Oriental Journal of Computer Science and Technology

Asmaa Ourdighi ¹, Abdelkader Benyettou ¹

Affiliations
1 Department of Computer Science, University of Sciences and Technology, Mohamed Boudiaf of Oran, SIMPA Laboratory, Oran, 31000, DZ

Abstract

The artificial neural networks have been generally based on rate coding in the earliest stage of computational neuroscience development. What if all the idea of computational paradigm involving the propagation of continuous data affected straight the enhancing of neural network performance and the main objective becomes how to encode the data for modeling biological behavior. The spiking neural networks (SNN) were founded around this concept where not only the network topology, neuron model and plasticity rule should be defined, but also used the timing of the spike to encode and compute information. In this paper, we proposed an automatic lipreading system for spoken digits based on spike response model (SRM). We experimentally demonstrated the impact of the coding strategy to improve the results by comparing two strategies: Spike time coding and population coding by using Gaussian receptive fields (GRF); which achieved 75% and 83.33% accuracy, respectively, on Tulips1.0 dataset.

Keywords

Spiking Neural Network (SNN), Spike Response Model (SRM), Automatic Lipreading, Spike Time Coding, Population Coding, Gaussian Receptive Fields (GRF).

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Noria Benyettou ¹, Abdelkader Benyettou ¹, Vincent Rodin ², Souad Yahia Berrouiguet ³

Affiliations
1 Laboratoire SIMPA, Universitedes Sciences et Technologie d'Oram, Mohammed Boudiaf, BP 1505, Oran, DZ
2 European University of Brittany, UBO, EA3883, LISyC, CS 93837, 29238 Brest Cedx3, FR
3 National Institute of Telecommunications and Information Technology and Communication, INTTIC, Senia Street, Elm'naouar, Oran Algéria, DZ

Abstract

Network intrusion detection Systems are designed to protect computer networks by observing frames and notifying the operators when a possible attack happened. But with the development of network and the information exchange, networks became increasingly vulnerable faced at the new forms of threats. It is necessary to improve the performance of an intrusion detection system. Inspired by immune biological system behavior and the performances of the multi-agent systems, we present in this article a new model (MAISID) of multi-agent system immune for intrusion detection. MAISID is a system that performs frames analyses by a group of immune agents’ collaboration. These agents are distributed on the network to achieve simultaneous treatments, and are auto-adaptable to the evolution of the environment and have also the property of communication and coordination in order to ensure a good detection of intrusions in a distributed network. In this approach, the MAISID model is installed in each host of the network and sub-network for an extensive monitoring and a simultaneous analysis of the frames.

Keywords

Intrusion Detection System, Artificial Immune System, Multi-Agents System, Network Security.

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Sarah Benziane ¹, Abdelkader Benyettou ²

Affiliations
1 University of Oran Es Senia, Abdelkader Benyettou, DZ
2 University of Science and Technology of Oran, DZ

Abstract

After fingerprints or iris digital, biometric identification of an individual can now be done through the veins of the hand. This technique uses a “palm vein scanner”: it is an optical sensor capable of “photographing” the palm vein using the “near infrared rays.” This technique uses a “palm vein scanner”.Biometric authenticationbyrecognition offinger veinorpalmis especially developed by Hitachi and Fujitsuin Japansince 2005. Compared to thetechnologyusingfingerprint recognition, it offers the advantage of beingsafer andit does not involveany physical contactwith the sensor.Vein recognitionis performed byremote effectoptically. This paper presents a survey of the biometric systems technology based hand vein.We first review the component modules including the algorithms they employ. Finally, a summary of the accuracy results reported in the literature is also provided.

Keywords

Hand Vein, Palm, Dorsal, Finger, and Biometric System.

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