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ZOUNEME, Boris Stephane,ADEPO, Joel,DIEDIE, Herve Gokou,OUMTANAGA, Souleymane International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.3
In recent decades, the heterogeneous and dynamic behavior of Internet traffic has placed new demands on the adaptive resource allocation of the optical network infrastructure. However, the advent of multifiber elastic optical networks has led to a higher degree of spectrum fragmentation than conventional flexible grid networks due to the dynamic and random establishment and removal of optical connections. In this paper, we propose heuristic routing and dynamic slot allocation algorithms to minimize spectrum fragmentation and reduce the probability of blocking future connection requests by considering the power consumption in elastic multifiber elastic optical networks.
Courses Recommendation Algorithm Based On Performance Prediction In E-Learning
Koffi, Dagou Dangui Augustin Sylvain Legrand,Ouattara, Nouho,Mambe, Digrais Moise,Oumtanaga, Souleymane,ADJE, Assohoun International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.2
The effectiveness of recommendation systems depends on the performance of the algorithms with which these systems are designed. The quality of the algorithms themselves depends on the quality of the strategies with which they were designed. These strategies differ from author to author. Thus, designing a good recommendation system means implementing the good strategies. It's in this context that several research works have been proposed on various strategies applied to algorithms to meet the needs of recommendations. Researchers are trying indefinitely to address this objective of seeking the qualities of recommendation algorithms. In this paper, we propose a new algorithm for recommending learning items. Learner performance predictions and collaborative recommendation methods are used as strategies for this algorithm. The proposed performance prediction model is based on convolutional neural networks (CNN). The results of the performance predictions are used by the proposed recommendation algorithm. The results of the predictions obtained show the efficiency of Deep Learning compared to the k-nearest neighbor (k-NN) algorithm. The proposed recommendation algorithm improves the recommendations of the learners' learning items. This algorithm also has the particularity of dissuading learning items in the learner's profile that are deemed inadequate for his or her training.