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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Electrical Engineering and Computer Science
Mohamed Fakir
Sultan Moulay Slimane University
Computer Science & IT Engineering

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 1 
Implementation of Business Intelligence For Sales Management Bouzekri Moustaid; Mohamed Fakir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 1: March 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.148 KB) | DOI: 10.11591/ijai.v5.i1.pp22-34

Abstract

Today's company operates in a socio-economic environment increasingly demanding. In such a context, it is obliged to adopt a competitive approach by exploiting at best the information that it possesses for developing appropriate action plans and taking effective decisions. The decision support systems provide to the enterprise the tools that help it for decision-making based on techniques and methodologies coming from domain of applied mathematics such as optimization, statistics and theory of the decision. The decision support systems are composed of various components such as data warehouses, ETL tools and reporting and analysis tools.
Fingerprint Classification Using Fuzzy-neural Network and Other Methods Idriss Tazight; Mohamed Fakir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.66 KB) | DOI: 10.11591/ijai.v3.i3.pp129-135

Abstract

The fingerprints are unique to each individual; they can be used as a means to distinguish one individual from another.Therefore they are used to identify a person. Fingerprint Classification is done to associate a given fingerprint to one of the existing classes, such as left loop, right loop, arch, tented arch and whorl. Classifying fingerprint images is a very complex pattern recognition problem, due to properties of intra-class diversitiesand inter-class similarities. Its objective is to reduce the responsetime and reducing the search space in an automatic identificationsystem fingerprint (AIS), in classifying fingerprints. In these papers we present a system of fingerprint classificationbased on singular characteristics for extracting feature vectorsand neural networks and fuzzy neural networks, SVM and Knearest neighbour for classifying.
Geodesic Distance on Riemannian Manifold using Jacobi Iterations in 3D Face Recognition System Rachid Ahdid; Said Safi; Mohamed Fakir; Bouzid Manaut
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 6, No 1: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (795.922 KB) | DOI: 10.11591/ijict.v6i1.pp10-19

Abstract

In this paper, we present an automatic application of 3D face recognition system using geodesic distance in Riemannian geometry. We consider, in this approach, the three dimensional face images as residing in Riemannian manifold and we compute the geodesic distance using the Jacobi iterations as a solution of the Eikonal equation. The problem of solving the Eikonal equation, unstructured simplified meshes of 3D face surface, such as tetrahedral and triangles are important for accurately modeling material interfaces and curved domains, which are approximations to curved surfaces in R3. In the classifying steps, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test this method and evaluate its performance, a simulation series of experiments were performed on 3D Shape REtrieval Contest 2008 database (SHREC2008).
Recognition of a Face in a Mixed Document Lhoussaine Bouhou; Rachid El Ayachi; Mohamed Fakir; Mohamed Oukessou
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp301-312

Abstract

Face recognition is the field of great interest in the domaine of research for several applications such as biometry identification, surveillance, and human-machine interaction…This paper exposes a system of face recognition. This system exploits an image document text embedding a color human face image. Initially, the system, in its phase of extraction, exploitis the horizontal and vertical histogram of the document, detects the image which contains the human face. The second task of the system consists of detecting the included face in other to determine, with the help of invariants moments, the characteristics of the face. The third and last task of the system is to determine, via the same invariants moments, the characteristics of each face stored in a database in order to compare them by means of a classification tool (Neural Networks and K nearest neighbors) with the one determined in the second task for the purpose of taking the decision of identification in that database, of the most similar face to the one detected in the input image.
Handwritten tifinagh character recognition using simple geometric shapes and graphs Youssef Ouadid; Abderrahmane Elbalaoui; Mehdi Boutaounte; Mohamed Fakir; Brahim Minaoui
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp598-605

Abstract

In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.
Co-Authors Abderrahmane Elbalaoui Bouzekri Moustaid Bouzid Manaut Brahim Minaoui Idriss Tazight Lhoussaine Bouhou Mehdi Boutaounte Mohamed Oukessou Rachid Ahdid Rachid El Ayachi Said Safi Youssef Ouadid