Angelo Ciaramella

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.

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.

2008 Special Issue: Interactive data analysis and clustering of genomic data

In this work a new clustering approach is used to explore a well- known dataset [Whitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I., Ball, C. A., Alexander, K. E., et al. (2002). Molecular biology of the cell: Vol. 13. Identification of genes periodically expressed in the human cell cycle and their expression in tumors (pp. 1977-2000)] of time dependent gene expression profiles in human cell cycle. The approach followed by us is realized with a multi-step procedure: after preprocessing, parameters are chosen by using data sub sampling and stability measures; for any used model, several different clustering solutions are obtained by random initialization and are selected basing on a similarity measure and a figure of merit; finally the selected solutions are tuned by evaluating a reliability measure. Three different models for clustering, K-means, Self-organizing Maps and Probabilistic Principal Surfaces are compared. Comparative analysis is carried out by considering: similarity between best solutions obtained through the three methods, absolute distortion value and validation through the use of Gene Ontology (GO) annotations. The GO annotations are used to give significance to the obtained clusters and to compare the results with those obtained in the work cited above.

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.

Probabilistic principal surfaces for yeast gene microarray data mining

The recent technological advances are producing huge data sets in almost all fields of scientific research, from astronomy to genetics. Although each research field often requires ad-hoc, fine tuned, procedures to properly exploit all the available information inherently present in the data, there is an urgent need for a new generation of general computational theories and tools capable to boost most human activities of data analysis. Here, we propose probabilistic principal surfaces (PPS) as an effective high-D data visualization and clustering tool for data mining applications, emphasizing its flexibility and generality of use in data-rich field. In order to better illustrate the potentialities of the method, we also provide a real world case-study by discussing the use of PPS for the analysis of yeast gene expression levels from microarray chips.

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.

Independent Data Model Selection for Ensemble Dispersion Forecasting

This work aims at introducing an approach to analyze the independence between different data model in a multi-model ensemble context. The models belong to operational long-range transport and dispersion models, but they are also used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides in the atmosphere. In order to compare models, an approach based on the hierarchical agglomeration of distributions of predicted radionuclide concentrations is proposed. We use two different similarity measures: Negentropy information and Kullback-Leibler divergence. These approaches are used to analyze the data obtained during the ETEX-1 exercise, and we show how to exploit these approaches to select subsets of independent models whose performance is comparable to those from the whole ensemble.

Clustering, Assessment and Validation: an application to gene expression data

In this work a multi-step approach for clustering assessment, visualization and data validation is introduced. Three main approaches for data clustering are used and compared: K-means, self organizing maps and probabilistic principal surfaces. A model explorer approach with different similarity measures is used to obtain the best parameters of the methods. The approach is used to identify genes periodically expressed in tumors related to the human cell cycle. Finally, clusters are validated by using GO term information.

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

Compressive sampling and adaptive dictionary learning for the packet loss recovery in audio multimedia streaming

In this work, a scheme based on a compressive sampling technique and a fast dictionary learning approach for reconstructing audio content in multimedia streaming is introduced. Audio streaming data are encapsulated in different packets by means of an interleaving technique. The compressive sampling technique is used to reconstruct audio information in case of lost packets, with a sparsifying basis provided by a greedy adaptive dictionary learning algorithm. In order to assess the performance of the methodology, several experiments on speech and musical audio signals are presented.