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In internet market, content providers (CPs) continue to play a primordial role in the process of accessing different types of data. Competition in this area is fierce; customers are looking for providers that offer them good content (credibility of content and quality of service) with a reasonable price. In this work, the authors analyze this competition between CPs and the economic influence of their strategies on the market. The authors formulate their problem as a non-cooperative game among multiple CPs for the same market. Through a detailed analysis, the researchers prove uniqueness of a pure Nash Equilibrium (NE). Furthermore, a fully distributed algorithm to converge on the NE point is presented. In order to quantify how efficient the NE point is, a detailed analysis of the Price of Anarchy (PoA) is adopted to ensure the performance of the system at equilibrium. Finally, an extensive numerical study is provided to describe the interactions between CPs and to point out the importance of quality of service (QoS) and credibility of content in the market.

Most of the reported works in the field of character recognition systems achieve modest results by using a single method for calculating the parameters of the character image and a single approach in the classification phase of the system. So, in order to improve the recognition rate, this document proposes an automatic system to recognize isolated printed Tifinagh characters by using a fusion of some classifiers and a combination of some features extraction methods. The Legendre moments, Zernike moments, Hu moments, Walsh transform, GIST and texture are used as descriptors in the features extraction phase due to their invariance to translation, rotation and scaling changes. In the classification phase, the neural network, the Bayesian network, the multiclass SVM (Support Vector Machine) and the nearest neighbour classifiers are combined together. The experimental results of each single features extraction method with each single classification method are compared with our approach to show its robustness. A recognition rate of 100 % is achieved by using some combined descriptors and classifiers.

Abstract-In order to improve the recognition rate, this document proposes an automatic system to recognize isolated printed Tifinagh characters by using a fusion of 3 classifiers and a combination of some features extraction methods. The Legendre moments, Zernike moments and Hu moments are used as descriptors in the features extraction phase due to their invariance to translation, rotation and scaling changes. In the classification phase, the neural network, the multiclass SVM (Support Vector Machine) and the nearest neighbour classifiers are combined together. The experimental results of each single features extraction method and each single classification method are compared with our approach to show its robustness.

In this article, the authors propose a new hybrid approach based on a continuous Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a neural network to refine the alignment results. This approach consists of three phases: (i) pre-alignment phase which allows to identify the formats of input ontologies, to adapt them and to transform them into Ontology Web Language (OWL) in order to solve the problem of heterogeneity of representation. (ii) alignment phase which combines syntactic and linguistic matching techniques and methods, based on the relevant attributes per different points of syntactic and structural technic. (iii) The post-alignment phase which optimizes the matching by a hybrid technique of continuous NSGA-II and networks of neurons. This approach is compared with the greatest systems per the Ontology Alignment Evaluation Initiative (OAEI) standard. The experimental results appear that the proposed approach is effective.

Ontology matching is a discipline that means two things

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