Investigation on trajectory pattern recognition for intelligent cognitive network environment using early destination prediction algorithm

Abstract

newline A distinct graphical behaviour forecast utilising trajectory tracking for newlineearly destination prediction. The proposed method primarily depends on client newlineconfiguration to identify the target contenders at the outset of an expedition, newlinewith guidelines additionally employed to restrict the candidates for the newlineobjective. A multitude of options is being evaluated for documenting the newlinevarious trajectories humanity has undertaken. This study explores two key newlinephases of data mining, focusing on trajectory pattern mining and cognitive newlinenetwork analysis. In the first phase, the Enhanced Bit Mask (EBM) search newlinetechnique is introduced to improve the performance of conventional Bit Mask newlinealgorithms in mining large-scale trajectory databases. The EBM technique newlineincreases the density of bit vectors, enhancing both time and space complexity newlinefor mining frequent itemsets and trajectory patterns. A detailed comparison newlinebetween EBM and the UP-Growth+ algorithm reveals that while UP-Growth+ newlinemaintains higher accuracy (90% 100%) across varied user loads, EBM offers newlinemore consistent performance in terms of processing time, making it suitable newlinefor applications that prioritize scalability over accuracy.