The development of a Time series Forecasting System is major concern for Artificial Intelligence researchers. Commonly, existing systems only assess temporal features and analyze the behavior data over time, thus, resulting in uncertain forecasting accuracy. Although many were proposed literature; they have not yet answered attending question. Hence, to overcome this problematic, we propose an ...

Existing image layer representations methods are very feed-forward, and then not able to deal with small ambiguities. A probabilistic model is proposed, and it learns and deduces each layer in that hierarchy together. Therefore, we consider a recursive probabilistic decomposition process, and derive a new yielded method based on recursive Latent Dirichlet Allocation. We show 2 significant prope...

We present two methods for expressing computations based on recurrence relations and discuss their relative merits. One method, the structural blanks approach, is built on top of traditional programming languages like Fortran or Pascal. It aims at program reuse and bases a certain architecture of software packages. The other method, the constructive recursive approach, is based on recursive rel...