}

{Lean philosophy aims at lose identification and elimination, continuously seeking for perfection in order to manage resources and processes in a better way. To reach this goal, the enterprise must use a proactive information system, able to provide accurate information to take the right decisions in the least delay. The information quality depends mainly on the way it is collected and broadcasted. The information system must also exhibit agility and flexibility, enhancing the enterprise flexibility and adaptability to face environment evolution. In this context, we propose to use data mining featuresto improve real time information processing in order to enhance enterprise resources and processes management. Such an approach seems to be efficient, mostly regarding defects detection and its related corrective actions. In this paper we define the adaptation of the Service Oriented Architecture in order to integrate the industrial constraints, identify the components related to manufacturing system. Then, we highlight data mining benefits via the definition of registries and metadata in order to contribute in processes and services execution enhancement},

{In this paper, an automatic text–independent Arabic word speaker identification system is presented using Fuzzy Wavelet Neural Network terminology (FWNN). The approach is combining wavelet theory to fuzzy logic and neural network which lead to fabricate a Fuzzy Wavenet. Position and dilation of the fuzzy wavenets are fixed and the weights are optimized according to learning algorithm in the network. The feature extraction for real Arabic word signals through Discrete Wavelet Transform (DWT) model is used. The proposed terminology here is training process for some words of all speakers done in FWNN learning phase then test for the other sample speech signals for speakers have been used in FWNN classification phase. Success theory of fuzzy wavenets has been generalized by extension to biorthogonal wavelets which lead to identification system development. Results showing the effectiveness of the proposed system presented in this paper},

{Currently, with the Internet’s massive use, electronics information growth exponentially; therefore human’s capacities to exploit them is decreased; then it is obvious that with these advances in technologies of information, intelligent systems are necessary to automatically and intelligently exploit these information. Agent technology
and Artificial intelligence, in particular Machine Learning, constitute powerful paradigms and tools for the development of such systems. Generally, existing works on Learning Agent-based systems are relative to specific domains; in this paper we propose suitable machine Learning methods for Agent-based systems independently of any domain application. Then we argue ours proposals by presenting the learning module of an Intelligent Agent for E-mails handling. This system is an instantiation of a general model, which we have built for Personal Learning Agent. Our Intelligent system is based on an inductive learning method, named Induction Decision Tree. This method is very suitable for learning from knowledge represented by attributes/values, such us extracted knowledge from E-mails},

{Former studies developed in laboratory validated an original method allowing the health monitoring in real time of an embedded composite structure using piezoelectric sensors. One of the problems preventing the industrial application of such a technique is the lack of an effective method able to detect and distinguish among the types of damage occurring during the service. With this thought behind, the mechanical answer, the evolution of the damage and the activity of Acoustic Emission (AE) during the tensile test of a composite with glass/epoxy matrix were studied. In this work, our contribution consists in coupling a conventional analysis of the Acoustic Emission data with a multivariable statistical analysis, in order to identify the damage mechanisms within a laminated glass/epoxy structure. In this way, paper contributes to the development of the Acoustic emission technique for the in situ monitoring of the evolution of defects in composites with polymeric matrix},