Multimedia blog volume prediction using adaptive neuro fuzzy inference system and evolutionary algorithms

摘要: Due to wide streaming multimedia blogs over the social networks, volume prediction has become indispensable for analysis of blog popularity. As a rule base driven method, Adaptive Neuro Fuzzy Inference System gained popularity in various tasks its efficiency and ease implementation. In this paper, two modified models have been proposed by tuning premise consequent parameters using (a) Particle swarm optimization (b) Genetic algorithms, improve predictive performance. Swarm Optimization helps reducing training cross validation error model whereas Algorithms optimize minimum clustering radius which aids formation base. Comparative method performed against Neural Networks, Support Vector Machines basic Neuro-Fuzzy System. Both variants outperformed state-of-art techniques algorithms when tested on UCI public dataset real Twitter, making it well suitable forecasting.