This paper proposes a new application of neural networks in telecommunications network simulation. A high-level abstracted analytical model, based on intensive investigation of packet queuing behaviour, substantially speeds up the basic simulation. Comparing the results from the model against the behaviour of a testbed leads to some difference between the model results and the experimental validation, an expected result given the level of abstraction. A neural network is applied to learn the relation between the model parameters and the output difference, and neural network prediction is used to 'jine-tune' the model accordingly. Resultsindicate that the proposed hybrid method (using the neural network to tune the abstracted model) achieves fast simulation and also remains accurate. This approach is particularly useful in the area of large-scale network designing and planning, where concern is more about the overall performance of the network than the detailed structure of a network node.

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Interconnections and communication between people in  social media enhance the information dissemination. This is leveraged by the business firms by using the social media as advertising platform for their products, brands and services. As interactive multimedia is more captivating than other media forms, the short and creative advertising videos are recently used as effective marketing tool. This work proposes a framework that taps the features of the video advertisements along with social networking properties like network clusters and  network degree to determine the transmission and retransmission of video between primary and secondary level clusters. The degradation in retransmission is estimated using the connections and their influence computed as connection coefficient between the nodes. The proposed framework is validated on Facebook advertisement videos by assessing its efficacy in dissemination and implementation of the products. The detailed experimentation reveals that the proposed model is much effective in determining the dissemination and implementation of the video content with the average mean square error of 0.24. The work is robust and versatile, that it could be deployed to other social media networks by customising the estimation of transmission and retransmission rate.