Aboubekeur Hamdi-Cherif

Vector space model for Arabic information retrieval — application to “Hadith” indexing

The Arabic language is one of the most important languages because it is the sacred and liturgical language of Islam, one of the influential monotheistic religions of our times. In the post-9/11 aftermath, Islam suddenly dominated western actuality for the remaining years of the present decade. Al-Qurpsilaan - The Reading par Excellence - and ldquoHadithrdquo - Saying - represent the two fundamental scriptural sources of Islamic Legislation. Specifically, ldquoHadithrdquo, or Prophetic Traditions, are sayings and doings of the Prophet of Islam (Peace and Blessings be upon Him). Researchers need automatic search tools within large ldquoHadithrdquo databasesto access one of the original sources of Islam. For this purpose, we describe the development of AuthenTique, an updated automatic text mining search tool, based on the vector space model (VSM). The aim is to allow the provision of a list of ldquoHadithsrdquo classified according to their degrees of similarity based on a given userpsilas query.

State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.

Evolutionary multiobjective optimization for medical classification

We propose a computational environment based on evolutionary algorithm for medical classification. We use evolutionary multiobjective optimization (EMO) to solve a general medical minimization problem. As an example, we simultaneously minimize three objectives, namely the number of genes responsible for cancer classification while reducing the number of misclassifications in both testing and learning data sets for real patients. Results quality is reported against three genetic operators namely selection, crossover and mutation, each of which offering three different methods. Our implementation gives comparable results to more sophisticated methods, such as NGSAII-like ones, with far less computational efforts.

Applying topic segmentation algorithms on arabic language

The need of having a topic segmentation system for Arabic text is to improve the functionalities of Arabic Information Retrieval (AIR). Topic segmentation of texts has been used to improve the accuracy of the subsequent processes such as question answering and information retrieval. In this paper, we present the assessment of two algorithms for Arabic text segmentation which are TextTilling and C99. We evaluate the performance of these algorithms using the classical Recall/Precision metrics and the Reader Judgment method.

Big data fuzzy management methods in gene regulatory networks inference: a review

To address one of the most challenging ecosystems issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence on the life and the development of living organisms, and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. The ecosystems impacted by GRN inference span various levels from cell to society -- globally.

Grammatical Inference System for Finite State Automata - GIFSA

As a first step toward the development of a general grammatical inference (GI) software environment, GIFSA represents an integrated system for inferring finite state automata (FSA). Using the unified modeling language (UML), GIFSA offers a continuously upgradable software system initially implementing two AI-based algorithms, namely the tabu search method and the minimum description length (MDL) principle. For portability reasons, Java™ programming language is used for development, enhanced by a friendly graphical user interface (GUI).

Apprentissage inductif de grammaires : Le système GASRIA

In this article, we show how the issue of grammar acquisition can be approached from the standpoint of learning heuristic rules of the language under consideration. We describe our GASRIA system consisting of: - An inductive learning module for grammar inference based on a novel method capable of parsing sentences not parsable by existing methods, called Partial Parsing Algorithm (PPA), - a first-order logic environment, - a knowledge base (KB) consisting of a rule base using variables. The result is a reasoning syntactic analyzer capable of inductive learning. In this article, we will essentially stress the learning side of our solution.