Learning in the recurrent random neural network
Neural Computation
Stability of the Random Neural Network Model
Proceedings of the EURASIP Workshop 1990 on Neural Networks
A study of real-time packet video quality using random neural networks
IEEE Transactions on Circuits and Systems for Video Technology
Quality assessment of interactive voice applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
Multiclass G-Networks of Processor Sharing Queues with Resets
ASMTA '08 Proceedings of the 15th international conference on Analytical and Stochastic Modeling Techniques and Applications
Networks of symmetric multi-class queues with signals changing classes
ASMTA'10 Proceedings of the 17th international conference on Analytical and stochastic modeling techniques and applications
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
An initiative for a classified bibliography on G-networks
Performance Evaluation
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Quantifying the quality of a video or audio transmission over the Internet is usually a hard task, as based on the statistical processing of the evaluations made by a panel of humans (the corresponding and standardized area is called subjective testing). In this paper we describe a methodology called Pseudo-Subjective Quality Assessment (PSQA), based on Random Neural Networks, which is able to perform this task automatically, accurately and efficiently. RNN had been chosen here because of their good performances over other possibilities; this is discussed in the paper. Some new insights on PSQA’s use and performance are also given. In particular we discuss new results concerning PSQA–based dynamic quality control, and conversational quality assessment.