Pipelined model for classification of the processed tweets

The The extraction and processing of the real-time tweets is one of the challenging tasks. The processed tweets are classified using unstructured models. These showed no. of neutral tweets after the classification the tweets. Therefore, here pipelined unstructured model is developed the for the classification of tweets and explored to reduce the number of neutral tweets. It showed more reduction in the number of neutral tweets as compared to other unstructured model such as the SWNC model results.

PLANNING CLONAL SELECTION PROGRAMMES FOR PERENNIAL CROPS

SUMMARYA formula is developed for calculating the expected gain when a first-order autoregressive repeated measures model for the plot errors is assumed. Using examples from our earlier papers, the similarities of the conclusions about the best selection programme from using simulation of an unstructured model and from using the autoregressive formula for expected gain are presented. The autoregressive formula is then used to derive optimal programmes when the number of plots or plot years is fixed for a range of values for the variance of the interactions of clone effects with years relative to the variance of the clone effects and for the variances and covariances between years of the plot residuals. In general, there are advantages in studying many clones at low replication rather than fewer clones at high replication.