2019
Titouna C, TITOUNA F, Ari AAA.
Outlier detection algorithm based on mahalanobis distance for wireless sensor networks. 2019 International Conference on Computer Communication and Informatics (ICCCI). 2019 :1-6.
Titouna C, TITOUNA F, Ari AAA.
Outlier detection algorithm based on mahalanobis distance for wireless sensor networks. 2019 International Conference on Computer Communication and Informatics (ICCCI). 2019 :1-6.
Titouna C, TITOUNA F, Ari AAA.
Outlier detection algorithm based on mahalanobis distance for wireless sensor networks. 2019 International Conference on Computer Communication and Informatics (ICCCI). 2019 :1-6.
Haoues M, Dahane M, Mouss NK.
Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints. Journal of Intelligent ManufacturingJournal of Intelligent Manufacturing. 2019;30 :701-725.
Haoues M, Dahane M, Mouss NK.
Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints. Journal of Intelligent ManufacturingJournal of Intelligent Manufacturing. 2019;30 :701-725.
Haoues M, Dahane M, Mouss NK.
Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints. Journal of Intelligent ManufacturingJournal of Intelligent Manufacturing. 2019;30 :701-725.
Mezzoudj S, Behloul A, Seghir R, Saadna Y.
A parallel content-based image retrieval system using spark and tachyon frameworks. Journal of King Saud University - Computer and Information SciencesJournal of King Saud University - Computer and Information Sciences. 2019.
AbstractWith the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps. (i) image indexation step, in which we use MapReduce distributed model on Spark in order to speed up the indexation process. We also use a memory-centric distributed storage system, called Tachyon, to enhance the write operation (ii) image retrieving step which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on MapReduce model implemented under Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.
Mezzoudj S, Behloul A, Seghir R, Saadna Y.
A parallel content-based image retrieval system using spark and tachyon frameworks. Journal of King Saud University - Computer and Information SciencesJournal of King Saud University - Computer and Information Sciences. 2019.
AbstractWith the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps. (i) image indexation step, in which we use MapReduce distributed model on Spark in order to speed up the indexation process. We also use a memory-centric distributed storage system, called Tachyon, to enhance the write operation (ii) image retrieving step which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on MapReduce model implemented under Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.
Mezzoudj S, Behloul A, Seghir R, Saadna Y.
A parallel content-based image retrieval system using spark and tachyon frameworks. Journal of King Saud University - Computer and Information SciencesJournal of King Saud University - Computer and Information Sciences. 2019.
AbstractWith the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps. (i) image indexation step, in which we use MapReduce distributed model on Spark in order to speed up the indexation process. We also use a memory-centric distributed storage system, called Tachyon, to enhance the write operation (ii) image retrieving step which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on MapReduce model implemented under Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.
Mezzoudj S, Behloul A, Seghir R, Saadna Y.
A parallel content-based image retrieval system using spark and tachyon frameworks. Journal of King Saud University - Computer and Information SciencesJournal of King Saud University - Computer and Information Sciences. 2019.
AbstractWith the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps. (i) image indexation step, in which we use MapReduce distributed model on Spark in order to speed up the indexation process. We also use a memory-centric distributed storage system, called Tachyon, to enhance the write operation (ii) image retrieving step which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on MapReduce model implemented under Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.
HADEF H, M. Djebabra, M. Mouda and D. Belala.
PCA-I and AHP Methods: Unavoidable Arguments in Accident Scenario Classification. J Fail. Anal. and PrevenJ Fail. Anal. and Preven. 2019;2019 :496–503.
AbstractRisk mapping is the foundation of the risk prevention strategy. It allows for the understanding of all factors that may affect the activities. It is a collective decision based on negotiation between the actors. Argument-based negotiation accelerates the negotiation process and converges it toward a final and common decision. It is in this context that this paper aims to illustrate the contribution of the improved PCA and AHP tools, considered as arguments, to the classification of major accident risks.
HADEF H, M. Djebabra, M. Mouda and D. Belala.
PCA-I and AHP Methods: Unavoidable Arguments in Accident Scenario Classification. J Fail. Anal. and PrevenJ Fail. Anal. and Preven. 2019;2019 :496–503.
AbstractRisk mapping is the foundation of the risk prevention strategy. It allows for the understanding of all factors that may affect the activities. It is a collective decision based on negotiation between the actors. Argument-based negotiation accelerates the negotiation process and converges it toward a final and common decision. It is in this context that this paper aims to illustrate the contribution of the improved PCA and AHP tools, considered as arguments, to the classification of major accident risks.