides a user-friendly visualization interface in both 2 and 3 dimensions, can work on noisy data with missing points, and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Analysis and identification of genes periodically expressed in a human cancer cell line (HeLa) using cDNA microarrays is carried out as test case.

Machine learning and soft computing for ICT security: an overview of current trends

In the last years, people have been seeing the pervasive use of computer, communication technology and Internet, e.g., e-mail, online shopping, banking, gaming, Internet telephony, streaming. Unfortunately, the reliability of the Internet and its services, and in general Information and Communication Technology (ICT) devices, is undermined by insecurity issues. On the other hand, machine learning and soft computing techniques have been widely applied to disparate fields, becoming, in several cases, the leading technology. The aim of the work is to investigate the trends of the machine learning (ML) and soft computing (SC) methodologies for ICT security. In particular, it overviews ML and SC applications for three hot topics in ICT security: password-based schemes for access control, intrusion detection and spam filtering.

Investigation of Single Nucleotide Polymorphisms Associated to Familial Combined Hyperlipidemia with Random Forests

Single nucleotide polymorphisms (SNPs) are the foremost part of many genome association studies. Selecting a subset of SNPs that is sufficiently informative but still small enough to reduce the genotyping overhead is an important step towards disease-gene association. In this work, a Random Forest (RF) approach to informative SNPs selection in Familial Combined Hyperlipidemia (FCH) is proposed. FCH is the most common form of familial hyperlipidemia. Affected patients have elevated levels of plasma triglycerides and/or total cholesterol and show increased risk of premature coronary heart disease. In order to identify susceptibility markers for FCH we perform the analysis of 21 SNPs in ten genes associated with high cardiovascular risk. RF appears to be a useful technique in identifying gene polymorphisms involved in FCH: the identified SNPs confirmed some variants in a couple of genes as genetic markers of FCH as proved in various studies in scientific literature and lead us to report for the first time a further gene association with FCH. This result could be promising and encourages to further investigate on the role of the identified gene in the development of FCH phenotype.

A note on some mathematical models on the effects of Bt-maize exposure

Some mathematical models for the estimation of the effects of Cry1Ab and Cry1F Bt-maize exposure in the biodiversity are examined. Novel results about these models are obtained and described in this note. The exact formula for the proportion of population that suffers mortality exposed either to Cry1Ab or Cry1AF pollen is derived. Moreover, regarding Cry1F pollen effects, the species sensitivity of Lepidoptera is discussed.

Fuzzy similarities in stars/galaxies classification

By basing on the concept of fuzzy similarity with respect to a continuous triangular norm built via the well known method of ordinal sums, we propose a modification of an our previous algorithm which improves the performance of the Stars/Galaxies classification in astronomical data mining. This algorithm is implemented in Matlab 6 and we make also use of Fuzzy c-Means clustering algorithm for constructing two prototypes with respect to which the fuzzy similarity is calculated. Keywords— Fuzzy Similarity, Neural Network

Statistical and Computational Methods for Genetic Diseases: An Overview

The identification of causes of genetic diseases has been carried out by several approaches with increasing complexity. Innovation of genetic methodologies leads to the production of large amounts of data that needs the support of statistical and computational methods to be correctly processed. The aim of the paper is to provide an overview of statistical and computational methods paying attention to methods for the sequence analysis and complex diseases.

Linear SVM-based recognition of elementary juggling movements using correlation dimension of Euler Angles of a single arm

Recognizing the human arm movements has several applications, and it can be performed in a number of ways through the use of one or more sensor devices that the technology offers. This paper aims to exploit the exercises performed by jugglers in order to recognize the arm movements on the basis of the only information on the arm orientation provided by the Euler Angles. The proposed recognizer has two modules, i.e., a feature extractor and a classifier. The former reconstructs the dynamics of the system and estimates three correlation dimensions, each associated with a given Euler Angle. The latter is formed by a Linear Support Vector Machine. Extensive experimentations show the effectiveness of the proposed approach.