Majority of the hybrids observed (7 out of 10) have very large pollen size and long pollen spines. Pollen size ranges from large to very large and long to very long spine length. Among the characters observed, variation was noted in their pollen size and spine length. Results showed that all hybrids have pantoporate type of aperture, echinate type of sculpturing, and spheroidal shape. Pollen shape variation was determined through Elliptic Fourier Coefficient Analysis. One-way ANOVA was employed if there is significant difference among the pollen of the hybrids in terms of the said quantitative characters. The pollen shape, type of aperture and sculpturing were determined qualitatively. rosa-sinensis hybrids from the Institute of Plant Breeding of the University of the Philippines-Los Banos, specifically in terms of pollen aperture, size, shape, length of spine and sculpturing. This study focused on the determination of variations in pollen grain morphology of ten (10) selected H. Through time, such variations could change the frequencies of alleles in the gene pool and could possibly lead to microevolution of the species. Phenotypic variations can already be observed in various parts of H.rosa-sinensis because of hybridization hence, it is likely that modifications are occurring on microscopic structures such as the pollen. An example of which is the numerous hybrids of Hibiscus rosa-sinensis which are primarily used for aesthetic purposes because of their colourful flowers. Hybridization of both plants and animals has innumerably benefitted man. Morphological Variation In Pollen Grains Of Philippine Hibiscus Rosa-Sinensis HybridsÄivine Joy A. PBEM is a new approach as it works on discovering the precise web browsing behavior from uncertain keywords and defines the semantic measurement with user recommendation of keywords within the user query PBEM approach especially focus on the remarkable performance enhancements brought by machine learning. To tackle these issues, we have proposed a Precedent Behavioral Extraction Module (PBEM), which also deals with large-scale heterogeneous information to fulfill the requirements of the potential users.
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In this paper, we highlight the some of the major challenges that the system faces and how to address them.
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The recommendation system works on to personalized set of videos to users based on their past actions on the website. Hosting a compilation of billions of videos, YouTube presents one of the leading scale and most precious videos personalization recommendation system in existence. Precedent Behavioral Extraction System For Personalization Recommendation