AI-based Pattern extraction and evaluation for knowledge representation in Biomedical Data

Abstract

We live in an era of High computing and performance where large data of various patterns of diseases is produced daily, where we need to understand the behavior of certain viruses and large groups of unicellular microorganisms.  It is an absolute necessity to detect the various patterns among these viruses and bacteria by using text mining techniques and applying Artificial Intelligence (AI) Machine learning (ML) mechanisms.

Pattern extraction and evolution for knowledge representation work with the idea of data and text mining and processing for optimizing the experience of a user by training the machine using the Applied Intelligence approach to work for the best output. The work is divided into three phases which helps to move forward in a better way. The first phase is pre-processing the data, transforming it labeling it, and storing it in the database. The second phase is pattern extraction in which the use of either the imperial or knowledge-based algorithm is made. The third and final phase is the phase in which after feature extraction and inducing the knowledge evolution and evidence-based reasoning, indexing we finally the data. This data gets updated to the knowledge base again.  The idea of proceeding with this process starts with taking the data that has been provided and applying various steps such as pre-processing input data and identifying the patterns in it by using and optimizing the algorithm.