Abstract With the increasing of data on the internet, data analysis has become ines capable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understand ing. Papers indexed in Web of Science and Scopus are in high demand. Nat ural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.

Abstract This paper reviews the essential biometrics and develops a way to combine them with the Computer and User Information, giving us an Electronic Bio metrics ID. This way, distributed databases contain imperative data from much helpful information that supports more security. We reviewed examples of what these databases would look like, which any responsible party could design to be global. As will be mentioned later, we obtain common international databases whose data are modified according to factors such as the owner of the device, the location of the device, and so on. This is very useful for tracking, and it combines biometrics with data set to give us a comprehensive electronic identification.